Jin, Dawei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 83-93). / Abstracts in English and Chinese. / ABSTRACT (IN CHINESE) --- p.ii / ABSTRACT (IN ENGLISH) --- p.iv / ACKNOWLEDGEMENTS --- p.vii / CONTENTS --- p.ix / LIST OF TABLES --- p.vxiii / LIST OF FIGURES --- p.xiv / LIST OF ABBREVIATIONS --- p.xvi / Chapter CHAPTER I --- INTRODUCTION --- p.1 / Chapter 1.1 --- Biology of C. albicans --- p.2 / Chapter 1.1.1 --- Taxonomy --- p.2 / Chapter 1.1.2 --- Basic cell biology --- p.2 / Chapter 1.1.2.1 --- Cell cycle and phenotypic switch --- p.2 / Chapter 1.1.2.2 --- Cell wall --- p.3 / Chapter 1.1.3 --- "Morphological, culture and biochemical characteristics" --- p.4 / Chapter 1.1.4 --- Genomics --- p.5 / Chapter 1.1.5 --- Pathogenecity --- p.6 / Chapter 1.2 --- Catheter-related bloodstream infections (CRBSI) caused by C. albicans --- p.7 / Chapter 1.2.1 --- Intravenous catheter type --- p.7 / Chapter 1.2.2 --- Epidemiology of CRBSI caused by C. albicans --- p.8 / Chapter 1.2.3 --- Pathogenesis of intravascular catheter-related infections --- p.9 / Chapter 1.2.4 --- Diagnosis of catheter-related infections --- p.10 / Chapter 1.2.5 --- Prevention and control --- p.11 / Chapter 1.3 --- Mechanism of C. albicans adhesion to catheters --- p.12 / Chapter 1.3.1 --- The definition of microbial adhesion --- p.12 / Chapter 1.3.2 --- Relationship between microbial adhesion and biofilm formation --- p.12 / Chapter 1.4 --- Agglutinin-like sequence (ALS) gene family of C. albicans --- p.14 / Chapter 1.4.1 --- Members of ALS gene family --- p.14 / Chapter 1.4.2 --- Chromosomal location of ALS genes --- p.14 / Chapter 1.4.3 --- ALS gene organization --- p.14 / Chapter 1.4.3.1 --- Three-domain structure of ALS genes --- p.15 / Chapter 1.4.3.2 --- Characterization of ALS genes. --- p.15 / Chapter 1.4.4 --- ALS gene allelic variation --- p.17 / Chapter 1.5 --- Experimental models for catheter adhesion study of C. albicans --- p.17 / Chapter 1.5.1 --- "Static adhesion model for C, albicans" --- p.18 / Chapter 1.5.1.1 --- Advantage of static adhesion model --- p.19 / Chapter 1.5.1.2 --- Limitation of static adhesion model --- p.19 / Chapter 1.5.2 --- Dynamic adhesion model for C. albicans --- p.19 / Chapter 1.5.2.1 --- Advantage of dynamic adhesion model --- p.20 / Chapter 1.5.2.2 --- Limitation of dynamic adhesion model --- p.20 / Chapter 1.5.3 --- Quantification methods of adherent cells --- p.21 / Chapter 1.5.4 --- ALS gene expression study in the in vitro model --- p.22 / Chapter 1.6 --- Aim of study --- p.22 / Chapter CHAPTER II --- MATERIALS & METHODS --- p.24 / Chapter 2.1 --- Strains used in this study --- p.25 / Chapter 2.2 --- Design of an in vitro dynamic adhesion model for C. albicans --- p.26 / Chapter 2.2.1 --- Flask --- p.26 / Chapter 2.2.2 --- Peristaltic pump --- p.26 / Chapter 2.2.3 --- Glass tube and vascular catheters. --- p.27 / Chapter 2.2.4 --- Sterility check of in vitro dynamic adhesion model --- p.27 / Chapter 2.3 --- Construction of C. albicans growth curve --- p.27 / Chapter 2.4 --- Measurement of C. albicans adhesion to catheters --- p.29 / Chapter 2.5 --- Detection of C. albicans ALS genes --- p.30 / Chapter 2.5.1 --- DNA extraction of C. albicans --- p.30 / Chapter 2.5.2 --- ALS primers design --- p.31 / Chapter 2.5.3 --- PCR reaction --- p.32 / Chapter 2.5.4 --- Gel electrophoresis --- p.32 / Chapter 2.5.5 --- Purification of PCR products --- p.33 / Chapter 2.6 --- Construction of E. coli plasmid containing gene --- p.34 / Chapter 2.6.1 --- Ligation using the pGEM®-T Easy Vector --- p.34 / Chapter 2.6.2 --- Preparation of E. coli DH5a electro-competent cells --- p.35 / Chapter 2.6.3 --- Clean up of DNA ligation reaction for electro-transformation --- p.36 / Chapter 2.6.4 --- Electro-transformation of E. coli DH5a electro-competent cells --- p.37 / Chapter 2.6.5 --- Blue / white screening for positive transformation of E. coli DH5a. --- p.37 / Chapter 2.6.6 --- Extraction of plasmid containing ALS1 gene --- p.39 / Chapter 2.6.7 --- Plasmid validation by PCR and gel electrophoresis --- p.39 / Chapter 2.6.8 --- Serial dilution of plasmid solutions for ALS1 standard curve construction --- p.40 / Chapter 2.7 --- C. albicans ALS1 gene expression in dynamic adhesion model --- p.41 / Chapter 2.7.1 --- Design of real-time PCR primers specific for C. albicans ALS1 --- p.41 / Chapter 2.7.2 --- Validation of primers specificity --- p.42 / Chapter 2.7.3 --- RNA extraction of C. albicans cells adhered on catheters --- p.43 / Chapter 2.7.4 --- Complementary DNA (cDNA) synthesis --- p.45 / Chapter 2.7.5 --- Quantitative real-time RT-PCR --- p.46 / Chapter 2.8 --- Statistical analyses --- p.48 / Chapter CHAPTER III --- RESULTS --- p.49 / Chapter 3.1. --- Validation of the in vitro dynamic adhesion model for C. albicans --- p.50 / Chapter 3.2. --- C. albicans growth curve construction --- p.50 / Chapter 3.3. --- Measurement of C. albicans adhesion on catheters --- p.50 / Chapter 3.4. --- Detection of C. albicans SC5314 ALS genes --- p.52 / Chapter 3.5. --- Validation of E. coli plasmid containing ALS1 gene --- p.54 / Chapter 3.6. --- C. albicans ALS 1 gene expression in dynamic adhesion model --- p.54 / Chapter 3.6.1. --- Specificity validation of ALS1 real-time primers --- p.55 / Chapter 3.6.2. --- Quantitative real-time RT-PCR --- p.55 / Chapter CHAPTER IV --- DISCUSSION --- p.57 / Chapter 4.1 --- Experimental design of the in vitro dynamic adhesion model --- p.58 / Chapter 4.1.1 --- Advantages of this in vitro dynamic adhesion model --- p.58 / Chapter 4.1.2 --- Limitation of this in vitro dynamic adhesion model --- p.58 / Chapter 4.1.3 --- Catheter arrangement inside the glass tube --- p.60 / Chapter 4.1.4 --- Reproducibility of experiments in the model --- p.62 / Chapter 4.1.5 --- Identification of potential contamination in the model --- p.63 / Chapter 4.1.6 --- Advantages of removing method for C. albicans adherent cells --- p.64 / Chapter 4.1.7 --- Limitation of removing method for C. albicans adherent cells --- p.64 / Chapter 4.1.8 --- Limitation of statistical analysis --- p.66 / Chapter 4.1.9 --- Primers design --- p.67 / Chapter 4.1.9.1 --- Primers of C. albicans ALS gene detection --- p.67 / Chapter 4.1.9.2 --- Validation of ALS 1 real-time primers specificity --- p.69 / Chapter 4.2 --- C. albicans adhesion to catheters --- p.70 / Chapter 4.2.1 --- Theoretical explanation of C. albicans adhesion to different catheters --- p.71 / Chapter 4.3 --- C. albicans ALS gene expression --- p.74 / Chapter 4.3.1 --- Functions of Als proteins --- p.75 / Chapter 4.3.1.1 --- Adhesive functions --- p.75 / Chapter 4.3.1.2 --- Other functions in C. albicans pathogenesis --- p.75 / Chapter 4.3.2 --- Analysis of ALS1 gene expression pattern in the in vitro model --- p.76 / Chapter 4.4 --- Clinical application of our study --- p.78 / Chapter 4.5 --- Future study --- p.80 / Chapter 4.6 --- Conclusion --- p.81 / REFERENCES --- p.83

Wong, Ling Yee. / Thesis submitted in: August 2007. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 110-127). / Abstracts in English and Chinese. / Thesis Abstract --- p.i / 論文摘要 --- p.iv / Acknowledgements --- p.vi / Abbreviations --- p.vii / Thesis Content --- p.xii / List of Figures --- p.xv / List of Tables --- p.xvii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Literature Review --- p.3 / Chapter 2.1. --- Multiple Myeloma (MM) --- p.3 / Chapter 2.1.1 --- Epidemiology --- p.4 / Chapter 2.1.2 --- Cause and Risk Factors --- p.5 / Chapter 2.1.3 --- Pathophysiology --- p.5 / Chapter 2.1.4 --- Diagnosis and Clinical Presentation --- p.6 / Chapter 2.1.5 --- Classification of Plasma Cell Disorders --- p.6 / Chapter 2.1.5.1 --- Monoclonal Gammopathy of Undetermined Significance (MGUS) --- p.6 / Chapter 2.1.5.2 --- Asymptomatic (Smouldering) MM --- p.7 / Chapter 2.1.5.3 --- Indolent MM --- p.7 / Chapter 2.1.5.4 --- Symptomatic MM --- p.8 / Chapter 2.1.6 --- Staging --- p.9 / Chapter 2.1.7 --- Treatment --- p.11 / Chapter 2.1.8 --- Molecular Abnormality --- p.12 / Chapter 2.2 --- DNA Microarray Analysis in MM --- p.13 / Chapter 2.2.1 --- MM Pathogenesis --- p.15 / Chapter 2.2.2 --- Molecular Classification of MM --- p.18 / Chapter 2.2.3 --- Anti-MM Drug Studies --- p.22 / Chapter 2.3 --- Cancer Treatment Response Prediction --- p.24 / Chapter 2.3.1 --- MP Treatment --- p.24 / Chapter 2.3.1.1 --- Melphalan --- p.25 / Chapter 2.3.1.2 --- Prednisone --- p.27 / Chapter 2.3.1.3 --- MP Treatment Response Prediction in MM --- p.29 / Chapter 2.3.2 --- Cancer Prognosis using DNA Microarray --- p.31 / Chapter Chapter 3 --- Materials and Methods --- p.36 / Chapter 3.1. --- Patient Specimens for Gene Expression Profiling and Quantitative Real-time PCR --- p.36 / Chapter 3.2. --- Magnetic Cell Sorting of CD138-positive Plasma Cells --- p.37 / Chapter 3.2.1 --- Density Gradient Centrifugation --- p.37 / Chapter 3.2.2 --- Positive Selection of CD138-positive Cells --- p.37 / Chapter 3.3 --- Generation of Gene Expression Profiles --- p.39 / Chapter 3.3.1 --- RNA Extraction --- p.39 / Chapter 3.3.2 --- RNA Assessment --- p.40 / Chapter 3.3.3 --- Synthesis and Purification of Double-strand cDNA --- p.40 / Chapter 3.3.4 --- In vitro Transcription (IVT) and Recovery of Biotin-labeled cRNA --- p.41 / Chapter 3.3.5 --- cRNA Fragmentation and Hybridization Reaction Mixture Preparation --- p.41 / Chapter 3.3.6 --- Hybridization --- p.42 / Chapter 3.3.7 --- Post-hybridization Wash --- p.42 / Chapter 3.3.8 --- Detection with Streptavidin-dye Conjugate --- p.43 / Chapter 3.3.9 --- Bioarray Scanning and Spot Signal Quantitation --- p.43 / Chapter 3.4 --- Microarray Data Analysis --- p.45 / Chapter 3.4.1 --- Normalization and Filtering --- p.45 / Chapter 3.4.2 --- Unsupervised Clustering Analysis --- p.45 / Chapter 3.4.3 --- Supervised Class Comparison Analysis --- p.46 / Chapter 3.5 --- Microarray Verification and Candidate Gene Validation --- p.47 / Chapter 3.5.1 --- RNA Extraction --- p.47 / Chapter 3.5.2 --- Reverse Transcription PCR --- p.47 / Chapter 3.5.3 --- Quantitative Real-time PCR --- p.48 / Chapter 3.6 --- Predictive Value Calculation --- p.49 / Chapter 3.7 --- Experimental Flow --- p.49 / Chapter Chapter 4 --- Results --- p.53 / Chapter 4.1 --- Gene Expression Profiling of Chinese MM --- p.53 / Chapter 4.1.1 --- Unsupervised Clustering Analysis --- p.53 / Chapter 4.1.1.1 --- Hierarchical Clustering --- p.53 / Chapter 4.1.1.2 --- Principal Component Analysis (PCA) --- p.54 / Chapter 4.1.2 --- Identification of Statistically Differentially Expressed Genes --- p.58 / Chapter 4.1.2.1 --- Two-Sample t-statistics --- p.58 / Chapter 4.1.2.2 --- Significance Analysis of Microarrays (SAM) --- p.58 / Chapter 4.1.2.3 --- Microarray Verification --- p.66 / Chapter 4.2 --- Development of MP Treatment Response Biomarker in MM --- p.70 / Chapter 4.2.1 --- Unsupervised Clustering Analysis --- p.70 / Chapter 4.2.1.1 --- Hierarchical Clustering --- p.70 / Chapter 4.2.1.2 --- PCA --- p.70 / Chapter 4.2.2 --- Identification of Statistically Differentially Expressed Genes --- p.74 / Chapter 4.2.2.1 --- Two sample t-statistics --- p.74 / Chapter 4.2.2.2 --- SAM --- p.74 / Chapter 4.2.3 --- Verification of Candidate Gene CYB5D1 --- p.76 / Chapter Chapter 5 --- Discussion --- p.79 / Chapter 5.1 --- Global Gene Expression Profiling: DNA Microarray --- p.79 / Chapter 5.2 --- Microarray Data Normalization and Gene Filtering --- p.81 / Chapter 5.3 --- Microarray Data Analysis --- p.83 / Chapter 5.3.1 --- Unsupervised Clustering Analysis --- p.83 / Chapter 5.3.1.1 --- Hierarchical Clustering --- p.83 / Chapter 5.3.1.2 --- PCA --- p.85 / Chapter 5.3.2 --- Identification of Statistically Differentially Expressed Genes --- p.86 / Chapter 5.4 --- Verification of Candidate Genes by Quantitative Real-time PCR --- p.89 / Chapter 5.5 --- Gene Expression Profiling of Chinese MM --- p.90 / Chapter 5.5.1 --- Comparison of Gene Expression Patterns of MM and Normal Plasma Cells --- p.90 / Chapter 5.5.2 --- Differentially Expressed Genes between MM and Normal Plasma Cells..… --- p.91 / Chapter 5.5.2.1 --- Common Differentially Expressed Genes with Previous Studies --- p.94 / Chapter 5.5.2.2 --- Potential Tumor Suppressor Genes in Differentially Expressed Genes..… --- p.96 / Chapter 5.5.2.3 --- Verified Differentially Expressed Genes --- p.98 / Chapter 5.5.3 --- Future Studies --- p.101 / Chapter 5.6 --- Development of MP Treatment Response Biomarker in MM --- p.103 / Chapter 5.6.1 --- Comparison of Gene Expression Patterns of MP Good Responders (GR) and Poor Responders (PR) --- p.103 / Chapter 5.6.2 --- Differentially Expressed Gene between MP GR and PR: CYB5D1 --- p.104 / Chapter 5.6.3 --- Possible Role of CYB5D1 in MP Resistance in MM Cells --- p.104 / Chapter 5.6.4 --- Potential Clinical Application of CYB5D1 in MP Treatment Response Prediction in MM --- p.106 / Chapter 5.6.5 --- Future Studies --- p.106 / Chapter Chapter 6 --- Conclusion --- p.108 / Chapter 6.1 --- Gene Expression Profiling of Chinese MM --- p.108 / Chapter 6.2 --- Development of MP Treatment Response Biomarker in MM --- p.108 / References --- p.110 / Appendix --- p.128

We describe a new direct method for estimating structure and motion from image intensities of multiple views. We extend the direct methods of Horn- and-Weldon to three views. Adding the third view enables us to solve for motion, and compute a dense depth map of the scene, directly from image spatio -temporal derivatives in a linear manner without first having to find point correspondences or compute optical flow. We describe the advantages and limitations of this method which are then verified through simulation and experiments with real images.

ABSTRACT COMPARATIVE SEQUENCE ANALYSIS OF THE INTERNAL TRANSCRIBED SPACER 2 REGION OF TURKISH RED PINE (Pinus brutia TEN.) AND NATURAL ALEPPO PINE (Pinus halepensis MILL.) POPULATIONS FROM TURKEY Tozkar, &Ouml / zge M.S., Department of Biology Supervisor: Prof. Dr. Zeki Kaya April, 2007, 107 pages Turkish red pine (Pinus brutia) is wide-spread and an important forest tree species in Turkey, occurring mainly in southern, western and north-western Turkey and as small isolated populations in the Black Sea region. Aleppo pine (Pinus halepensis) has naturally found only in Adana and Mugla provinces as small population in mixture with Turkish red pine. Although Turkish red pine and Aleppo pine are morphologically different, Turkish red pine has been regarded as subspecies of Aleppo pine by some taxonomists due to occurrence of natural hybridization between these two species. However, the phylogenic relationship between these species needs to be explored further. In the present study, by sampling overlapped populations of both species from Mugla and Adana provinces (4 populations of Turkish red pine and 3 populations of Aleppo pine), internal transcribed spacer (ITS) region of ribosomal DNA were comparatively studied with sequence analysis. Although ITS1, 5.8s and ITS2 regions of ribosomal DNA were studied with ITS primers, only ITS2 region was successfully amplified with polymerase chain reaction (PCR). The complete data set for this region was analysed using MEGA3.1 and Arlequin softwares. Analysis of molecular variance (AMOVA) demonstrated the highest genetic differentiation between Turkish red pine and Aleppo pine in Mugla with 100 percentage of variation. AMOVA analysis also indicated the possibility of low-level migration of genes between Turkish red pine and Aleppo pine populations in Adana with 50.65 percent of molecular variance. Haplotype comparison revealed that two major haplotypes were represented Based on the results of ITS2 region sequence analysis, Turkish populations of Aleppo pine and Turkish red pine populations could not be fully differentiated. In Mugla province Turkish red pine and Aleppo pine revealed more differentiation due to reproductive isolation. But in Adana province, two species shared more common genetic background due to possible hybridization. Since ITS2 region of nuclear ribosomal DNA revealed a few variable and parsimony informative sites for both species, thus, only ITS2 region of ribosomal DNA does not appear to be sufficient for fully resolving genetic relationships between Turkish red pine and Aleppo pine populations. Further studies including ITS1 and 5.8s regions of ribosomal DNA and populations included from major Aleppo pine distribution areas will be useful to understand the evolutionary relationship between Aleppo pine and Turkish red pine populations in Turkey.

Distributed iterative algorithms are of great importance, as they are known to provide low-complexity and approximate solutions to what are otherwise high-dimensional intractable optimization problems. The theory of message-passing based algorithms is fairly well developed in the coding, machine learning and statistical physics literatures. Even though several applications of message-passing algorithms have already been identified, this work aims at establishing that a plethora of other applications exist where it can be of great importance. In particular, the goal of this work is to develop and demonstrate applications of this class of algorithms in network communications and computational biology. In the domain of communications, message-passing based algorithms provide distributed ways of inferring the optimal solution without the aid of a central agent for various optimization problems that happen in the resource allocation of communication networks. Our main framework is Affinity Propagation (AP), originally developed for clustering problems. We reinterpret this framework to unify the development of distributed algorithms for discrete resource allocation problems. Also, we consider a network-coded communication network, where continuous rate allocation is studied. We formulate an optimization problem with a linear cost function, and then utilize a Belief Propagation (BP) approach to determine a decentralized rate allocation strategy. Next, we move to the domain of computational biology, where graphical representations and computational biology play a major role. First, we consider the motif finding problem with several DNA sequences. In effect, this is a sequence matching problem, which can be modeled using various graphical representations and also solved using low-complexity algorithms based on message-passing techniques. In addition, we address the application of message-passing algorithms for a DNA sequencing problem where the one dimensional structure of a single DNA sequence is identified. We reinterpret the problem as being equivalent to the decoding of a nonlinear code. Based on the iterative decoding framework, we develop an appropriate graphical model which enables us to derive a message-passing algorithm to improve the performance of the DNA sequencing problem. Although this work consists of disparate application domains of communications, networks and computational biology, graphical models and distributed message-passing algorithms form a common underlying theme. / text

Energy use is a part of everyday life and the use of energy is a part of the global climate change. Policy makers urge individuals to change their daily behaviour in order to mitigate climate change and care for our common environment. The dissertation regards daily behaviour as activities performed by individuals. The theoretical base is the time-geographic approach wherein everyday life is regarded as a sequence of interlinked activities performed by indivisible individuals. The dissertation investigates individuals’ energy use as an outcome of the activities they perform in everyday life. The empirical base of the dissertation is time-diaries from the Swedish time use survey 2010/2011. The diary data is explored as sequences of daily activities by using sequence analysis and clustering. The results show that individuals’ energy use is closely interweaved with how they live their everyday lives in terms of activity sequences. The results imply that changing an activity affects both the intricate web of interaction in the household and the interdependence of activities in everyday life. Change does not only affect the singular activity that was the object for the change, but rather major parts of the sequence of activities. In order to address energy conservation in information campaigns considerations ought to be taken on how everyday life is shaped and formed by the individual, by negotiations between the individuals in households, and societal structures. Information can be targeted to groups of individuals  with similar activity sequences as they are revealed by cluster analysis. / Energianvändningen är en del av vardagen likaväl som användningen av energi är en del av den globala klimatförändringen. För att mildra effekterna på vår gemensamma miljö uppmanas människor av politiker och andra beslutsfattare att förändra sitt vardagsbeteende. I avhandlingen betraktas vardagsbeteendet som människors dagliga aktiviteter. Avhandlingens teoretiska grund är den tidsgeografiska ansatsen, där människors vardag betraktas som en sekvens av de aktiviteter som utförs av odelbara individer. Människors dagliga sekvens av aktiviteter undersöks för att ta reda på vilken energianvändning som genomförandet av aktiviteterna ger upphov till. Den empiriska grunden för avhandlingen är tidsdagboksdata från den svenska tidsanvändningsstudien från 2010/2011 och avhandlingen utforskar tidsdagböckerna som sekvenser av aktiviteter med hjälp av sekvens- och klusteranalys. Resultaten visar att individers energianvändning är nära sammanvävd med de aktivitetssekvenser som visar hur vardagslivet levs. Resultaten pekar vidare på att förändringar av enskilda aktiviteter också påverkar andra aktiviteter i det dagliga livet. Förändringar av en aktivitet påverkar således hela den dagliga sekvensen av aktiviteter. I utformningen av information som syftar till att minska hushållens energianvändning bör hänsyn tas till hur vardagslivets aktivitetssekvens formas av den enskilde i samspelet både med andra individer i hushållet och med samhällsstrukturerna. Målgruppsinriktad information kan utformas med utgångspunkt from människors likartade aktivitetsmönster så som de framgår genom klusteranalys.

Huge amounts of digital information are stored in the World today and the amount is increasing by quintillion bytes every day. Approximate data mining algorithms are very important to efficiently deal with such amounts of data due to the computation speed required by various real-world applications, whereas exact data mining methods tend to be slow and are best employed where the precise results are of the highest important. This thesis focuses on several data mining tasks related to analysis of big data: frequent pattern mining and visual representation. For mining frequent patterns in big data, three novel approximate methods are proposed and evaluated on real and artificial databases: • Random Sampling Method (RSM) creates a random sample from the original database and makes assumptions on the frequent and rare sequences based on the analysis results of the random sample. A significant benefit is a theoretical estimate of classification errors made by this method using standard statistical methods. • Multiple Re-sampling Method (MRM) is an improved version of RSM method with a re-sampling strategy that decreases the probability to incorrectly classify the sequences as frequent or rare. • Markov Property Based Method (MPBM) relies upon the Markov property. MPBM requires reading the original database several times (the number equals to the order of the Markov process) and then calculates the empirical frequencies using the Markov property. For visual representation... [to full text] / Didžiuliai informacijos kiekiai yra sukaupiami kiekvieną dieną pasaulyje bei jie sparčiai auga. Apytiksliai duomenų tyrybos algoritmai yra labai svarbūs analizuojant tokius didelius duomenų kiekius, nes algoritmų greitis yra ypač svarbus daugelyje sričių, tuo tarpu tikslieji metodai paprastai yra lėti bei naudojami tik uždaviniuose, kuriuose reikalingas tikslus atsakymas. Ši disertacija analizuoja kelias duomenų tyrybos sritis: dažnų sekų paiešką bei vizualizaciją sprendimų priėmimui. Dažnų sekų paieškai buvo pasiūlyti trys nauji apytiksliai metodai, kurie buvo testuojami naudojant tikras bei dirbtinai sugeneruotas duomenų bazes: • Atsitiktinės imties metodas (Random Sampling Method - RSM) formuoja pradinės duomenų bazės atsitiktinę imtį ir nustato dažnas sekas, remiantis atsitiktinės imties analizės rezultatais. Šio metodo privalumas yra teorinis paklaidų tikimybių įvertinimas, naudojantis standartiniais statistiniais metodais. • Daugybinio perskaičiavimo metodas (Multiple Re-sampling Method - MRM) yra RSM metodo patobulinimas, kuris formuoja kelias pradinės duomenų bazės atsitiktines imtis ir taip sumažina paklaidų tikimybes. • Markovo savybe besiremiantis metodas (Markov Property Based Method - MPBM) kelis kartus skaito pradinę duomenų bazę, priklausomai nuo Markovo proceso eilės, bei apskaičiuoja empirinius dažnius remdamasis Markovo savybe. Didelio duomenų kiekio vizualizavimui buvo naudojami pirkėjų internetu elgsenos duomenys, kurie analizuojami naudojant... [toliau žr. visą tekstą]

The ability of Candida albicans to change morphology plays several roles in its virulence and as a human commensal. The yeast-to-hyphae transition is tightly regulated by several sets of activating and repressing pathways. The DNA-binding proteins Rfg1p, Nrg1p and the global repressor Tup1p are part of the repressors found to regulate this morphogenesis. Knowledge of these repressors is based on extrapolations from homology to S. cerevisiae and from expression studies of mutants in inducing conditions, all of which are indirect means of determining a protein's function. We proposed a genome-wide location study of the Nrg1 and Rfg1 transcription factors to obtain direct data to identify their in vivo targets. Our results suggest different avenues for Nrg1p function and a regulation behaviour diverging from the previously suggested model: Nrg1p acts not only as a repressor but also as a transcription activator. Furthermore it regulates its target genes through binding in their coding regions instead binding to the expected regulatory elements on promoters.

With recent and continuing advances in bioinformatics, the volume of sequence data has increased tremendously. Along with this increase, there is a growing need to develop efficient algorithms to process such data in order to make useful and important discoveries. Careful analysis of genomic data will benefit science and society in numerous ways, including the understanding of protein sequence functions, early detection of diseases, and finding evolutionary relationships that exist among various organisms. Most sequence analysis problems arising from computational genomics and evolutionary biology fall into the class of NP-complete problems. Advances in exact and approximate algorithms to address these problems are critical. In this thesis, we investigate a novel graph theoretical model that deals with fundamental evolutionary problems. The model allows incorporation of the evolutionary operations ``insertion', ``deletion', and ``substitution', and various parameters such as relative distances and weights. By varying appropriate parameters and weights within the model, several important combinatorial problems can be represented, including the weighted supersequence, weighted superstring, and weighted longest common sequence problems. Consequently, our model provides a general computational framework for solving a wide variety of important and difficult biological sequencing problems, including the multiple sequence alignment problem, and the problem of finding an evolutionary ancestor of multiple sequences. In this thesis, we develop large scale combinatorial optimization techniques to solve our graph theoretical model. In particular, we formulate the problem as two distinct but related models: constrained network flow problem and weighted node packing problem. The integer programming models are solved in a branch and bound setting using simultaneous column and row generation. The methodology developed will also be useful to solve large scale integer programming problems arising in other areas such as transportation and logistics.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 121-126).