Enantioselective indicator displacement assays (eIDAs), was transitioned to a high-throughput screening protocols, for the rapid determination of concentration and enantioselectivity (ee) of chiral diols and α-hydroxycarboxylic acid. To improve the design of our previously established receptor based on o-(N,N-dialkylaminomethyl)arylboronate scaffolds for eIDAs. The rigidity of the receptor, which pertinent from the formation of an intramolecular N-B dative bond was investigated. o-(Pyrrolidinylmethyl)phenylboronic acid its complexes with bifunctional substrates such as catechol, [alpha]-hydroxyisobutyric acid, and hydrobenzoin was studied in detail by x-ray crystallography and ¹¹B NMR. Our structural study predicts that the formation of an N-B dative bond, and/or solvolysis to afford a tetrahedral boronate anion, depends on the solvent and the complexing substrate present. To simplify the operation of eIDAs, we introduced an analytical method, which utilize a dual-chamber quartz cuvette, which reduces the number of spectroscopic measurements from two to one and introduced artificial neural networks (ANNs) which simplifies data analysis. In a second example a high-throughtput screening protocol for hydrobenzoin was developed. The method involves the sequential utilization of what we define herein as screening, training, and analysis plates. Several enantioselective boronic-acid based receptors were screened using 96-well plates, both for their ability to discriminate the enantiomers of hydrobenzoin and to find their optimal pairing with indicators resulting in the largest optical responses. The best receptor/indicator combination was then used to train an ANN to determine concentration and ee. To prove the practicality of the developed protocol, analysis plates were created containing true unknown samples of hydrobenzoin generated by established Sharpless asymmetric dihydroxylation reactions, and the best ligand was correctly identified. The system was extended to pattern recognition for the rapid determination of identity, concentration, and ee of chiral vicinal diols. A diverse enantioselective sensor array was generated with three chiral boronic acid receptors and pH indicators. The optical response produced by the sensor array, was analyzed by two pattern recognition algorithms: principal component analysis (PCA) and ANNs. The PCA plot demonstrated good chemoselective and enantioselective separation of the analytes, and ANNs was used to accurately determine the concentration and ee of five unknown samples. / text

Η ομαδοποίηση ομαδοποιεί τα δεδομένα βασισμένη μόνο σε πληροφορία που βρίσκεται σε αυτά η οποία περιγράφει τα αντικείμενα και τις σχέσεις τους. Ο στόχος είναι τα αντικείμενα που βρίσκονται σε μια ομάδα να είναι όμοια(ή σχετικά) μεταξύ τους και διαφορετικά απο τα αντικείμενα των άλλων ομάδων. Όσο μεγαλύτερη είναι η ομοιότητα(ή η ομοιογένεια) σε μια ομάδα και όσο μεγαλύτερη είναι η διαφορετικότητα ανάμεσα στις ομάδες τόσο καλύτερη είναι η ομαδοποίηση. Οι μεθόδοι ομαδοποίησης μπορούν να διακριθούν σε τρείς κατηγορίες, ιεραρχικές, διαχωριστικές, και στις βασισμένες στη πυκνότητα. Οι ιεραρχικοί αλγόριθμοι μας δίνουν ιεραρχίες ομάδων σε μία top-down(συγχωνευτική) ή bottom-up(διαχωριστική) μορφή. Η εργασία αυτή επικεντρώνεται στην ιεραρχική διαχωριστική ομαδοποίηση. Ανάμεσα στους ιεραρχικούς διαχωριστικούς αλγορίθμους ξεχωρίζουμε τον αλγόριθμο Principal Direction Divisive Partitioning (PDDP). Ο PDDP χρησιμοποιεί την προβολή των δεδομένων στα κύρια συστατικά της αντίστοιχης μήτρας συνδιασποράς. Αυτό επιτρέπει την εφαρμογή σε δεδομένα υψηλής διάστασης. Στην εργασία αυτή προτείνεται μια βελτίωση του αλγορίθμου \Principal Direction Divisive Partitioning. Ο προτεινόμενος αλγόριθμος συνδυάζει στοιχεία από την εκτίμηση πυκνότητας και τις μεθόδους βασισμένες στην προβολή με έναν γρήγορο και αποδοτικό αλγόριθμο, ικανό να αντιμετωπίσει δεδομένα υψηλής διάστασης. Τα πειραματικά αποτελέσματα δείχνουν βελτιωμένη απόδοση ομαδοποίησης σε σύγκριση με άλλες δημοφιλείς μεθόδους. Επίσης ερευνάται το πρόβλημα του αυτόματου καθορισμού του πλήθους των ομάδων που είναι πολύ σημαντικό την ανάλυση ομάδων. / Cluster analysis groups data objects based only on information found in the data that describes the objects and their relationships. The goal is that the objects within a group be similar (or related) to one another and different from (or unrelated to) the objects in other groups. The greater the similarity (or homogeneity) within a group and the greater the difference between groups, the better or more distinct the clustering. Clustering methods can be broadly divided into three categories, hierarchical, partitioning and density-based (while there are other categorisations). Hierarchical algorithms provide nested hierarchies of clusters in a top-down (agglomerative), or bottom-up (divisive) fashion. This work is focused on the class of hierarchical divisive clustering algorithms. Amongst the class of divisive hierarchical algorithms, the Principal Direction Divisive Partitioning (PDDP) algorithm is of particular value. PDDP uses the projection of the data onto the principal components of the associated data covariance matrix. This allows the application to high dimensional data. In this work an improvement of the algorithm PDDP is proposed. The proposed algorithm merges concepts from density estimation and projection-based methods towards a fast and efficient clustering algorithm, capable of dealing with high dimensional data. Experimental results show improved partitioning performance compared to other popular methods. Moreover, we explore the problem of automatically determining the number of clusters that is central in cluster analysis.

This study shows that remote sensing and GIS are powerful tools in identifying geologically induced lineaments from digitally enhanced ETM+ satellite imageries and the digital elevation model (DEM) in remote areas such as the Makran accretionary prism, southeast Iran. The presence of the conjugate shear fractures in the eastern part, along with the extensional, and the presence of reidal sets associated with the subsidiary fractures of the Minab-Zendal fault system in the western part, suggests that the structural pattern changes from pure shear to simple shear from east to the west across the prism. Moreover, the gradual increase in the value of the angle between the two conjugate shear fractures, from south (coastal Makran) to north across the prism, and the presence of high-angle north-dipping reverse faults, with few south-dipping normal faults, suggest that deformation changes from brittle, in the south, to ductile in the northern part of the prism.

Die Risikoprämie einer Unternehmensanleihe dient prinzipiell der wirtschaftlichen Kompensation für die Übernahme zusätzlicher Risiken gegenüber den Risiken der Benchmark. Allerdings findet sich in der bisher veröffentlichen Literatur eine Vielzahl von den praktischen Messkonzepten, die in vielen Fällen nicht fehlerfrei und problemlos zustande gekommen sind. Daher ist die präzise und quantitative Messung der Risikoprämien von Unternehmensanleihen eine betriebswirtschaftliche Notwendigkeit. In der vorliegenden Arbeit werden im Hinblick auf die Erreichbarkeit drei alternative Messkonzepte bezüglich der Risikoprämien von Unternehmensanleihen vorgestellt und miteinander verglichen. Einige bisherige Studien sind der Auffassung, dass die Risikoprämien von Unternehmensanleihen zumeist von den Nicht-Kreditkomponenten beeinflusst werden. Um diese Marktanomalien zu erklären, verwenden die vorliegenden Untersuchungen das statistische lineare Faktor-Modell. In diesem Zusammenhang wird die Untersuchung von LITTERMAN/SCHEINKMAN (1991) auf die risikobehafteten Unternehmensanleihen übertragen. Im Kern steht die Frage, welche Risikoarten bzw. wie viele Einflussfaktoren wirken sich auf die Risikoprämien von Unternehmensanleihen in wieweit aus. Das Ziel ist ein sparsames lineares Faktor-Modell mit wirtschaftlicher Bedeutung aufzubauen. Somit leistet diese Dissertationsschrift einen wesentlichen Beitrag zur Gestaltung der Anleiheanalyse bzw. zur Portfolioverwaltung.

Disregard for patient-specific joint-level variability may be related to decreased functional ability, poor implant longevity and dissatisfaction post-TKA. The purpose of this study was to, 1) compare pre and post-implant intraoperative passive knee adduction angle kinematic patterns and characterize the effect of surgical intervention on each pattern, 2) examine the association between passive pre and post-implant knee kinematics measured intraoperatively and dynamic knee kinematics and kinetics pre and post-TKA measured during gait, and 3) compare dynamic post-TKA kinematic and kinetic patterns between patient-specific knee recipients and traditional TKA recipient. Patients received a TKA using the Stryker Precision Knee navigation system capturing pre/post-implant kinematics through a passive range of flexion. One-week prior and 1-year post-TKA patients underwent three-dimensional gait analysis. Knee joint waveforms were calculated according to the joint coordinate system. Principal component analysis (PCA) was applied to frontal plane gait angles, moments and navigation angles. Paired two- tailed t-tests were used to compare principal component (PC) scores between pre and post-implant patterns, and a one-way ANOVA was used to test if post-implant patterns were significantly different from zero. Two-tailed Pearson correlation coefficients tested for associations between navigation and gait PCscores, and an un-paired two-tailed t-test was used to compare PCscores between patient-specific and traditional TKA groups. Six different passive kinematic phenotypes were captured pre-implant. Although some waveform patterns persisted at small magnitudes post-implant (PC1 and PC3: p<0.001), curves remained within the clinically acceptable alignment range through passive motion. A positive correlation was found between navigation adduction angle PC1 and gait adduction moment PC1 pre and post-TKA (p<0.001, r=0.79; p<0.01 r=0.67), and a negative correlation between navigation adduction angle PC1 and gait adduction angle PC1 post-TKA (p=0.03, r=-0.53). The patient-specific group showed significantly lower PC2 scores than the traditional TKA group (p=0.03), describing a lower flexion moment magnitude during early stance phase, possibly representing a functional limitation or non- confidence during gait. These results were an important first step to assess patient- specific approaches to TKA, suggesting possible applications for patient-specific intraoperative kinematics to aid in surgical decision-making and influence functional outcomes.

Age related macular degeneration (AMD) is a public health concern in an aging society. The retinal pigment epithelium (RPE) layer of the eye is a principal site of pathogenesis for AMD. Morphological characteristics of the cells in the RPE layer can be used to discriminate age and disease status of individuals. In this thesis three genotypes of mice of various ages are used to study the predictive abilities of these characteristics. The disease state is represented by two mutant genotypes and the healthy state by the wild-type. Classification analysis is applied to the RPE morphology from the different spatial regions of the RPE layer. Variable reduction is accomplished by principal component analysis (PCA) and classification analysis by the k-nearest neighbor (k-NN) algorithm. In this way the differential ability of the spatial regions to predict age and disease status by cellular variables is explored.

The extensive agricultural usage of organohalogenated pesticides has raised many concerns about their potential hazards especially in the soil environment. Environmental metabolomics is an emerging field that investigates the changes in the metabolic profile of native organisms in their environment due to the presence of an environmental stressor. Research presented here explores the potential of Nuclear Magnetic Resonance (NMR)-based metabolomics to examine the sub-lethal exposure of the earthworm, Eisenia fetida to sub-lethal concentrations of organohalogenated pesticides. Various one-dimensional (1-D) and two dimensional (2-D) NMR techniques were compared in a contact filter paper test earthworm metabolomic study using endosulfan, a prevalent pesticide in the environment. The results determined that both the 1H Presaturation Utilizing Gradients and Echos (PURGE) and the 1H-13C Heteronuclear Single Quantum Coherence (HSQC) NMR techniques were most effective in discriminating and identifying significant metabolites in earthworms due to contaminant exposure. These two NMR techniques were further explored in another metabolomic study using various sub-lethal concentrations of endosulfan and an organofluorine pesticide, trifluralin to E. fetida. Principal component analysis (PCA) tests showed increasing separation between the exposed and unexposed earthworms as the concentrations for both contaminants increased. A neurotoxic mode of action (MOA) for endosulfan and a non-polar narcotic MOA for trifluralin were delineated as many significant metabolites, arising from exposure, were identified. The earthworm tissue extract is commonly used as the biological medium for metabolomic studies. However, many overlapping resonances are apparent in an earthworm tissue extract NMR spectrum due to the abundance of metabolites present. To mitigate this spectral overlap, the earthworm’s coelomic fluid (CF) was tested as a complementary biological medium to the tissue extract in an endosulfan exposure metabolomic study to identify additional metabolites of stress. Compared to tests on the tissue extract, a plethora of different metabolites were identified in the earthworm CF using 1-D PURGE and 2-D HSQC NMR techniques. In addition to the neurotoxic MOA identified previously, an apoptotic MOA was also postulated due to endosulfan exposure. This thesis also explored the application of 1-D and 2-D NMR techniques in a soil metabolomic study to understand the exposure of E. fetida to sub-lethal concentrations of endosulfan and its main degradation product, endosulfan sulfate. The earthworm’s CF and tissue extract were both analyzed to maximize the significant metabolites identified due to contaminant exposure. The PCA results identified similar toxicity for both organochlorine contaminants as the same separation, between exposed to the unexposed earthworms, were detected at various concentrations. Both neurotoxic and apopotic MOAs were observed as identical fluctuations of significant metabolites were found. This research demonstrates the potential of NMR-based metabolomics as a powerful environmental monitoring tool to understand sub-lethal organohalogenated pesticide exposure in soil using earthworms as living probes.

A face authentication system based on principal component analysis and neural networks is developed in this thesis. The system consists of three stages / preprocessing, principal component analysis, and recognition. In preprocessing stage, normalization illumination, and head orientation were done. Principal component analysis is applied to find the aspects of face which are important for identification. Eigenvectors and eigenfaces are calculated from the initial face image set. New faces are projected onto the space expanded by eigenfaces and represented by weighted sum of the eigenfaces. These weights are used to identify the faces. Neural network is used to create the face database and recognize and authenticate the face by using these weights. In this work, a separate network was build for each person. The input face is projected onto the eigenface space first and new descriptor is obtained. The new descriptor is used as input to each person&amp / #8217 / s network, trained earlier. The one with maximum output is selected and reported as the host if it passes predefined recognition threshold. The algorithms that have been developed are tested on ORL, Yale and Feret Face Databases.

The main task in this thesis is to design a robot vision system with face detection and tracking capability. Hence there are two main works in the thesis: Firstly, the detection of the face on an image that is taken from the camera on the robot must be achieved. Hence this is a serious real time image processing task and time constraints are very important because of this reason. A processing rate of 1 frame/second is tried to be achieved and hence a fast face detection algorithm had to be used. The Eigenface method and the Subspace LDA (Linear Discriminant Analysis) method are implemented, tested and compared for face detection and Eigenface method proposed by Turk and Pentland is decided to be used. The images are first passed through a number of preprocessing algorithms to obtain better performance, like skin detection, histogram equalization etc. After this filtering process the face candidate regions are put through the face detection algorithm to understand whether there is a face or not in the image. Some modifications are applied to the eigenface algorithm to detect the faces better and faster. Secondly, the robot must move towards the face in the image. This task includes robot motion. The robot to be used for this purpose is a Pioneer 2-DX8 Plus, which is a product of ActivMedia Robotics Inc. and only the interfaces to move the robot have been implemented in the thesis software. The robot is to detect the faces at different distances and arrange its position according to the distance of the human to the robot. Hence a scaling mechanism must be used either in the training images, or in the input image taken from the camera. Because of timing constraint and low camera resolution, a limited number of scaling is applied in the face detection process. With this reason faces of people who are very far or very close to the robot will not be detected. A background independent face detection system is tried to be designed. However the resultant algorithm is slightly dependent to the background. There is no any other constraints in the system.

Different approaches to the face recognition are studied in this thesis. These approaches are PCA (Eigenface), Kernel Eigenface and Fisher LDA. Principal component analysis extracts the most important information contained in the face to construct a computational model that best describes the face. In Eigenface approach, variation between the face images are described by using a set of characteristic face images in order to find out the eigenvectors (Eigenfaces) of the covariance matrix of the distribution, spanned by a training set of face images. Then, every face image is represented by a linear combination of these eigenvectors. Recognition is implemented by projecting a new image into the face subspace spanned by the Eigenfaces and then classifying the face by comparing its position in face space with the positions of known individuals. In Kernel Eigenface method, non-linear mapping of input space is implemented before PCA in order to handle non-linearly embedded properties of images (i.e. background differences, illumination changes, and facial expressions etc.). In Fisher LDA, LDA is applied after PCA to increase the discrimination between classes. These methods are implemented on three databases that are: Yale face database, AT&amp / T (formerly Olivetti Research Laboratory) face database, and METU Vision Lab face database. Experiment results are compared with respect to the effects of changes in illumination, pose and expression. Kernel Eigenface and Fisher LDA show slightly better performance with respect to Eigenfaces method under changes in illumination. Expression differences did not affect the performance of Eigenfaces method. From test results, it can be observed that Eigenfaces approach is an adequate method that can be used in face recognition systems due to its simplicity, speed and learning capability. By this way, it can easily be used in real time systems.