Transgenic Zebrafish as a Novel Animal Model to Study Tauopathies and Other Neurodegenerative Disorders in vivo

Our ageing society is confronted with a dramatic increase in patients suffering from tauopathies such as Alzheimer's disease, frontotemporal dementia and others. Typical neuropathological lesions including tangles composed of hyper-phosphorylated tau protein as well as severe neuronal cell death characterize these disorders. No mechanism-based cures are available at present. Genetically modified animals are invaluable models to understand the molecular disease mechanisms and to screen for modifying compounds. We recently introduced tau-transgenic zebrafish as a novel model for tauopathies. Our model allows recapitulating key pathological features of tauopathies within an extremely short time. Moreover, life imaging of tau-dependent neuronal cell death was performed for the very first time. This demonstrated tau-dependent neuronal cell loss independent of tangle formation. Finally, we exemplified that the zebrafish frontotemporal dementia model can be used to screen for drugs that prevent abnormal tau phosphorylation and neuronal cell death. Copyright (C) 2010 S. Karger AG, Base

Zierpflanzen ökologisch – Anbau und Absatz in der Schweiz, Holland und Deutschland

Die Bedeutung der Öko-Zierpflanzen ist zwar immer noch gering, doch das Interesse an ihnen wächst. Seitdem Anfang 1996 in der Schweiz die erste europäische Tagung zum Thema „Biologischer Zierpflanzenbau“ stattgefunden hat, werden an immer mehr Verkaufsstellen solche Zierpflanzen angeboten. Die Autoren ziehen Bilanz und zeigen auf, was getan werden muss, damit diese Pflanzen den ihnen gebührenden Platz am Markt einnehmen können

Mutual Explanations for Cooperative Decision Making in Medicine

Exploiting mutual explanations for interactive learning is presented as part of an interdisciplinary research project on transparent machine learning for medical decision support. Focus of the project is to combine deep learning black box approaches with interpretable machine learning for classification of different types of medical images to combine the predictive accuracy of deep learning and the transparency and comprehensibility of interpretable models. Specifically, we present an extension of the Inductive Logic Programming system Aleph to allow for interactive learning. Medical experts can ask for verbal explanations. They can correct classification decisions and in addition can also correct the explanations. Thereby, expert knowledge can be taken into account in form of constraints for model adaption

Mutations in ap1b1 Cause Mistargeting of the Na(+)/K(+)-ATPase Pump in Sensory Hair Cells.

The hair cells of the inner ear are polarized epithelial cells with a specialized structure at the apical surface, the mechanosensitive hair bundle. Mechanotransduction occurs within the hair bundle, whereas synaptic transmission takes place at the basolateral membrane. The molecular basis of the development and maintenance of the apical and basal compartments in sensory hair cells is poorly understood. Here we describe auditory/vestibular mutants isolated from forward genetic screens in zebrafish with lesions in the adaptor protein 1 beta subunit 1 (ap1b1) gene. Ap1b1 is a subunit of the adaptor complex AP-1, which has been implicated in the targeting of basolateral membrane proteins. In ap1b1 mutants we observed that although the overall development of the inner ear and lateral-line organ appeared normal, the sensory epithelium showed progressive signs of degeneration. Mechanically-evoked calcium transients were reduced in mutant hair cells, indicating that mechanotransduction was also compromised. To gain insight into the cellular and molecular defects in ap1b1 mutants, we examined the localization of basolateral membrane proteins in hair cells. We observed that the Na(+)/K(+)-ATPase pump (NKA) was less abundant in the basolateral membrane and was mislocalized to apical bundles in ap1b1 mutant hair cells. Accordingly, intracellular Na(+) levels were increased in ap1b1 mutant hair cells. Our results suggest that Ap1b1 is essential for maintaining integrity and ion homeostasis in hair cells

Bläsihof damals: erste landw. Ausbildungsstätte für Landbevölkerung im Kt. ZH

Wie es dazu kam? Was waren damals die Herausforderungen? Wie sah der Bläsihof früher aus? Wie wurde unterrichtet? Wieso ging es nicht weiter

The Next Generation of Medical Decision Support : A Roadmap Toward Transparent Expert Companions

Artikelnummer 507973Increasing quality and performance of artificial intelligence (AI) in general and machine learning (ML) in particular is followed by a wider use of these approaches in everyday life. As part of this development, ML classifiers have also gained more importance for diagnosing diseases within biomedical engineering and medical sciences. However, many of those ubiquitous high-performing ML algorithms reveal a black-box-nature, leading to opaque and incomprehensible systems that complicate human interpretations of single predictions or the whole prediction process. This puts up a serious challenge on human decision makers to develop trust, which is much needed in life-changing decision tasks. This paper is designed to answer the question how expert companion systems for decision support can be designed to be interpretable and therefore transparent and comprehensible for humans. On the other hand, an approach for interactive ML as well as human-in-the-loop-learning is demonstrated in order to integrate human expert knowledge into ML models so that humans and machines act as companions within a critical decision task. We especially address the problem of Semantic Alignment between ML classifiers and its human users as a prerequisite for semantically relevant and useful explanations as well as interactions. Our roadmap paper presents and discusses an interdisciplinary yet integrated Comprehensible Artificial Intelligence (cAI)-transition-framework with regard to the task of medical diagnosis. We explain and integrate relevant concepts and research areas to provide the reader with a hands-on-cookbook for achieving the transition from opaque black-box models to interactive, transparent, comprehensible and trustworthy systems. To make our approach tangible, we present suitable state of the art methods with regard to the medical domain and include a realization concept of our framework. The emphasis is on the concept of Mutual Explanations (ME) that we introduce as a dialog-based, incremental process in order to provide human ML users with trust, but also with stronger participation within the learning process

Decreased skin colonization with Malassezia spp. and increased skin colonization with Candida spp. in patients with severe atopic dermatitis

Background: Atopic dermatitis (AD) is a chronic relapsing inflammatory skin disease in which patients are sensitized towards a plethora of allergens. The hosts fungal microbiota, the mycobiota, that is believed to be altered in patients suffering from AD acts as such an allergen. The correlation context of specific sensitization, changes in mycobiota and its impact on disease severity however remains poorly understood. Objectives: We aim to enhance the understanding of the specific sensitization towards the mycobiota in AD patients in relation to their fungal skin colonization. Methods: Sensitization pattern towards the Malassezia spp. and Candida albicans of 16 AD patients and 14 healthy controls (HC) were analyzed with the newly developed multiplex-assay ALEX2® and the established singleplex-assay ImmunoCAP®. We compared these findings with the fungal skin colonization analyzed by DNA sequencing of the internal transcribed spacer region 1 (ITS1). Results: Sensitization in general and towards Malassezia spp. and C. albicans is increased in AD patients compared to HC with a quantitative difference in severe AD when compared to mild to moderate AD. Further we saw an association between sensitization towards and skin colonization with Candida spp. yet a negative correlation between sensitization towards and skin colonization with Malassezia spp. Conclusion: We conclude that AD in general and severe AD in particular is associated with increased sensitization towards the hosts own mycobiota. There is positive correlation in Candida spp. skin colonization and negative in Malassezia spp. skin colonization when compared to AD, AD severity as well as to specific sensitization patterns

Deriving Temporal Prototypes from Saliency Map Clusters for the Analysis of Deep-Learning-based Facial Action Unit Classification

Reliably determining the emotional state of a person is a difficult task for both humans as well as machines. Automatic detection and evaluation of facial expressions is particularly important if people are unable to express their emotional state themselves, for example due to cognitive impairments. Identifying the presence of Action Units in a human’s face is a psychologically validated approach of quantifying which emotion is expressed. To automate the detection process of Action Units Neural Networks have been trained. However, the black-box nature of Deep Neural Networks provides no insight on the relevant features identified during the decision process. Approaches of Explainable Artificial Intelligence have to be applied to provide an explanation why the network came to a certain conclusion. In this work "Layer-Wise Relevance Propagation" (LRP) in combination with the meta analysis approach "Spectral Relevance Analysis" (SpRAy) is used to derive temporal prototypes from predictions in video sequences. Temporal prototypes provide an aggregated view on the prediction of the network by grouping together similar frames by considering relevance. Additionally, a specific visualization method for temporal prototypes is presented that highlights the most relevant areas for a prediction of an Action Unit. A quantitative evaluation of our approach shows that temporal prototypes aggregate temporal information well. The proposed method can be used to generate concise visual explanations for a sequence of interpretable saliency maps. Based on the above, this work shall provide the foundation for a new temporal analysis method as well as an explanation approach that is supposed to help researchers and experts to gain a deeper understanding of how the underlying network decides which Action Units are active in a particular emotional state

Verifying Deep Learning-based Decisions for Facial Expression Recognition

Neural networks with high performance can still be biased towards non-relevant features. However, reliability and robustness is especially important for high-risk fields such as clinical pain treatment. We therefore propose a verification pipeline, which consists of three steps. First, we classify facial expressions with a neural network. Next, we apply layer-wise relevance propagation to create pixel-based explanations. Finally, we quantify these visual explanations based on a bounding-box method with respect to facial regions. Although our results show that the neural network achieves state-of-the-art results, the evaluation of the visual explanations reveals that relevant facial regions may not be considered.Comment: accepted at ESANN 202