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- ItemResearch data management in agricultural sciences in Germany: We are not yet where we want to be(San Francisco, California, US : PLOS, 2022) Senft, Matthias; Stahl, Ulrike; Svoboda, NikolaiTo meet the future challenges and foster integrated and holistic research approaches in agricultural sciences, new and sustainable methods in research data management (RDM) are needed. The involvement of scientific users is a critical success factor for their development. We conducted an online survey in 2020 among different user groups in agricultural sciences about their RDM practices and needs. In total, the questionnaire contained 52 questions on information about produced and (re-)used data, data quality aspects, information about the use of standards, publication practices and legal aspects of agricultural research data, the current situation in RDM in regards to awareness, consulting and curricula as well as needs of the agricultural community in respect to future developments. We received 196 (partially) completed questionnaires from data providers, data users, infrastructure and information service providers. In addition to the diversity in the research data landscape of agricultural sciences in Germany, the study reveals challenges, deficits and uncertainties in handling research data in agricultural sciences standing in the way of access and efficient reuse of valuable research data. However, the study also suggests and discusses potential solutions to enhance data publications, facilitate and secure data re-use, ensure data quality and develop services (i.e. training, support and bundling services). Therefore, our research article provides the basis for the development of common RDM, future infrastructures and services needed to foster the cultural change in handling research data across agricultural sciences in Germany and beyond.
- ItemOut of the lab and into the bathroom: Evening short-term exposure to conventional light suppresses melatonin and increases alertness perception(Basel : MDPI AG, 2013) Wahnschaffe, A.; Haedel, S.; Rodenbeck, A.; Stoll, C.; Rudolph, H.; Kozakov, R.; Schoepp, H.; Kunz, D.Life in 24-h society relies on the use of artificial light at night that might disrupt synchronization of the endogenous circadian timing system to the solar day. This could have a negative impact on sleep-wake patterns and psychiatric symptoms. The aim of the study was to investigate the influence of evening light emitted by domestic and work place lamps in a naturalistic setting on melatonin levels and alertness in humans. Healthy subjects (6 male, 3 female, 22-33 years) were exposed to constant dim light (<10 lx) for six evenings from 7:00 p.m. to midnight. On evenings 2 through 6, 1 h before habitual bedtime, they were also exposed to light emitted by 5 different conventional lamps for 30 min. Exposure to yellow light did not alter the increase of melatonin in saliva compared to dim light baseline during (38 ± 27 pg/mL vs. 39 ± 23 pg/mL) and after light exposure (39 ± 22 pg/mL vs. 44 ± 26 pg/mL). In contrast, lighting conditions including blue components reduced melatonin increase significantly both during (office daylight white: 25 ± 16 pg/mL, bathroom daylight white: 24 ± 10 pg/mL, Planon warm white: 26 ± 14 pg/mL, hall daylight white: 22 ± 14 pg/mL) and after light exposure (office daylight white: 25 ± 15 pg/mL, bathroom daylight white: 23 ± 9 pg/mL, Planon warm white: 24 ± 13 pg/mL, hall daylight white: 22 ± 26 pg/mL). Subjective alertness was significantly increased after exposure to three of the lighting conditions which included blue spectral components in their spectra. Evening exposure to conventional lamps in an everyday setting influences melatonin excretion and alertness perception within 30 min.
- ItemEvolutionary design of explainable algorithms for biomedical image segmentation([London] : Nature Publishing Group UK, 2023) Cortacero, Kévin; McKenzie, Brienne; Müller, Sabina; Khazen, Roxana; Lafouresse, Fanny; Corsaut, Gaëlle; Van Acker, Nathalie; Frenois, François-Xavier; Lamant, Laurence; Meyer, Nicolas; Vergier, Béatrice; Wilson, Dennis G.; Luga, Hervé; Staufer, Oskar; Dustin, Michael L.; Valitutti, Salvatore; Cussat-Blanc, SylvainAn unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting “black box” models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches.
- ItemOrder patterns networks (orpan) - A method to estimate time-evolving functional connectivity from multivariate time series(Lausanne : Frontiers Research Foundation, 2012) Schinkel, S.; Zamora-López, G.; Dimigen, O.; Sommer, W.; Kurths, J.Complex networks provide an excellent framework for studying the function of the human brain activity. Yet estimating functional networks from measured signals is not trivial, especially if the data is non-stationary and noisy as it is often the case with physiological recordings. In this article we propose a method that uses the local rank structure of the data to define functional links in terms of identical rank structures. The method yields temporal sequences of networks which permits to trace the evolution of the functional connectivity during the time course of the observation. We demonstrate the potentials of this approach with model data as well as with experimental data from an electrophysiological study on language processing.
- ItemColiCoords: A Python package for the analysis of bacterial fluorescence microscopy data(San Francisco, California, US : PLOS, 2019) Smit, Jochem H.; Li, Yichen; Warszawik, Eliza M.; Herrmann, Andreas; Cordes, Thorben; Gilestro, Giorgio FSingle-molecule fluorescence microscopy studies of bacteria provide unique insights into the mechanisms of cellular processes and protein machineries in ways that are unrivalled by any other technique. With the cost of microscopes dropping and the availability of fully automated microscopes, the volume of microscopy data produced has increased tremendously. These developments have moved the bottleneck of throughput from image acquisition and sample preparation to data analysis. Furthermore, requirements for analysis procedures have become more stringent given the demand of various journals to make data and analysis procedures available. To address these issues we have developed a new data analysis package for analysis of fluorescence microscopy data from rod-like cells. Our software ColiCoords structures microscopy data at the single-cell level and implements a coordinate system describing each cell. This allows for the transformation of Cartesian coordinates from transmission light and fluorescence images and single-molecule localization microscopy (SMLM) data to cellular coordinates. Using this transformation, many cells can be combined to increase the statistical power of fluorescence microscopy datasets of any kind. ColiCoords is open source, implemented in the programming language Python, and is extensively documented. This allows for modifications for specific needs or to inspect and publish data analysis procedures. By providing a format that allows for easy sharing of code and associated data, we intend to promote open and reproducible research. The source code and documentation can be found via the project’s GitHub page.