Pig-to-human xenotransplantation is promising for overcoming the shortage of suitable human donor organs, but is hampered by immunological barriers. The next immunological hurdle is the acute vascular rejection (AVR), which is associated with activation of the endothelium and the coagulation system. Recently, we demonstrated that transgenic expression of the zinc finger protein A20 protects porcine cells against apoptotic and inflammatory stimuli (Oropeza et al. 2009 Xenotransplantation 16, 522–534). Compared with other anti-apoptotic proteins, A20 also has immune-modulatory potential as shown in an CD95(Fas)Ligand assay. However, in that study, hA20 was only expressed in skeletal muscle, heart and porcine aortic endothelial cells of transgenic pigs. For use in xenotransplantation, it is critical to produce pigs with ubiquitous expression of hA20. Here, we constructed a new vector based on the Sleeping Beauty transposon plasmid pT2/HB containing the hA20 cDNA driven by the ubiquitously and strongly expressing CAGGS-promoter and co-transfected gal–/– porcine fibroblasts (Hauschild et al. 2011 Proc. Natl. Acad. Sci. USA 108, 15 010) together with the SB transposase 100X plasmid (pT2/HB and SB transposase both kindly provided by Dr. Zoltan Ivics). Cells were selected with 400 µg of G418/mL of medium for 14 days. Subsequently, cells were screened by PCR. Transfected cell clones were pooled and used as donor cells in somatic cell nuclear transfer. Reconstructed embryos were transferred to 2 synchronized sows. Both remained pregnant on Day 25 of gestation. One recipient is expected to deliver in September 2012. The second sow, which received 104 embryos, was sacrificed on Day 26 of pregnancy and 2 fetuses could be obtained (cloning efficiency 1.92%). The fetuses had integrated the transgene into their genome as shown by PCR. Real-time PCR results from fetal fibroblasts indicated similar hA20 mRNA expression levels in both fetuses, whereas wild type controls were negative. The hA20 expression level was 2.5 and 3.1 times lower than in the original pooled of transfected cells. Fetus #1, which showed a slightly higher hA20 mRNA expression, was used for recloning. In total, 3 more recipients received an average of 104 hA20 transgenic embryos each. Currently, the hA20 protein level in fetal fibroblasts is determined by fluorescence activated cell sorting analysis. Once pigs are born, the tissue distribution of hA20 will be analysed. In parallel, the function of the transgene will be studied in the CD95(Fas)Ligand assay. Hearts and kidneys of the hA20-transgenic pigs will be further tested in ex vivo perfusion assays and in a pig-to-baboon xenotransplantation. This approach is promising for advancing pig-to-human xenotransplantation to preclinical application.

Die Replikation von Daten stellt in heterogenen, autonomen Informationssystemen hohe An- spr¨uche an eine geeignete Replikationsstrategie. So m¨ussen Schreib- und Lesezugriffe auf die Replikate derart koordiniert werden, dass ein optimaler Kompromiss hinsichtlich der konkur- rierenden Replikationsziele Verf¨ugbarkeit, Performance und Konsistenz erreicht wird. Dieses Abw¨agen hinsichtlich der Replikationsziele wird dadurch erschwert, dass die beteiligten Infor- mationssysteme ihre Systemzust¨ande ¨andern und dass auf diese Ver¨anderungen reagiert werden muss. F¨ur diese Anwendungsbereiche wurden adaptive Replikationsstrategien entwickelt, die sich zur Laufzeit den ver¨anderten Systemzust¨anden anpassen. In dieser Dissertation wird die regelbasierte Replikationsstrategie RegRess sowie die Regel- sprache RRML vorgestellt, die die Formulierung von Replikationsregeln f¨ur RegRess erm¨oglicht. Bei RegRess erfolgt die Koordination f¨ur Schreib- und Lesezugriffe auf Basis dieser Regeln, indem vor jedem Zugriff eine Inferenz der Regeln durchgef¨uhrt wird, wodurch die von dem Zugriff be- troffenen Replikate ermittelt werden. Durch diese Vorgehensweise wird unterschiedlichstes Kon- sistenzverhalten von RegRess realisiert, insbesondere werden tempor¨are Inkonsistenzen toleriert. Eine Regelmenge mit f¨ur den Anwendungsfall spezifizierten Regeln bildet die Konfiguration von RegRess. Weil in den Regeln Systemzust¨ande ber¨ucksichtigt werden k¨onnen, kann zur Laufzeit das Verhalten angepasst werden. Somit handelt es sich bei RegRess um eine konfigurierbare, adaptive Replikationsstrategie. Mit der Regelsprache RRML k¨onnen so genannte Reaktionsregeln formuliert werden. Bei einer Regel der RRML wird auf Zugriffe auf Replikationseinheiten, die Teilmengen aller logi- schen Objekte bilden, reagiert, indem die Bedingung der Regel gepr¨uft wird. Die Bedingung einer Replikationsregel beinhaltet neben G¨ultigkeitszeitr¨aumen vor allem fachliche und techni- sche Konsistenzbedingungen, die z.B. eine Reaktion auf zeitlichen Verzug der Aktualisierungen oder Nicht-Verf¨ugbarkeit eines Rechners erlauben. Wenn die Bedingung einer Replikationsregel erf¨ullt ist, dann wird im Aktionsteil der Regel die Zugriffsart auf die Replikate festgelegt. Weil die Replikationsregeln widerspr¨uchliche Aktionen ausl¨osen k¨onnen, beinhaltet die RRML eine Widerspruchsbehandlung. Zur Realisierung der Replikationsstrategie RegRess dient der Replikationsmanager KARMA, der neben den Protokollen f¨ur die Schreib- und Lesezugriffe einen Regelinterpreter f¨ur die Repli- kationsregeln der RRML beinhaltet. F¨ur den KARMA wird eine Softwarearchitektur konzipiert, wobei eine Spezifikation der einzelnen Komponenten des KARMA vorgenommen wird. Ein wich- tiger Aspekt bei den Zugriffen auf die Replikate ist die transaktionale Anbindung der beteiligten Systeme. Daher werden Transaktionskonzepte spezifiziert, die bei der Umsetzung der Protokolle ben¨otigt werden. Der Replikationsmanager KARMA ist mittels Plugin-Mechanismus in den Simulator F4SR integriert, der im Rahmen dieser Dissertation entstanden ist. Mit dem F4SR k¨onnen Repli- kationsstrategien oder unterschiedliche Konfigurationen einer Replikationsstrategie verglichen werden. So kann beispielsweise das Verhalten hinsichtlich der Replikationsziele Verf¨ugbarkeit, Performance und Konsistenz f¨ur verschiedene Regelmengen untersucht werden. Der F4SR bietet f¨ur die Analyse verschiedene Diagramme, die die Ergebnisse eines Simulationslaufs illustrieren.

Object recognition problems in computer vision are often based on single image data processing. In various applications this processing can be extended to a complete sequence of images, usually received passively. In contrast, we propose a method for active object recognition, where a camera is selectively moved around a considered object. Doing so, we aim at reliable classification results with a clearly reduced amount of necessary views by optimizing the camera movement for the access of new viewpoints (viewpoint selection). Therefore, the optimization criterion is the gain of class discriminative information when observing the appropriate next image. We show how to apply an unsupervised reinforcement learning algorithm to that problem. Specifically, we focus on the modeling of continuous states, continuous actions and supporting rewards for an optimized recognition. We also present an algorithm for the sequential fusion of gathered image information and we combine all these components into a single framework. The experimental evaluations are split into results for synthetic and real objects with one- or two-dimensional camera actions, respectively. This allows the systematic evaluation of the theoretical correctness as well as the practical applicability of the proposed method. Our experiments showed that the proposed combined viewpoint selection and viewpoint fusion approach is able to significantly improve the recognition rates compared to passive object recognition with randomly chosen views.

This work is devoted to the description of an experimental data acquisition automated method which is required to fill the model of Parkinson's disease preclinical stage. Digital images of the immunostained brain sections of experimental animals are used as a data source. Proposed method: 1) is based on following mathematical morphology operations: opening, grayscale reconstruction, closing, bot-hat transformation, morphological gradient, watershed transformation; 2) enables: to smooth heterogeneous complex background; to select small objects on images depended on given sizes and gray values; to eliminate out-of-focus objects; to separate close objects; to calculate features of selected objects; 3) is intended for automatic extraction of dopaminergic neurons terminals on striatum frontal section images. Experimental investigations confirmed possibility and suitability of section images automated processing and analysis by means of the method. The results of the method use are segmented object contours binary image and object feature list.

This paper describes an information theoretic approach for next best view planning in active state estimation, and its application to three computer vision tasks. In active state estimation, the state estimation process contains sensor actions which affect the state observation, and therefore the final state estimate. We use the information theoretic measure of mutual information to quantify the information content in this estimate. The optimal sensor actions are those that are expected to maximally increase the information content of the estimate.This action selection process is then applied to three seperate computer vision tasks: object recognition, object tracking and object reconstruction. Each task is formulated as an active state estimation problem. In these tasks, a given sensor action describes a camera position, or view. The information theoretic framework allows us to determine the next best view, i.e. the view that best supports the computer vision task.We show the benefits of next best view planning in several experiments, in which we compare the estimation error produced by planned views with the error produced by regularly sampled or unchanging views.

This paper presents new vector quantization based methods for selecting well-suited data for hand-eye calibration from a given sequence of hand and eye movements. Data selection can improve the accuracy of classic hand-eye calibration, and make it possible in the first place in situations where the standard approach of manually selecting positions is inconvenient or even impossible, especially when using continuously recorded data. A variety of methods is proposed, which differ from each other in the dimensionality of the vector quantization compared to the degrees of freedom of the rotation representation, and how the rotation angle is incorporated. The performance of the proposed vector quantization based data selection methods is evaluated using data obtained from a manually moved optical tracking system (hand) and an endoscopic camera (eye).

The paper is devoted to the development and formal representation of the descriptive model of information technology for automating morphologic analysis of cytological specimens (lymphatic system tumors). The main contributions are detailed description of algebraic constructions used for creating of mathematical model of information technology and its specification in the form of algorithmic scheme based on Descriptive Image Algebras. It is specified the descriptive model of an image recognition task and the stage of an image reduction to a recognizable from. The theoretical base of the model is the Descriptive Approach to Image Analysis and its main mathematical tools. It is demonstrated practical application of algebraic tools of the Descriptive Approach to Image Analysis and presented an algorithmic scheme of a technology implementing the apparatus of Descriptive Image Algebras.

The paper is devoted to the development and formal representation of the descriptive model of information technology for automating morphologic analysis of cytological specimens (lymphatic system tumors). The main contributions are detailed description of algebraic constructions used for creating of mathematical model of information technology and its specification in the form of algorithmic scheme based on Descriptive Image Algebras. It is specified the descriptive model of an image recognition task and the stage of an image reduction to a recognizable from. The theoretical base of the model is the Descriptive Approach to Image Analysis and its main mathematical tools. It is demonstrated practical application of algebraic tools of the Descriptive Approach to Image Analysis and presented an algorithmic scheme of a technology implementing the apparatus of Descriptive Image Algebras.

The next generation space internet (NGSI) is based on all-IP-based mobile network that merges land-based network, sea-based network, sky-based network, space-based network, deep space-based network together using existing assess network technologies. There are high signal propagation delays, high error rate, bandwidth variation and time-variety in NGSI. In order to adapt to various space communication environment constraints and bandwidth variation, we propose a reduced dimension scalable video coding scheme based on CCSDS IDCS algorithm and quality of service (QoS) control method by cross layer design (CLD). The experimental result shows that this new method has better performance than that of existing algorithms, and can be adaptive to the bandwidth variation dynamically.

Prosody is used to improve the performance of the automatic speech translation system VERBMOBIL [8]. In our earlier work we have developed efficient and robust word-based features that describe F0, energy, speaking rate, and pauses. These features were used to classify prosodic events. We achieved the best recognition results with 95-dimensional feature vectors that describe a context of +/2 words [4]. In the experiments presented in this paper we additionally used Part-Of-Speech (POS) flags as features. The POS features are based on a hierarchical POS label system with up to 15 classes. The 95-dimensional acoustic-prosdic feature vectors are augmented with up to 105 POS features that describe a context of up to +/3 words. The new features significantly improved the recognition of phrase boundaries, phrase accents and question mood; the recognition errors could be reduced by up to 16.7%. The POS flags allow a neural network (NN) to learn a simple language model. We show that it is important to include this syntactic knowledge during the classification of the acoustic-prosodic features instead of combining it later. This implies that there is some kind of synergy: The POS information helps to correctly classify the acoustic observations. The results presented in this paper provide an effective way to improve the recognition of prosodic events with almost no computational overhead.

Active reconstruction of 3D surfaces deals with the control of camer a viewpoints to minimize error and uncertainty in the reconstructed shape of an object. In this paper we develop a mathematical relationship between the setup and focal lengths of a stereo camera system and the corresponding error in 3D reconstruction of a given surface. We explicitly model the noise in the image plane, which can be interpreted as pixel noise or as uncertainty in the localization of corresponding point features. The results can be used to plan sensor positioning, e.g., using information theoretic concepts for optimal sensor data selection.

We present a probabilistic approach to semantic and pragmatic analysis in restricted domains and methods to improve the understanding performance. As framework we use the EVAR system, an automatic dialog system for answering queries on German Intercity train connections over the public telephone network. We introduce the statistical model extracting the semantic content from a word chain as well as our annotation scheme for the semantic of word chains for a specialized task which allows fast and easy annotation. The task of detecting the semantic contents of a word chain is described as the problem of assigning semantic attributes to words. The statistical framework we use has to deal with incomplete data estimation problems which are solved through applying the Expectation Maximization algorithm. The resulting iterative estimation formulas for the desired parameters are presented. The baseline experiment and the obtained recognition results prove the feasibility of our model. We present several methods that are able to support our probabilistic semantic analysis. We present experiments using categorial systems on the lexicon, different initializing methods for probabilistic parameters and combinations of these methods. The results show that the supporting power of the techniques is small when they are used individually but remarkable for the combination of them.

We start with a brief overview of our work in speech recognition and understanding which led from monomodal (speech only) human-machine dialog to multimodal human-machine interaction and assistance. Our work in speech communication initially had the goal to develop a complete system for question answering by spoken dialog [7,15]. This goal was achieved in various projects funded by the German Research Foundation [14] and the German Federal Ministry of Education and Research [16]. Problems of multilingual communication were considered in projects supported by the European Union [2,4,10]. In the Verbmobil project the speech-to-speech translation problem was investigated and it turned out that prosody and the recognition of emotion was important and extremely useful - if not indispensible - to disambiguate utterances and to influence the dialog strategy [3,17]. Multimodal and multimedia aspects of human-machine communication became a topic in the follow-up projects Embassi [11], SmartKom [1], FORSIP [12], and SmartWeb [9]. The SmartWeb project [19], which involves 17 partners from companies, research institutes, and universities, has the general goal to provide the foundations for multimodal human-machine communication with distributed semantic web services using different mobile devices, hand-held, mounted in a car or to a motor cycle. It uses speech and video signals as well as signals from other sensors, e.g. ECG or skin resistance. A special problem in human-machine interaction and assistance is the question whether the user speaks to the machine or not, that is, the distinction of on- and off-talk. It is shown how on-/off-talk can be classified by the combination of prosodic and image features. Using additional sensors the user state in general is estimated to give further cues to the dialog control. This may be used, for example, to avoid input from the dialog system in a situation where a driver is under stress. In other projects the special problem of children's speech processing was considered [20]. Among others it was investigated whether a manual correction of automatically computed fundamental frequency F0 and word boundaries might have a positive effect on the automatic classification of the 4 classes anger, motherese, emphatic, and neutral; this was not the case, leading to the conclusion that presently there is no need for improved F0 algorithms in emotion recognition. The word accuracy (WA) of native and non-native English speaking children was investigated; it was shown that non-native speakers (age 10-15) achieve about the same WA as children aged 6-7 using a speech recognizer trained with native children speech. The recognizer also was used to develop an automatic scoring of the pronunciation quality of children learning English. A special problem are impairments of speech which may be congenital (e.g. the cleft lip and palate) or acquired by disease (e.g. cancer of the larynx). Impairments are, among others, treated with speech training by speech therapists. They score the speech quality subjectively according to various criteria. The idea is that the WA of an automatic speech recognizer should be highly correlated with the human rating. Using speech samples from laryngectomees it is shown that the machine rating is about as good as the rating of five human experts and can also be done via telephone. This opens the possibility of an objective and standardized rating of speech quality.

In this paper we present a system for statistical object classification and localization that applies a simplified image acquisition process for the learning phase. Instead of using complex setups to take training images in known poses, which is very time-consuming and not possible for some objects, we use a handheld camera. The pose parameters of objects in all training frames that are necessary for creating the object models are determined using a structure-from-motion algorithm. The local feature vectors we use are derived from wavelet multiresolution analysis. We model the object area as a function of 3D transformations and introduce a background model. Experiments made on a real data set taken with a handheld camera with more than 2500 images show that it is possible to obtain good classification and localization rates using this fast image acquisition method.

In visual 3-D reconstruction tasks with mobile cameras, one wishes to move the cameras so that they provide the views that lead to the best reconstruction result. When the camera motion is adapted during the reconstruction, the view of interest is the next best view for the current shape estimate. We present such a next best view planning approach for visual 3-D reconstruction. The reconstruction is based on a probabilistic state estimation with sensor actions. The next best view is determined by a metric of the state estimation's uncertainty. We compare three metrics: D-optimality, which is based on the entropy and corresponds to the (D)eterminant of the covariance matrix of a Gaussian distribution, E-optimality, and T-optimality, which are based on (E)igenvalues or on the (T)race of this matrix, respectively. We show the validity of our approach with a simulation as well as real-world experiments, and compare reconstruction accuracy and computation time for the optimality criteria.