@article{RadingerBraunJechowetal.2026, author = {Radinger, Johannes and Braun, Marlon and Jechow, Andreas and H{\"o}lker, Franz and Michiels, Niko K. and Feldhege, Fritz and Roser, Phillip and Arlinghaus, Robert}, title = {Effectiveness of fluorescent angling lures on catch outcomes in European perch (Perca fluviatilis) under ambient light conditions}, series = {Canadian Journal of Fisheries and Aquatic Sciences}, volume = {83}, journal = {Canadian Journal of Fisheries and Aquatic Sciences}, publisher = {Canadian Science Publishing}, doi = {10.1139/cjfas-2025-0084}, pages = {1 -- 16}, year = {2026}, abstract = {Anecdotal reports in angling media suggest that using fluorescent lures may increase catch rates in dim light or at high turbidity. We conducted a controlled angling experiment, comprising 501 30-min experimental fishing trials in three meso- to eutrophic waterbodies and assessed catch rates and sizes of European perch (Perca fluviatilis) caught when offered two soft plastic lures (fluorescent vs. non-fluorescent) with similar reflective spectra. We also examined fluorescent properties of a range of market-available lures and modelled the experimental lure's fluorescing effects under natural lake light. Considering the specific light environment of the study waters, the experimental fluorescent lure could get excited by downwelling visible daylight and fluoresce at depths of up to 3 meters. Based on a sample catch of 331 perch, and after controlling for interactions with illuminance, cloud cover, water depth and daytime, the fluorescence of the experimental lure did, however, neither affect the catch rate nor the size of perch caught. Lure fluorescence maybe less important than many anglers believe, but further studies in different lake conditions are needed.}, language = {en} } @article{DickersonAndreasNoessleretal.2026, author = {Dickerson, Ashton L. and Andreas, Jechow and N{\"o}ßler, Michelle and Walles, Tim J. and Berger, Stella A. and H{\"o}lker, Franz and Nejstgaard, Jens C.}, title = {High-resolution in situ imaging reveals size-specific moonlight responses in zooplankton diel vertical migration}, series = {Scientific Reports}, volume = {16}, journal = {Scientific Reports}, number = {1}, publisher = {Nature}, doi = {10.1038/s41598-026-36105-0}, pages = {1 -- 14}, year = {2026}, abstract = {Light is the primary cue driving zooplankton diel vertical migration (DVM), a strategy that balances predation risk with resource access. However, DVM is often oversimplified, with limited consideration of how light-driven risks and resource needs vary across taxa and life stages. This simplification is partly due to constraints on collecting high-resolution, size-resolved data —especially at night, when subtle shifts in illumination reshape nocturnal risk landscapes. To overcome these limitations, we deployed a high-resolution in situ modular Deep-focus Plankton Imager and an image-recognition approach to quantify fine scale DVM and body sizes of Cladocerans and Copepods in Lake Stechlin, Germany. Data was collected from day into night and across moonrise and was compared with environmental data from vertical profiling sondes. Typical DVM patterns emerged, with deeper daytime distributions, however, moonlight introduced additional behavioural complexity: larger individuals avoided illuminated layers, likely managing predation risk, while smaller individuals moved into these layers, possibly exploiting foraging opportunities and reduced risk. These light-mediated shifts were further shaped by ecological conditions; copepods tracked food-rich layers regardless of light levels at night, while cladocerans showed light-dependent responses to both temperature and food, such that light caused them to avoid otherwise favourable (warm, food-rich) layers. Our approach provides new insight into how zooplankton navigate nocturnal lightscapes, revealing size- and taxon-specific strategies. By establishing size-dependent responses to natural moonlight, this work provides a crucial baseline for predicting how artificial light at night may restructure zooplankton communities and destabilize freshwater food webs.}, language = {en} } @article{PuzyrevTrittelHarthetal.2025, author = {Puzyrev, Dmitry and Trittel, Torsten and Harth, Kirsten and Sata, Sai Preetham and Stannarius, Ralf and Cruz Hidalgo, Ra{\´u}l}, title = {AI-aided visual data analysis for granular gases: Complex particles, high density, and other challenges}, series = {EPJ Web of Conferences}, volume = {340}, journal = {EPJ Web of Conferences}, editor = {Alam, M. and Das, S.L. and Bose, M. and Murthy, T.G. and Chikkadi, V. and Singh, A. and Luding, S.}, publisher = {EDP Sciences}, doi = {10.1051/epjconf/202534012013}, year = {2025}, abstract = {Microgravity experiments with three-dimensional (3D) granular gases, i.e., ensembles of freemoving macroscopic particles which collide inelastically, produce large amounts of stereo video footage which require processing and analysis. The main steps of data treatment are particle detection, 3D matching and tracking in stereoscopic views, and quantification of ensemble statistical properties such as, e.g. mean kinetic energy or collision processes. Frequent overlapping or clustering of particles and their complicated movement patterns require smart software solutions. In recent years, Artificial Intelligence/Machine Learning (AI/ML) methods were successfully used for analysis of granular systems. We have applied such techniques to the granular gases of rod-like particles and developed a software tool which enables a full cycle of semi-automatic experimental data analysis. The approach is now tested on more complex, non-convex particles, shaped as 3D crosses (hexapods). Another challenge is optical analysis of dense granular gases, where individual particles cannot be tracked. We present a preliminary result of application of an ML method for number density profiles extraction in VIP-Gran experiment with dense ensemble of rod-like particles.}, language = {en} } @article{WehlandCorydonGonzalezTorresetal.2025, author = {Wehland, Markus and Corydon, Thomas J. and Gonz{\´a}lez-Torres, Luis Fernando and Abdelfattah, Fatima and Sahana, Jayashree and Schulz, Herbert and Mushunuri, Ashwini and Burenkova, Hanna and Wuest, Simon L. and Kr{\"u}ger, Marcus and Kraus, Armin and Grimm, Daniela}, title = {New Knowledge About Tissue Engineering Under Microgravity Conditions in Space and on Earth}, series = {International Journal of Molecular Sciences}, volume = {27}, journal = {International Journal of Molecular Sciences}, number = {1}, publisher = {MDPI}, doi = {10.3390/ijms27010341}, year = {2025}, abstract = {Microgravity (µg)-generated three-dimensional (3D) multicellular aggregates can serve as models of tissue and disease development. They are relevant in the fields of cancer and in vitro metastasis or regenerative medicine (tissue engineering). Driven by the 3R concept—replacement, reduction, and refinement of animal testing—µg-exposure of human cells represents a new alternative method that avoids animal experiments entirely. New Approach Methodologies (NAMs) are used in biomedical research, pharmacology, toxicology, cancer research, radiotherapy, and translational regenerative medicine. Various types of human cells grow as 3D spheroids or organoids when exposed to µg-conditions provided by µg simulating instruments on Earth. Examples for such µg-simulators are the Rotating Wall Vessel, the Random Positioning Machine, and the 2D or 3D clinostat. This review summarizes the most recent literature focusing on µg-engineered tissues. We are discussing all reports examining different tumor cell types from breast, lung, thyroid, prostate, and gastrointestinal cancers. Moreover, we are focusing on µg-generated spheroids and organoids derived from healthy cells like chondrocytes, stem cells, bone cells, endothelial cells, and cardiovascular cells. The obtained data from NAMs and µg-experiments clearly imply that they can support translational medicine on Earth.}, language = {en} } @article{SteinPilgermannWeberetal.2025, author = {Stein, Stefan and Pilgermann, Michael and Weber, Simon and Sedlmayr, Martin}, title = {Leveraging MDS2 and SBOM data for LLM-assisted vulnerability analysis of medical devices}, series = {Computational and Structural Biotechnology Journal}, volume = {28}, journal = {Computational and Structural Biotechnology Journal}, publisher = {Elsevier}, doi = {10.1016/j.csbj.2025.07.012}, pages = {267 -- 280}, year = {2025}, abstract = {This study investigated the use of a semi-automated, Retrieval-Augmented Generation (RAG)-based multi-agent architecture to analyze security-relevant data and assemble specialized exploitation paths targeting medical devices. The input dataset comprised device-specific sources, namely, the Manufacturer Disclosure Statement for Medical Device Security (MDS2) documents and Software Bills of Materials (SBOMs), enriched with public vulnerability databases, including Common Vulnerabilities and Exposures (CVE), Known Exploited Vulnerabilities (KEV), and Metasploit exploit records. The objective was to assess whether a modular, Large Language Model (LLM)-driven agent system could autonomously correlate device metadata with known vulnerabilities and existing exploit information to support structured threat modeling. The architecture follows a static RAG design based on predefined prompts and fixed retrieval logic, without autonomous agent planning or dynamic query adaptation. The developed Vulnerability Intelligence for Threat Analysis in Medical Security (VITAMedSec) system operates under human-prompted supervision and successfully synthesizes actionable insights and exploitation paths without requiring manual step-by-step input during execution. Although technically coherent results were obtained under controlled conditions, real-world validation remains a critical avenue for future research. This study further discusses the dual-use implications of such an agent-based framework, its relevance to patient safety in medical device cybersecurity, and the broader applicability of the proposed architecture to other critical infrastructure sectors. These findings emphasize both the technical potential and ethical responsibility for applying semi-automated AI workflows in medical cybersecurity contexts.}, language = {en} } @article{JamilMihajloskaTrpcheskaPopovskaMitrovikjetal.2025, author = {Jamil, Mahnoor and Mihajloska Trpcheska, Hristina and Popovska-Mitrovikj, Aleksandra and Dimitrova, Vesna and Creutzburg, Reiner}, title = {Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis}, series = {Applied Sciences}, volume = {15}, journal = {Applied Sciences}, number = {11}, publisher = {MDPI}, issn = {2076-3417}, doi = {10.3390/app15116158}, year = {2025}, abstract = {Image-based spam poses a significant challenge for traditional text-based filters, as malicious content is often embedded within images to bypass keyword detection techniques. This study investigates and compares the performance of six machine learning models—ResNet50, XGBoost, Logistic Regression, LightGBM, Support Vector Machine (SVM), and VGG16—using a curated dataset containing 678 legitimate (ham) and 520 spam images. The novelty of this research lies in its comprehensive side-by-side evaluation of diverse models on the same dataset, using standardized dataset preprocessing, balanced data splits, and validation techniques. Model performance was assessed using evaluation metrics such as accuracy, receiver operating characteristic (ROC) curve, precision, recall, and area under the curve (AUC). The results indicate that ResNet50 achieved the highest classification performance, followed closely by XGBoost and Logistic Regression. This work provides practical insights into the strengths and limitations of traditional, ensemble-based, and deep learning models for image-based spam detection. The findings can support the development of more effective and generalizable spam filtering solutions in multimedia-rich communication platforms.}, language = {en} } @article{FonvielleBistarelliTaoetal.2025, author = {Fonvielle, Jeremy and Bistarelli, Lukas Thuile and Tao, Yile and Woodhouse, Jason N. and Shatwell, Tom and Villalba, Luis A. and Berger, Stella A. and Kyba, Christopher C. M. and Nejstgaard, Jens C. and Jechow, Andreas and Kupprat, Franziska and Stephan, Susanne and Walles, Tim J. W. and Wollrab, Sabine and Hoelker, Franz and Dittmar, Thorsten and Gessner, Mark O. and Singer, Gabriel A. and Grossart, Hans-Peter}, title = {Skyglow increases cyanobacteria abundance and organic matter cycling in lakes}, series = {Water Research}, journal = {Water Research}, publisher = {Elsevier}, doi = {10.1016/j.watres.2025.123315}, pages = {1 -- 11}, year = {2025}, abstract = {Artificial light propagating towards the night sky can be scattered back to Earth and reach ecosystems tens of kilometres away from the original light source. This phenomenon is known as artificial skyglow. Its consequences on freshwaters are largely unknown. In a large-scale lake enclosure experiment, we found that skyglow at levels of 0.06 and 6 lux increased the abundance of anoxygenic aerobic phototrophs and cyanobacteria by 32 (+/- 22) times. An ecosystem metabolome analysis revealed that skyglow increased the production of algal-derived metabolites, which appeared to stimulate heterotrophic activities as well. Furthermore, we found evidence that skyglow decreased the number of bacteria-bacteria interactions. Effects of skyglow were more pronounced at night, suggesting that responses to skyglow can occur on short time scales. Overall, our results call for considering skyglow as a reality of increasing importance for microbial communities and carbon cycling in lake ecosystems.}, language = {en} } @article{DietenbergerJechowKalinkatetal.2024, author = {Dietenberger, Manuel and Jechow, Andreas and Kalinkat, Gregor and Schroer, Sibylle and Saathoff, Birte and H{\"o}lker, Franz}, title = {Reducing the fatal attraction of nocturnal insects using tailored and shielded road lights}, series = {communications biology}, volume = {7}, journal = {communications biology}, publisher = {Nature}, doi = {10.1038/s42003-024-06304-4}, pages = {1 -- 12}, year = {2024}, abstract = {The attraction of insects to artificial light is a global environmental problem with far-reaching implications for ecosystems. Since light pollution is rarely integrated into conservation approaches, effective mitigation strategies towards environmentally friendly lighting that drastically reduce insect attraction are urgently needed. Here, we tested novel luminaires in two experiments (i) at a controlled experimental field site and (ii) on streets within three municipalities. The luminaires are individually tailored to only emit light onto the target area and to reduce spill light. In addition, a customized shielding renders the light source nearly invisible beyond the lit area. We show that these novel luminaires significantly reduce the attraction effect on flying insects compared to different conventional luminaires with the same illuminance on the ground. This underlines the huge potential of spatially optimized lighting to help to bend the curve of global insect decline without compromising human safety aspects. A customized light distribution should therefore be part of sustainable future lighting concepts, most relevant in the vicinity of protected areas.}, language = {en} } @article{OrlowskiWagnerl'Orteyeetal.2017, author = {Orlowski, Katja and Wagner, Dennis and l'Orteye, Annett and Ringk, Heike and Kohl, Verena and Schrader, Thomas}, title = {Analysing the activation of the plantar flexor muscles during treadmill walking}, series = {Gait \& Posture}, volume = {57}, journal = {Gait \& Posture}, number = {Supplement 1}, publisher = {Elsevier}, issn = {0966-6362}, doi = {10.1016/j.gaitpost.2017.06.377}, pages = {213 -- 214}, year = {2017}, language = {en} } @article{SteinWeberPilgermannetal.2024, author = {Stein, Stefan and Weber, Simon and Pilgermann, Michael and Schrader, Thomas and Sedlmayr, Martin}, title = {A Novel Approach to Medical Device IT Security Landscape Analysis Leveraging Manufacturer Disclosure Statements}, series = {IEEE Access}, journal = {IEEE Access}, number = {12}, publisher = {IEEE}, issn = {2169-3536}, doi = {10.1109/ACCESS.2024.3487824}, pages = {160506 -- 160515}, year = {2024}, abstract = {The growing number of cyberattacks targeting the healthcare sector increasingly threatens network-enabled medical devices that are vital for life-sustaining patient care. Security researchers and healthcare IT managers are pursuing effective methods to assess the IT security landscape of medical devices. Their goal is to develop a comprehensive understanding of the devices' IT security status. Recent studies have successfully uncovered structural deficiencies in medical device security. However, the limitations of their data sources, particularly in evaluating features like logging capabilities and third-party libraries, restrict the scope of their findings. In this study, we present the first systematic analysis of Manufacturer Disclosure Statement for Medical Device Security (MDS2) documents to evaluate their use in creating holistic statements regarding the IT security posture of medical devices. We examined a total of 147 MDS2 documents encompassing devices from 105 different classes. Our findings indicate that MDS2 documents, especially those from the second version (2013) onwards, are suitable for this purpose. We also discuss the shortcomings of the latest MDS2 version in meeting current IT security requirements. Based on the gaps identified, we developed several recommendations to improve MDS2 documents and enhance their effectiveness across the global healthcare sector. In the future, these documents could be used not only for comprehensive landscape analyses but also for organization-specific reports, providing healthcare managers with direct insights into the IT security status within their institutions.}, language = {en} }