Mathematical models of signal transduction pathways are characterized by a large number of proteins and uncertain parameters, yet only a limited amount of quantitative data is available. The dissertation addresses this problem using two different approaches: the first approach deals with a model simplification procedure for signaling pathways that reduces the model size but retains the physical interpretation of the remaining states, while the second approach deals with creating rich data sets by computing transcription factor profiles from fluorescent images of green-fluorescent-protein (GFP) reporter cells. For the first approach a model simplification procedure for signaling pathway models is presented. The technique makes use of sensitivity and observability analysis to select the retained proteins for the simplified model. The presented technique is applied to an IL-6 signaling pathway model. It is found that the model size can be significantly reduced and the simplified model is able to adequately predict the dynamics of key proteins of the signaling pathway. An approach for quantitatively determining transcription factor profiles from GFP reporter data is developed as the second major contribution of this work. The procedure analyzes fluorescent images to determine fluorescence intensity profiles using principal component analysis and K-means clustering, and then computes the transcription factor concentration from the fluorescence intensity profiles by solving an inverse problem involving a model describing transcription, translation, and activation of green fluorescent proteins. Activation profiles of the transcription factors NF-κB, nuclear STAT3, and C/EBPβ are obtained using the presented approach. The data for NF-κB is used to develop a model for TNF-α signal transduction while the data for nuclear STAT3 and C/EBPβ is used to verify the simplified IL-6 model. Finally, an approach is developed to compute the distribution of transcription factor profiles among a population of cells. This approach consists of an algorithm for identifying individual fluorescent cells from fluorescent images, and an algorithm to compute the distribution of transcription factor profiles from the fluorescence intensity distribution by solving an inverse problem. The technique is applied to experimental data to derive the distribution of NF-κB concentrations from fluorescent images of a NF-κB GFP reporter system.

In this thesis, improving the performance of the automatic recognition of the Doppler radar targets is studied. The radar used in this study is a ground-surveillance doppler radar. Target types are car, truck, bus, tank, helicopter, moving man and running man. The input of this thesis is the output of the real doppler radar signals which are normalized and preprocessed (TRP vectors: Target Recognition Pattern vectors) in the doctorate thesis by Erdogan (2002). TRP vectors are normalized and homogenized doppler radar target signals with respect to target speed, target aspect angle and target range. Some target classes have repetitions in time in their TRPs. By the use of these repetitions, improvement of the target type classification performance is studied. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms are used for doppler radar target classification and the results are evaluated. Before classification PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), NMF (Nonnegative Matrix Factorization) and ICA (Independent Component Analysis) are implemented and applied to normalized doppler radar signals for feature extraction and dimension reduction in an efficient way. These techniques transform the input vectors, which are the normalized doppler radar signals, to another space. The effects of the implementation of these feature extraction algoritms and the use of the repetitions in doppler radar target signals on the doppler radar target classification performance are studied.

Complex patterns arise in many extended nonlinear nonequilibrium systems in physics, chemistry and biology. Information extraction from these complex patterns is a challenge and has been a main subject of research for many years. We study patterns in Rayleigh-Benard convection (RBC) acquired from our laboratory experiments to develop new characterization techniques for complex spatio-temporal patterns. Computational homology, a new topological characterization technique, is applied to the experimental data to investigate dynamics by quantifying convective patterns in a unique way. The homology analysis is used to detect symmetry breakings between hot and cold flows as a function of thermal driving in experiments, where other conventional techniques, e.g., curvature and wave-number distribution, failed to reveal this asymmetry. Furthermore, quantitative information is acquired from the outputs of homology to identify different spatio-temporal states. We use this information to obtain a reduced dynamical description of spatio-temporal chaos to investigate extensivity and physical boundary effects in RBC. The results from homological analysis are also compared to other dimensionality reduction techniques such as Karhunen-Loeve decomposition and Fourier analysis.

癌症至今已連續蟬聯並高居國人十大死因之首，由於癌症初期病患接受適時治療的存活率較高，因此若能「早期發現，早期診斷，早期治療」則可降低死亡率。本文所引用的資料庫，是經由「表面強化雷射解吸電離飛行質譜技術」（SELDI-TOF-MS）所擷取建置的蛋白質質譜儀資料，包括兩筆高維度資料：一筆為攝護腺癌症，另一筆則為頭頸癌症。然而蛋白質質譜儀資料常因維度變數繁雜眾多，對於資料的存取容量及運算時間而言，往往造成相當沉重的負擔與不便；有鑑於此，本文之目的即在探討將高維度資料經由維度縮減後，找出分錯率最小化之分析方法，希冀提高癌症病例資料分類的準確性。 本研究分為實驗組及對照組兩部分，實驗組是以主成份分析（Principal Component Analysis，PCA）進行維度縮減，再利用支持向量機（Support Vector Machine，SVM）予以分類，最後藉由重疊法（Overlap）以期改善分類效果；對照組則是以支持向量機直接進行分類。分析結果顯示，重疊法對於攝護腺癌症具有顯著的改善效果，但對於頭頸癌症的改善效果卻不明顯。此外，本研究也探討關於蛋白質質譜儀資料之質量範圍，藉以確認專家學者所建議的質量範圍是否與分析結果相互一致。在攝護腺癌症中的原始資料，專家學者所建議的質量範圍以外，似乎仍隱藏著重要的相關資訊；在頭頸癌症中的原始資料，專家學者所建議的質量範圍以外，對於研究分析而言則並沒有實質上的幫助。 / Cancer has been the number one leading cause of death in Taiwan for the past 24 years. Early detection of this disease would significantly reduce the mortality rate. The database adopted in this study is from the Protein Mass Spectrometry Data Sets acquired and established by “Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry” (SELDI-TOF-MS) technique, including the Prostate Cancer and Head/Neck Cancer Data Sets. However, because of its high dimensionality, dealing the analysis of the raw data is not easy. Therefore, the purpose of this thesis is to search a feasible method, putting the dimension reduction and minimizing classification errors in the same time. The data sets are separated into the experimental and controlled groups. The first step of the experimental group is to use dimension reduction by Principal Component Analysis (PCA), following by Support Vector Machine (SVM) for classification, and finally Overlap Method is used to reduce classification errors. For comparison, the controlled group uses SVM for classification. The empirical results indicate that the improvement of Overlap Method is significant in the Prostate Cancer case, but not in that of the Head/Neck case. We also study data range suggested according to the expert opinions. We find that there is information hidden outside the data range suggested by the experts in the Prostate Cancer case, but not in the Head/Neck case.

In the last decade, there has been a growing interest in the use of differential sensing for molecular recognition. Inspired by the mammalian olfactory system, differential sensing employs an array of non-selective receptors, which through cross-reactive interactions, create a distinct pattern for each analyte tested. The unique fingerprints obtained for each analyte with differential sensing are studied with statistical analysis techniques, such as principal component analysis and linear discriminant analysis. It was postulated that serum albumin proteins would be applicable to differential sensing schemes due to significant differences in sequence identity between different serum albumin species, and due to the wide range of hydrophobic molecules which are known to bind to these proteins. Consequently, cross-reactive serum albumin arrays were developed, utilizing hydrophobic fluorescent indicators to detect hydrophobic molecules. As such, serum albumin cross-reactive arrays were employed to discriminate subtly different hydrophobic analytes, and mixtures of these analytes, in the form of terpenes and perfumes, plasticizers and plastic explosive mixtures, and glycerides and adipocyte extracts. In this doctoral work, a detailed review of the field of differential sensing, and a thorough study of principal component analysis and linear discriminant analysis in various differential sensing scenarios, are given. These introductory chapters aid in better understanding the methods and techniques applied in later experimental chapters. In chapter 3, serum albumins, a PRODAN indicator, and an additive are shown to discriminate five terpene analytes and terpene doped perfumes. Chapter 4 describes an array with serum albumins, two dansyl fluorophores, and an additive which successfully differentiate the plasticizers found within the plastic explosives C4 and Semtex and simulated C4 and Semtex mixtures. Discrimination of these simulated mixtures was also achieved with this array in the presence of soil contaminants, demonstrating the potential real-world applicability of this sensing ensemble. Finally, chapter 5 details an array consisting of serum albumins, several fluorescent indicators, and a Grubb's olefin metathesis reaction, to differentiate saturated and unsaturated triglycerides, diglycerides, and monoglycerides. Mixtures of glycerides in adipocyte extracts taken from rats with different health states were then successfully discriminated, showing promise for clinical applications in differentiating adipoctyes from pre-diabetic, type 2 diabetic, and non-diabetic individuals. / text

顯示器在日常生活中應用廣泛，未來市場發展朝向大尺寸、畫面精緻度兩方面發展，在畫面精緻度方面，目前主要發展技術有二：OLED及量子點，由於OLED尚有產品壽命短、畫面殘影、成本較高等缺點待克服，且在製程上與現今主流LCD相差甚多，因此本研究針對另一可能發展之技術－量子點進行研究，了解產業現況、技術發展趨勢，並給予台灣相關企業建議。 本研究蒐集與專利品質相關的專利量化指標－專利家族規模、申請專利範圍項數、引用專利數、被引用次數及專利年齡，並利用主成分分析法計算專利品質方程式，以針對研究範圍內的專利進行品質排序，並且蒐集專利權人相關市場及專利活動資訊。在專利分析部分將產業區分上、中、下游三區位進行分析，每一產業區位包含重點專利、重要專利權人分析，在中游部分，另進行專利權人研發專利佈局分析，以專利活動程度與專利品質兩軸向衡量市場競爭者之地位。 研究結論首先將總結如何以專利量化指標衡量專利品質，接者以專利分析結果說明量子點顯示技術整體產業發展現況，並歸納台灣企業未來策略。近年投入研究之企業、申請專利數量漸增，但目前尚無企業在此技術領域處於領導者之地位，上游企業的研發方向多是改善LED背光源各波長強度不均的問題，可加強與量子點研發製造商的合作；中游企業需注意韓國廠商大量申請專利所帶來的效果及部分專利權人專利活動程度低，然而握有高品質專利，對其他企業可能造成威脅；下游企業則須思考如何以其他關鍵技術搭配量子點顯示器，研發符合消費者需求之產品。研究最後，針對以專利量化指標進行專利品質分析的過程進行檢討，給予未來研究建議。 / Nowadays displays have wide applications in our daily life, people are looking forward to large size displays and high image performance displays. OLEDs and Quantum dots are the most important technologies which may enhance the image performance. However, OLEDs have some key disadvantages, including the high price, the motion blur and the short lifespan. Therefore, this study focused on Quantum dots. By looking into the industry and realizing the development of Quantum dots, this study gave advice to related companies in Taiwan. To measure the value of patents, we collected five quantifiable indicators of each patent－the patent age, the size of patent family, the amount of claims, forward and backward citations. Then, we calculated the weight of each indicators by Principal Component Analysis（PCA）. As the result, the value of patents were estimated. In the chapter of patent analysis, we classified patents and patentees to the upper, middle and lower stream, each category included the analysis of important patents and patentees. In the middle stream, we also analyzed the patent portfolio, according to two axis－the patent activity and the patent quality proposed by Ernst in 1998. In sum, this study found that companies invested and researched in the industry increasingly; however, there was no company at the leader position. In the upper stream, companies were improving the intensity of different wavelengths in the backlight, cooperating with quantum dot suppliers could be a good strategy for those companies. In the middle stream, Taiwan companies should pay attention on competitors locating in the high patent activity and the low patent activity but high patent quality. In the lower stream, products disposed quantum dots should also fit in needs of customers. Finally, this study reviewed the analysis of patent quantifiable indicators and gave suggestions for the future research.

The collection and interpretation of data is a critical component of traffic and transportation engineering used to establish baseline performance measures and to forecast future conditions. One important source of traffic data is commercial motor vehicle (CMV) weight and classification data used as input to critical tasks in transportation design, operations, and planning. The evolution of Intelligent Transportation System (ITS) technologies has been providing transportation engineers and planners with an increased availability of CMV data. The primary sources of these data are automatic vehicle classification (AVC) and weigh-in-motion (WIM). Microscopic traffic simulation models have been used extensively to model the dynamic and stochastic nature of transportation systems including vehicle composition. One aspect of effective microscopic traffic simulation models that has received increased attention in recent years is the calibration of these models, which has traditionally been concerned with identifying the "best" parameter set from a range of acceptable values. Recent research has begun the process of automating the calibration process in an effort to accurately reflect the components of the transportation system being analyzed. The objective of this research is to develop a methodology in which the effects of CMVs can be included in the calibration of microscopic traffic simulation models. The research examines the ITS data available on weight and operating characteristics of CMVs and incorporates this data in the calibration of microscopic traffic simulation models. The research develops a methodology to model CMVs using microscopic traffic simulation models and then utilizes the output of these models to generate the data necessary to quantify the impacts of CMVs on infrastructure, travel time, and emissions. The research uses advanced statistical tools including principal component analysis (PCA) and recursive partitioning to identify relationships between data collection sites (i.e., WIM, AVC) such that the data collected at WIM sites can be utilized to estimate weight and length distributions at AVC sites. The research also examines methodologies to include the distribution or measures of central tendency and dispersion (i.e., mean, variance) into the calibration process. The approach is applied using the CORSIM model and calibrated utilizing an automated genetic algorithm methodology.

To be able to make informed descicions regarding the research of new drug molecules (ligands), it is crucial to have access to information regarding the chemical interaction between the drug and its biological target (protein). Computer-based methods have a given role in drug research today and, by using methods such as molecular docking, it is possible to investigate the way in which ligands and proteins interact. Despite the acceleration in computer power experienced in the last decades many problems persist in modelling these complicated interactions. The main objective of this thesis was to investigate and improve molecular modelling methods aimed to estimate protein-ligand binding. In order to do so, we have utilised chemometric tools, e.g. design of experiments (DoE) and principal component analysis (PCA), in the field of molecular modelling. More specifically, molecular docking was investigated as a tool for reproduction of ligand poses in protein 3D structures and for virtual screening. Adjustable parameters in two docking software were varied using DoE and parameter settings were identified which lead to improved results. In an additional study, we explored the nature of ligand-binding cavities in proteins since they are important factors in protein-ligand interactions, especially in the prediction of the function of newly found proteins. We developed a strategy, comprising a new set of descriptors and PCA, to map proteins based on their cavity physicochemical properties. Finally, we applied our developed strategies to design a set of glycopeptides which were used to study autoimmune arthritis. A combination of docking and statistical molecular design, synthesis and biological evaluation led to new binders for two different class II MHC proteins and recognition by a panel of T-cell hybridomas. New and interesting SAR conclusions could be drawn and the results will serve as a basis for selection of peptides to include in in vivo studies.

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