@article{2950,
  abstract     = {Contractile actomyosin rings drive various fundamental morphogenetic processes ranging from cytokinesis to wound healing. Actomyosin rings are generally thought to function by circumferential contraction. Here, we show that the spreading of the enveloping cell layer (EVL) over the yolk cell during zebrafish gastrulation is driven by a contractile actomyosin ring. In contrast to previous suggestions, we find that this ring functions not only by circumferential contraction but also by a flow-friction mechanism. This generates a pulling force through resistance against retrograde actomyosin flow. EVL spreading proceeds normally in situations where circumferential contraction is unproductive, indicating that the flow-friction mechanism is sufficient. Thus, actomyosin rings can function in epithelial morphogenesis through a combination of cable-constriction and flow-friction mechanisms.},
  author       = {Behrndt, Martin and Salbreux, Guillaume and Campinho, Pedro and Hauschild, Robert and Oswald, Felix and Roensch, Julia and Grill, Stephan and Heisenberg, Carl-Philipp J},
  journal      = {Science},
  number       = {6104},
  pages        = {257 -- 260},
  publisher    = {American Association for the Advancement of Science},
  title        = {{Forces driving epithelial spreading in zebrafish gastrulation}},
  doi          = {10.1126/science.1224143},
  volume       = {338},
  year         = {2012},
}

@article{2965,
  abstract     = {Dieser Artikel soll die sechs verschiedenen Creative Commons Lizenzen erläutern und ihre Bedeutung im Rahmen des wissenschaftlichen Publizierens und des Open Access erklären (CC-BY, CC-BY-SA, CC-BY-NC, CC-BY-ND, CC-BYNC-SA, CC-BY-NC-ND).},
  author       = {Danowski, Patrick},
  journal      = {Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare},
  number       = {2},
  pages        = {200 -- 212},
  publisher    = {Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare},
  title        = {{Kontext Open Access: Creative Commons}},
  volume       = {65},
  year         = {2012},
}

@article{490,
  abstract     = {BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals. },
  author       = {Schlögl, Alois and Vidaurre, Carmen and Sander, Tilmann},
  journal      = {Computational Intelligence and Neuroscience},
  publisher    = {Hindawi Publishing Corporation},
  title        = {{BioSig: The free and open source software library for biomedical signal processing}},
  doi          = {10.1155/2011/935364},
  volume       = {2011},
  year         = {2011},
}

@inproceedings{9943,
  abstract     = {Segmentation is the process of partitioning digital images into meaningful regions. The analysis of biological high content images often requires segmentation as a first step. We propose ilastik as an easy-to-use tool which allows the user without expertise in image processing to perform segmentation and classification in a unified way. ilastik learns from labels provided by the user through a convenient mouse interface. Based on these labels, ilastik infers a problem specific segmentation. A random forest classifier is used in the learning step, in which each pixel's neighborhood is characterized by a set of generic (nonlinear) features. ilastik supports up to three spatial plus one spectral dimension and makes use of all dimensions in the feature calculation. ilastik provides realtime feedback that enables the user to interactively refine the segmentation result and hence further fine-tune the classifier. An uncertainty measure guides the user to ambiguous regions in the images. Real time performance is achieved by multi-threading which fully exploits the capabilities of modern multi-core machines. Once a classifier has been trained on a set of representative images, it can be exported and used to automatically process a very large number of images (e.g. using the CellProfiler pipeline). ilastik is an open source project and released under the BSD license at www.ilastik.org.},
  author       = {Sommer, Christoph M and Straehle, Christoph and Köthe, Ullrich and Hamprecht, Fred A.},
  booktitle    = {2011 IEEE International Symposium on Biomedical Imaging: from Nano to Micro},
  isbn         = {978-1-4244-4127-3},
  issn         = {1945-8452},
  keywords     = {image segmentation, biomedical imaging, three dimensional displays, neurons, retina, observers, image color analysis},
  location     = {Chicago, Illinois, USA},
  publisher    = {Institute of Electrical and Electronics Engineers},
  title        = {{Ilastik: Interactive learning and segmentation toolkit}},
  doi          = {10.1109/isbi.2011.5872394},
  year         = {2011},
}

@inbook{14983,
  abstract     = {This chapter tackles a difficult challenge: presenting signal processing material to non-experts. This chapter is meant to be comprehensible to people who have some math background, including a course in linear algebra and basic statistics, but do not specialize in mathematics, engineering, or related fields. Some formulas assume the reader is familiar with matrices and basic matrix operations, but not more advanced material. Furthermore, we tried to make the chapter readable even if you skip the formulas. Nevertheless, we include some simple methods to demonstrate the basics of adaptive data processing, then we proceed with some advanced methods that are fundamental in adaptive signal processing, and are likely to be useful in a variety of applications. The advanced algorithms are also online available [30]. In the second part, these techniques are applied to some real-world BCI data.},
  author       = {Schlögl, Alois and Vidaurre, Carmen and Müller, Klaus-Robert},
  booktitle    = {Brain-Computer Interfaces},
  editor       = {Graimann, Bernhard and Pfurtscheller, Gert and Allison, Brendan},
  isbn         = {9783642020902},
  issn         = {1612-3018},
  pages        = {331--355},
  publisher    = {Springer},
  title        = {{Adaptive Methods in BCI Research - An Introductory Tutorial}},
  doi          = {10.1007/978-3-642-02091-9_18},
  year         = {2010},
}

@article{4157,
  abstract     = {Integrin- and cadherin-mediated adhesion is central for cell and tissue morphogenesis, allowing cells and tissues to change shape without loosing integrity. Studies predominantly in cell culture showed that mechanosensation through adhesion structures is achieved by force-mediated modulation of their molecular composition. The specific molecular composition of adhesion sites in turn determines their signalling activity and dynamic reorganization. Here, we will review how adhesion sites respond to mecanical stimuli, and how spatially and temporally regulated signalling from different adhesion sites controls cell migration and tissue morphogenesis.},
  author       = {Papusheva, Ekaterina and Heisenberg, Carl-Philipp J},
  journal      = {EMBO Journal},
  number       = {16},
  pages        = {2753 -- 2768},
  publisher    = {Wiley-Blackwell},
  title        = {{Spatial organization of adhesion: force-dependent regulation and function in tissue morphogenesis}},
  doi          = {10.1038/emboj.2010.182},
  volume       = {29},
  year         = {2010},
}