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Striated muscle contraction is regulated by the translocation of troponin-tropomyosin strands over the thin filament surface. Relaxation relies partly on highly-favorable, conformation-dependent electrostatic contacts between actin and tropomyosin, which position tropomyosin such that it impedes actomyosin associations. Impaired relaxation and hypercontractile properties are hallmarks of various muscle disorders. The α-cardiac actin M305L hypertrophic cardiomyopathy-causing mutation lies near residues that help confine tropomyosin to an inhibitory position along thin filaments. Here, we investigate M305L actin in vivo, in vitro, and in silico to resolve emergent pathological properties and disease mechanisms. Our data suggest the mutation reduces actin flexibility and distorts the actin-tropomyosin electrostatic energy landscape that, in muscle, result in aberrant contractile inhibition and excessive force. Thus, actin flexibility may be required to establish and maintain interfacial contacts with tropomyosin as well as facilitate its movement over distinct actin surface features and is, therefore, likely necessary for proper regulation of contraction.
Mendelian diseases of dysregulated canonical NF-κB signaling: From immunodeficiency to inflammation
(2020)
It is only a matter of time until autonomous vehicles become ubiquitous; however, human driving supervision will remain a necessity for decades. To assess the drive's ability to take control over the vehicle in critical scenarios, driver distractions can be monitored using wearable sensors or sensors that are embedded in the vehicle, such as video cameras. The types of driving distractions that can be sensed with various sensors is an open research question that this study attempts to answer. This study compared data from physiological sensors (palm electrodermal activity (pEDA), heart rate and breathing rate) and visual sensors (eye tracking, pupil diameter, nasal EDA (nEDA), emotional activation and facial action units (AUs)) for the detection of four types of distractions. The dataset was collected in a previous driving simulation study. The statistical tests showed that the most informative feature/modality for detecting driver distraction depends on the type of distraction, with emotional activation and AUs being the most promising. The experimental comparison of seven classical machine learning (ML) and seven end-to-end deep learning (DL) methods, which were evaluated on a separate test set of 10 subjects, showed that when classifying windows into distracted or not distracted, the highest F1-score of 79%; was realized by the extreme gradient boosting (XGB) classifier using 60-second windows of AUs as input. When classifying complete driving sessions, XGB's F1-score was 94%. The best-performing DL model was a spectro-temporal ResNet, which realized an F1-score of 75%; when classifying segments and an F1-score of 87%; when classifying complete driving sessions. Finally, this study identified and discussed problems, such as label jitter, scenario overfitting and unsatisfactory generalization performance, that may adversely affect related ML approaches.
This study advances the research and methodological approach to measuring and understanding national-level destination competitiveness, sustainability and governance, by creating a model that could be of use for both developing and developed destinations. The study gives a detailed overview of the research field of measuring destination competitiveness and sustainability. It also identifies major predictors of destination competitiveness and sustainability and thereby presents destination researchers and practitioners with a useful list of priority areas, both from a global perspective and from the perspective of other similar destinations. Finally, the study identifies two major types of destination governance with implications for research, policy and practice across the destination life-cycle. The research deals with the analysis of the secondary data from the World Economic Forum Travel and Tourism Index (WEF T&T). Major types of destination governance and predictors of belonging to either one of the types, as well as inside cluster predictors have been extracted through a two-step cluster analysis. The results support the notion that a meaningful model of national-level destination governance needs to take into account different development levels of different destinations. The main limitation of the study is its typology creation approach, as it inevitably leads to simplifications.
Towards a conceptual framework for sustainable business models in the food and beverage industry
(2020)
Privatheit am Arbeitsplatz
(2020)
Eco-InfoVis at Work
(2020)