Untersuchungen zur Rolle von Isoformen katalytischer Untereinheiten der PKA

ZUSAMMENFASSUNG: Proteinkinasen übernehmen zentrale Aufgaben in der Signaltransduktion höherer Zellen. Dabei ist die cAMP-abhängige Proteinkinase (PKA) bezüglich ihrer Struktur und Funktion eine der am besten charakterisierten Proteinkinasen. Trotzdem ist wenig über direkte Interaktionspartner der katalytischen Untereinheiten (PKA-C) bekannt. In einem Split-Ubiquitin basiertem Yeast Two Hybrid- (Y2H-)System wurden potenzielle Interaktionspartner der PKA-C identifiziert. Als Bait wurden sowohl die humane Hauptisoform Cα (hCα) als auch die Proteinkinase X (PrKX) eingesetzt. Nach der Bestätigung der Funktionalität der PKA-C-Baitproteine, dem Nachweis der Expression und der Interaktion mit dem bekannten Interaktionspartner PKI wurde ein Y2H-Screen gegen eine Mausembryo-cDNA-Expressionsbibliothek durchgeführt. Von 2*10^6 Klonen wurden 76 Kolonien isoliert, die ein mit PrKX interagierendes Preyprotein exprimierten. Über die Sequenzierung der enthaltenen Prey-Vektoren wurden 25 unterschiedliche, potenzielle Interaktionspartner identifiziert. Für hCα wurden über 2*10^6 S. cerevisiae-Kolonien untersucht, von denen 1.959 positiv waren (1.663 unter erhöhter Stringenz). Über die Sequenzierung von ca. 10% der Klone (168) konnten Sequenzen für 67 verschiedene, potenzielle Interaktionspartner der hCα identifiziert werden. 15 der Preyproteine wurden in beiden Screens identifiziert. Die PKA-C-spezifische Wechselwirkung der insgesamt 77 Preyproteine wurde im Bait Dependency Test gegen largeT, ein Protein ohne Bezug zum PKA-System, untersucht. Aus den PKA-C-spezifischen Bindern wurden die löslichen Preyproteine AMY-1, Bax72-192, Fabp3, Gng11, MiF, Nm23-M1, Nm23-M2, Sssca1 und VASP256-375 für die weitere in vitro-Validierung ausgewählt. Die Interaktion von FLAG-Strep-Strep-hCα (FSS-hCα) mit den über Strep-Tactin aus der rekombinanten Expression in E. coli gereinigten One-STrEP-HA-Proteinen (SSHA-Proteine) wurde über Koimmunpräzipitation für SSHA-Fabp3, -Nm23-M1, -Nm23-M2, -Sssca1 und -VASP256-375 bestätigt. In SPR-Untersuchungen, für die hCα kovalent an die Oberfläche eines CM5-Sensorchips gekoppelt wurde, wurden die ATP/Mg2+-Abhängigkeit der Bindungen sowie differentielle Effekte der ATP-kompetitiven Inhibitoren H89 und HA-1077 untersucht. Freie hCα, die vor der Injektion zu den SSHA-Proteinen gegeben wurde, kompetierte im Gegensatz zu FSS-PrKX die Bindung an die hCα-Oberfläche. Erste kinetische Analysen lieferten Gleichgewichtsdissoziationskonstanten im µM- (SSHA-Fabp3, -Sssca1), nM- (SSHA-Nm23-M1, –M2) bzw. pM- (SSHA-VASP256-375) Bereich. In funktionellen Analysen konnte eine Phosphorylierung von SSHA-Sssca1 und VASP256-375 durch hCα und FSS-PrKX im Autoradiogramm nachgewiesen werden. SSHA-VASP256-375 zeigte zudem eine starke Inhibition von hCα im Mobility Shift-Assay. Dieser inhibitorische Effekt sowie die hohe Affinität konnten jedoch auf eine Kombination aus der Linkersequenz des Vektors und dem N-Terminus von VASP256-375 zurückgeführt werden. Über die Wechselwirkungen der hier identifizierten Interaktionspartner Fabp3, Nm23-M1 und Nm23-M2 mit hCα können in Folgeuntersuchungen neue PKA-Funktionen insbesondere im Herzen sowie während der Zellmigration aufgedeckt werden. Sssca1 stellt dagegen ein neues, näher zu charakterisierendes PKA-Substrat dar.

Substrate Conformal Imprint Lithography als innovatives Verfahren zur Herstellung von Nanospektrometern im sichtbaren Spektralbereich

Selbstbestimmung und -gestaltung des eigenen Alltages gewinnen immer mehr an Bedeutung, insbesondere für ältere Mitmenschen in ländlichen Regionen, die auf ärztliche Versorgung angewiesen sind. Die Schaffung sogenannter smart personal environments mittels einer Vielzahl von, nahezu unsichtbar installierten Sensoren im gewohnten Lebensraum liefert dem Anwender (lebens-) notwendige Informationen über seine Umgebung oder seinen eigenen Körper. Dabei gilt es nicht den Anwender mit technischen Daten, wie Spektren, zu überfordern. Vielmehr sollte die Handhabung so einfach wie möglich gestaltet werden und die ausgewertete Information als Indikationsmittel zum weiteren Handeln dienen. Die Anforderungen an moderne Technologien sind folglich eine starke Miniaturisierung, zur optimalen Integration und Mobilität, bei gleichzeitig hoher Auflösung und Stabilität. Die Zielsetzung der vorliegenden Arbeit ist die Miniaturisierung eines spektroskopischen Systems bei gleichzeitig hohem Auflösungsvermögen für die Detektion im sichtbaren Spektralbereich. Eine Möglichkeit für die Herstellung eines konkurrenzfähigen „Mini-„ oder „Mikrospektrometers“ basiert auf Fabry-Pérot (FP) Filtersystemen, da hierbei die Miniaturisierung nicht wie üblich auf Gittersysteme limitiert ist. Der maßgebliche Faktor für das spektrale Auflösungsvermögen des Spektrometers ist die vertikale Präzision und Homogenität der einzelnen 3D Filterkavitäten, die die unterschiedlichen Transmissionswellenlängen der einzelnen Filter festlegen. Die wirtschaftliche Konkurrenzfähigkeit des am INA entwickelten Nanospektremeters wurde durch die maximale Reduzierung der Prozessschritte, nämlich auf einen einzigen Schritt, erreicht. Erstmalig wird eine neuartige Nanoimprint Technologie, die sog. Substrate Conformal Imprint Lithography, für die Herstellung von wellenlängen-selektierenden Filterkavitäten von stark miniaturisierten Spektrometern eingesetzt. Im Zuge dieser Arbeit wird das Design des FP Filtersystems entwickelt und technologisch mittels Dünnschichtdeposition und der Nanoimprinttechnologie realisiert. Ein besonderer Schwerpunkt liegt hierbei in der Untersuchung des Prägematerials, dessen optische Eigenschaften maßgeblich über die Performance des Filtersystems entscheiden. Mit Hilfe eines speziell gefertigten Mikroskopspektrometers werden die gefertigten Filterfelder hinsichtlich ihrer Transmissionseigenschaften und ihres Auflösungsvermögens hin untersucht. Im Hinblick auf publizierte Arbeiten konkurrierender Arbeitsgruppen konnte eine deutliche Verbesserung des miniaturisierten Spektrometers erreicht werden. Die Minimierung der Prozessschritte auf einen einzigen Prägeschritt sorgt gleichzeitig für eine schnelle und zuverlässige Replikation der wellenlängenselektierenden Filterkavitäten. Im Rahmen dieser Arbeit wurde aufgezeigt, dass das angestrebte Nanospektrometer, trotz der sehr geringen Größe, eine hohe Auflösung liefern kann und gerade wegen der starken Miniaturisierung mit kommerziellen Mini- und Mikro-spektrometern konkurrenzfähig ist.

Hierarchical Object Recognition Using Libraries of Parameterized Model Sub-Parts

This thesis describes the development of a model-based vision system that exploits hierarchies of both object structure and object scale. The focus of the research is to use these hierarchies to achieve robust recognition based on effective organization and indexing schemes for model libraries. The goal of the system is to recognize parameterized instances of non-rigid model objects contained in a large knowledge base despite the presence of noise and occlusion. Robustness is achieved by developing a system that can recognize viewed objects that are scaled or mirror-image instances of the known models or that contain components sub-parts with different relative scaling, rotation, or translation than in models. The approach taken in this thesis is to develop an object shape representation that incorporates a component sub-part hierarchy- to allow for efficient and correct indexing into an automatically generated model library as well as for relative parameterization among sub-parts, and a scale hierarchy- to allow for a general to specific recognition procedure. After analysis of the issues and inherent tradeoffs in the recognition process, a system is implemented using a representation based on significant contour curvature changes and a recognition engine based on geometric constraints of feature properties. Examples of the system's performance are given, followed by an analysis of the results. In conclusion, the system's benefits and limitations are presented.

The Use of Grouping in Visual Object Recognition.

The report describes a recognition system called GROPER, which performs grouping by using distance and relative orientation constraints that estimate the likelihood of different edges in an image coming from the same object. The thesis presents both a theoretical analysis of the grouping problem and a practical implementation of a grouping system. GROPER also uses an indexing module to allow it to make use of knowledge of different objects, any of which might appear in an image. We test GROPER by comparing it to a similar recognition system that does not use grouping.

Statistical Object Recognition

Two formulations of model-based object recognition are described. MAP Model Matching evaluates joint hypotheses of match and pose, while Posterior Marginal Pose Estimation evaluates the pose only. Local search in pose space is carried out with the Expectation--Maximization (EM) algorithm. Recognition experiments are described where the EM algorithm is used to refine and evaluate pose hypotheses in 2D and 3D. Initial hypotheses for the 2D experiments were generated by a simple indexing method: Angle Pair Indexing. The Linear Combination of Views method of Ullman and Basri is employed as the projection model in the 3D experiments.

Geometry and Photometry in 3D Visual Recognition

The report addresses the problem of visual recognition under two sources of variability: geometric and photometric. The geometric deals with the relation between 3D objects and their views under orthographic and perspective projection. The photometric deals with the relation between 3D matte objects and their images under changing illumination conditions. Taken together, an alignment-based method is presented for recognizing objects viewed from arbitrary viewing positions and illuminated by arbitrary settings of light sources.

Attentional Selection in Object Recognition

A key problem in object recognition is selection, namely, the problem of identifying regions in an image within which to start the recognition process, ideally by isolating regions that are likely to come from a single object. Such a selection mechanism has been found to be crucial in reducing the combinatorial search involved in the matching stage of object recognition. Even though selection is of help in recognition, it has largely remained unsolved because of the difficulty in isolating regions belonging to objects under complex imaging conditions involving occlusions, changing illumination, and object appearances. This thesis presents a novel approach to the selection problem by proposing a computational model of visual attentional selection as a paradigm for selection in recognition. In particular, it proposes two modes of attentional selection, namely, attracted and pay attention modes as being appropriate for data and model-driven selection in recognition. An implementation of this model has led to new ways of extracting color, texture and line group information in images, and their subsequent use in isolating areas of the scene likely to contain the model object. Among the specific results in this thesis are: a method of specifying color by perceptual color categories for fast color region segmentation and color-based localization of objects, and a result showing that the recognition of texture patterns on model objects is possible under changes in orientation and occlusions without detailed segmentation. The thesis also presents an evaluation of the proposed model by integrating with a 3D from 2D object recognition system and recording the improvement in performance. These results indicate that attentional selection can significantly overcome the computational bottleneck in object recognition, both due to a reduction in the number of features, and due to a reduction in the number of matches during recognition using the information derived during selection. Finally, these studies have revealed a surprising use of selection, namely, in the partial solution of the pose of a 3D object.

An Analysis of the Effect of Gaussian Error in Object Recognition

Object recognition is complicated by clutter, occlusion, and sensor error. Since pose hypotheses are based on image feature locations, these effects can lead to false negatives and positives. In a typical recognition algorithm, pose hypotheses are tested against the image, and a score is assigned to each hypothesis. We use a statistical model to determine the score distribution associated with correct and incorrect pose hypotheses, and use binary hypothesis testing techniques to distinguish between them. Using this approach we can compare algorithms and noise models, and automatically choose values for internal system thresholds to minimize the probability of making a mistake.

Object Recognition with Pictorial Structures

This thesis presents a statistical framework for object recognition. The framework is motivated by the pictorial structure models introduced by Fischler and Elschlager nearly 30 years ago. The basic idea is to model an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. The problem of detecting an object in an image and the problem of learning an object model using training examples are naturally formulated under a statistical approach. We present efficient algorithms to solve these problems in our framework. We demonstrate our techniques by training models to represent faces and human bodies. The models are then used to locate the corresponding objects in novel images.

Surface Reflectance Recognition and Real-World Illumination Statistics

Humans distinguish materials such as metal, plastic, and paper effortlessly at a glance. Traditional computer vision systems cannot solve this problem at all. Recognizing surface reflectance properties from a single photograph is difficult because the observed image depends heavily on the amount of light incident from every direction. A mirrored sphere, for example, produces a different image in every environment. To make matters worse, two surfaces with different reflectance properties could produce identical images. The mirrored sphere simply reflects its surroundings, so in the right artificial setting, it could mimic the appearance of a matte ping-pong ball. Yet, humans possess an intuitive sense of what materials typically "look like" in the real world. This thesis develops computational algorithms with a similar ability to recognize reflectance properties from photographs under unknown, real-world illumination conditions. Real-world illumination is complex, with light typically incident on a surface from every direction. We find, however, that real-world illumination patterns are not arbitrary. They exhibit highly predictable spatial structure, which we describe largely in the wavelet domain. Although they differ in several respects from the typical photographs, illumination patterns share much of the regularity described in the natural image statistics literature. These properties of real-world illumination lead to predictable image statistics for a surface with given reflectance properties. We construct a system that classifies a surface according to its reflectance from a single photograph under unknown illuminination. Our algorithm learns relationships between surface reflectance and certain statistics computed from the observed image. Like the human visual system, we solve the otherwise underconstrained inverse problem of reflectance estimation by taking advantage of the statistical regularity of illumination. For surfaces with homogeneous reflectance properties and known geometry, our system rivals human performance.

Rotation Invariant Real-time Face Detection and Recognition System

In this report, a face recognition system that is capable of detecting and recognizing frontal and rotated faces was developed. Two face recognition methods focusing on the aspect of pose invariance are presented and evaluated - the whole face approach and the component-based approach. The main challenge of this project is to develop a system that is able to identify faces under different viewing angles in realtime. The development of such a system will enhance the capability and robustness of current face recognition technology. The whole-face approach recognizes faces by classifying a single feature vector consisting of the gray values of the whole face image. The component-based approach first locates the facial components and extracts them. These components are normalized and combined into a single feature vector for classification. The Support Vector Machine (SVM) is used as the classifier for both approaches. Extensive tests with respect to the robustness against pose changes are performed on a database that includes faces rotated up to about 40 degrees in depth. The component-based approach clearly outperforms the whole-face approach on all tests. Although this approach isproven to be more reliable, it is still too slow for real-time applications. That is the reason why a real-time face recognition system using the whole-face approach is implemented to recognize people in color video sequences.

A Detailed Look at Scale and Translation Invariance in a Hierarchical Neural Model of Visual Object Recognition

The HMAX model has recently been proposed by Riesenhuber & Poggio as a hierarchical model of position- and size-invariant object recognition in visual cortex. It has also turned out to model successfully a number of other properties of the ventral visual stream (the visual pathway thought to be crucial for object recognition in cortex), and particularly of (view-tuned) neurons in macaque inferotemporal cortex, the brain area at the top of the ventral stream. The original modeling study only used ``paperclip'' stimuli, as in the corresponding physiology experiment, and did not explore systematically how model units' invariance properties depended on model parameters. In this study, we aimed at a deeper understanding of the inner workings of HMAX and its performance for various parameter settings and ``natural'' stimulus classes. We examined HMAX responses for different stimulus sizes and positions systematically and found a dependence of model units' responses on stimulus position for which a quantitative description is offered. Interestingly, we find that scale invariance properties of hierarchical neural models are not independent of stimulus class, as opposed to translation invariance, even though both are affine transformations within the image plane.

View-Based Strategies for 3D Object Recognition

A persistent issue of debate in the area of 3D object recognition concerns the nature of the experientially acquired object models in the primate visual system. One prominent proposal in this regard has expounded the use of object centered models, such as representations of the objects' 3D structures in a coordinate frame independent of the viewing parameters [Marr and Nishihara, 1978]. In contrast to this is another proposal which suggests that the viewing parameters encountered during the learning phase might be inextricably linked to subsequent performance on a recognition task [Tarr and Pinker, 1989; Poggio and Edelman, 1990]. The 'object model', according to this idea, is simply a collection of the sample views encountered during training. Given that object centered recognition strategies have the attractive feature of leading to viewpoint independence, they have garnered much of the research effort in the field of computational vision. Furthermore, since human recognition performance seems remarkably robust in the face of imaging variations [Ellis et al., 1989], it has often been implicitly assumed that the visual system employs an object centered strategy. In the present study we examine this assumption more closely. Our experimental results with a class of novel 3D structures strongly suggest the use of a view-based strategy by the human visual system even when it has the opportunity of constructing and using object-centered models. In fact, for our chosen class of objects, the results seem to support a stronger claim: 3D object recognition is 2D view-based.

Fast Object Recognition in Noisy Images Using Simulated Annealing

A fast simulated annealing algorithm is developed for automatic object recognition. The normalized correlation coefficient is used as a measure of the match between a hypothesized object and an image. Templates are generated on-line during the search by transforming model images. Simulated annealing reduces the search time by orders of magnitude with respect to an exhaustive search. The algorithm is applied to the problem of how landmarks, for example, traffic signs, can be recognized by an autonomous vehicle or a navigating robot. The algorithm works well in noisy, real-world images of complicated scenes for model images with high information content.