Algorithm-Based Hearing and Speech Therapy Rehabilitation after Cochlear Implantation.

INTRODUCTION: Due to the changes in the indication range for cochlear implants and the demographic development towards an aging society, more and more people are in receipt of cochlear implants. An implantation requires a close-meshed audiological and logopedic aftercare. Hearing therapy rehabilitation currently requires great personnel effort and is time consuming. Hearing and speech therapy rehabilitation can be supported by digital hearing training programs. However, the apps currently on the market are to a limited degree personalized and structured. Increasing digitalization makes it possible, especially in times of pandemics, to decouple hearing therapy treatment from everyday clinical practice. MATERIAL AND METHODS: For this purpose, an app is in development that provides hearing therapy tailored to the patient. The individual factors that influence hearing outcome are considered. Using intelligent algorithms, the app determines the selection of exercises, the level of difficulty and the speed at which the difficulty is increased. RESULTS: The app works autonomously without being connected to local speech therapists. In addition, the app is able to analyze patient difficulties within the exercises and provides conclusions about the need for technical adjustments. CONCLUSIONS: The presented newly developed app represents a possibility to support, replace, expand and improve the classic outpatient hearing and speech therapy after CI implantation. The way the application works allows it to reach more people and provide a time- and cost-saving alternative to traditional therapy.

An incremental approach to automated protein localisation.

BACKGROUND: The subcellular localisation of proteins in intact living cells is an important means for gaining information about protein functions. Even dynamic processes can be captured, which can barely be predicted based on amino acid sequences. Besides increasing our knowledge about intracellular processes, this information facilitates the development of innovative therapies and new diagnostic methods. In order to perform such a localisation, the proteins under analysis are usually fused with a fluorescent protein. So, they can be observed by means of a fluorescence microscope and analysed. In recent years, several automated methods have been proposed for performing such analyses. Here, two different types of approaches can be distinguished: techniques which enable the recognition of a fixed set of protein locations and methods that identify new ones. To our knowledge, a combination of both approaches--i.e. a technique, which enables supervised learning using a known set of protein locations and is able to identify and incorporate new protein locations afterwards--has not been presented yet. Furthermore, associated problems, e.g. the recognition of cells to be analysed, have usually been neglected. RESULTS: We introduce a novel approach to automated protein localisation in living cells. In contrast to well-known techniques, the protein localisation technique presented in this article aims at combining the two types of approaches described above: After an automatic identification of unknown protein locations, a potential user is enabled to incorporate them into the pre-trained system. An incremental neural network allows the classification of a fixed set of protein location as well as the detection, clustering and incorporation of additional patterns that occur during an experiment. Here, the proposed technique achieves promising results with respect to both tasks. In addition, the protein localisation procedure has been adapted to an existing cell recognition approach. Therefore, it is especially well-suited for high-throughput investigations where user interactions have to be avoided. CONCLUSION: We have shown that several aspects required for developing an automatic protein localisation technique--namely the recognition of cells, the classification of protein distribution patterns into a set of learnt protein locations, and the detection and learning of new locations--can be combined successfully. So, the proposed method constitutes a crucial step to render image-based protein localisation techniques amenable to large-scale experiments.

Database driven test case generation for protein-protein docking.

UNLABELLED: We present a method for automatic test case generation for protein-protein docking. A consensus-type approach is proposed processing the whole PDB and classifying protein structures into complexes and unbound proteins by combining information from three different approaches (current PDB-at-a-glance classification, search of complexes by sequence identical unbound structures and chain naming). Out of this classification test cases are generated automatically. All calculations were run on the database. The information stored is available via a web interface. The user can choose several criteria for generating his own subset out of our test cases, e.g. for testing docking algorithms. AVAILABILITY: http://bibiserv.techfak.uni-bielefeld.de/agt-sdp/ CONTACT: fzoellne@techfak.uni-bielefeld.de.

Methods for automatic microarray image segmentation.

This paper describes image processing methods for automatic spotted microarray image analysis. Automatic gridding is important to achieve constant data quality and is, therefore, especially interesting for large-scale experiments as well as for integration of microarray expression data from different sources. We propose a Markov random field (MRF) based approach to high-level grid segmentation, which is robust to common problems encountered with array images and does not require calibration. We also propose an active contour method for single-spot segmentation. Active contour models describe objects in images by properties of their boundaries. Both MRFs and active contour models have been used in various other computer vision applications. The traditional active contour model must be generalized for successful application to microarray spot segmentation. Our active contour model is employed for spot detection in the MRF score functions as well as for spot signal segmentation in quantitative array image analysis. An evaluation using several image series from different sources shows the robustness of our methods.

Comparing bound and unbound protein structures using energy calculation and rotamer statistics.

Protein data in the PDB covers only a snapshot of a protein structure. For flexible docking conformational changes need to be considered. Rotamer statistics provide the likelihood for side chain conformations, and further comparison of bound and unbound state yields differences in preferred positions. Furthermore, we do a full sampling of selected chi angles and apply the AMBER force field. Conformation of energy minima complies with the rotamer statistics. Both types of information target the reduction of search space for enumerative docking algorithms and provide parameters for elastic docking.