Fakultätsübergreifend / Sonstige Einrichtung

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CortexVR: Immersive analysis and training of cognitive executive functions of soccer players using virtual reality and machine learning
(2022) Krupitzer, Christian; Naber, Jens; Stauffert, Jan-Philipp; Mayer, Jan; Spielmann, Jan; Ehmann, Paul; Boci, Noel; Bürkle, Maurice; Ho, André; Komorek, Clemens; Heinickel, Felix; Kounev, Samuel; Becker, Christian; Latoschik, Marc Erich
Goal: This paper presents an immersive Virtual Reality (VR) system to analyze and train Executive Functions (EFs) of soccer players. EFs are important cognitive functions for athletes. They are a relevant quality that distinguishes amateurs from professionals. Method: The system is based on immersive technology, hence, the user interacts naturally and experiences a training session in a virtual world. The proposed system has a modular design supporting the extension of various so-called game modes. Game modes combine selected game mechanics with specific simulation content to target particular training aspects. The system architecture decouples selection/parameterization and analysis of training sessions via a coaching app from an Unity3D-based VR simulation core. Monitoring of user performance and progress is recorded by a database that sends the necessary feedback to the coaching app for analysis. Results: The system is tested for VR-critical performance criteria to reveal the usefulness of a new interaction paradigm in the cognitive training and analysis of EFs. Subjective ratings for overall usability show that the design as VR application enhances the user experience compared to a traditional desktop app; whereas the new, unfamiliar interaction paradigm does not negatively impact the effort for using the application. Conclusion: The system can provide immersive training of EF in a fully virtual environment, eliminating potential distraction. It further provides an easy-to-use analyzes tool to compare user but also an automatic, adaptive training mode.
DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics
(2021) Kienbaum, Lydia; Correa Abondano, Miguel; Blas, Raul; Schmid, Karl
Background: Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. Results: Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy (r = 0.99). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10–20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering. Conclusions: Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.
Food informatics - Review of the current state-of-the-art, revised definition, and classification into the research landscape
(2021) Krupitzer, Christian; Stein, Anthony
Background: The increasing population of humans, changing food consumption behavior, as well as the recent developments in the awareness for food sustainability, lead to new challenges for the production of food. Advances in the Internet of Things (IoT) and Artificial Intelligence (AI) technology, including Machine Learning and data analytics, might help to account for these challenges. Scope and Approach: Several research perspectives, among them Precision Agriculture, Industrial IoT, Internet of Food, or Smart Health, already provide new opportunities through digitalization. In this paper, we review the current state-of-the-art of the mentioned concepts. An additional concept is Food Informatics, which so far is mostly recognized as a mainly data-driven approach to support the production of food. In this review paper, we propose and discuss a new perspective for the concept of Food Informatics as a supportive discipline that subsumes the incorporation of information technology, mainly IoT and AI, in order to support the variety of aspects tangent to the food production process and delineate it from other, existing research streams in the domain. Key Findings and Conclusions: Many different concepts related to the digitalization in food science overlap. Further, Food Informatics is vaguely defined. In this paper, we provide a clear definition of Food Informatics and delineate it from related concepts. We corroborate our new perspective on Food Informatics by presenting several case studies about how it can support the food production as well as the intermediate steps until its consumption, and further describe its integration with related concepts.
Functionality of the Na+-translocating NADH:quinone oxidoreductase and quinol:fumarate reductase from Prevotella bryantii inferred from homology modeling
(2024) Hau, Jann-Louis; Schleicher, Lena; Herdan, Sebastian; Simon, Jörg; Seifert, Jana; Fritz, Günter; Steuber, Julia; Hau, Jann-Louis; Institute of Biology, University of Hohenheim, Garbenstraße 30, 70599, Stuttgart, Germany; Schleicher, Lena; Institute of Biology, University of Hohenheim, Garbenstraße 30, 70599, Stuttgart, Germany; Herdan, Sebastian; Institute of Biology, University of Hohenheim, Garbenstraße 30, 70599, Stuttgart, Germany; Simon, Jörg; Microbial Energy Conservation and Biotechnology, Department of Biology, Technical University of Darmstadt, Schnittspahnstraße 10, 64287, Darmstadt, Germany; Seifert, Jana; HoLMiR-Hohenheim Center for Livestock Microbiome Research, University of Hohenheim, Leonore-Blosser-Reisen-Weg 3, 70599, Stuttgart, Germany; Fritz, Günter; Institute of Biology, University of Hohenheim, Garbenstraße 30, 70599, Stuttgart, Germany; Steuber, Julia; Institute of Biology, University of Hohenheim, Garbenstraße 30, 70599, Stuttgart, Germany
Members of the family Prevotellaceae are Gram-negative, obligate anaerobic bacteria found in animal and human microbiota. In Prevotella bryantii , the Na + -translocating NADH:quinone oxidoreductase (NQR) and quinol:fumarate reductase (QFR) interact using menaquinone as electron carrier, catalyzing NADH:fumarate oxidoreduction. P. bryantii NQR establishes a sodium-motive force, whereas P. bryantii QFR does not contribute to membrane energization. To elucidate the possible mode of function, we present 3D structural models of NQR and QFR from P. bryantii to predict cofactor-binding sites, electron transfer routes and interaction with substrates. Molecular docking reveals the proposed mode of menaquinone binding to the quinone site of subunit NqrB of P. bryantii NQR. A comparison of the 3D model of P. bryantii QFR with experimentally determined structures suggests alternative pathways for transmembrane proton transport in this type of QFR . Our findings are relevant for NADH-dependent succinate formation in anaerobic bacteria which operate both NQR and QFR.

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