Faculty of Biological Sciences: Research Data
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- Research DataTwo distinct modes of Vgll4-mediated Tead regulation control organ size in zebrafish - SourceData2026-03-19Control of organ size during development and homeostasis relies on balanced regulation of Hippo pathway transcriptional output, yet how TEAD activity is precisely regulated in vivo remains unclear. Using the zebrafish posterior lateral line (pLL) we show that Yap1 is required early in pLL progenitors to ensure sufficient cell numbers in the migrating primordium. In contrast, the two zebrafish Vgll4 paralogs, Vgll4b and Vgll4l, act partially redundantly to limit pLLP size and cell number. Through loss- and gain-of-function analyses, epistasis experiments, transcriptional reporter quantification and pharmacological treatments, we find that Vgll4 restricts Tead-dependent transcription through two co-existing mechanisms: inhibition of Yap1–Tead–mediated transcriptional activation and Tead-dependent repression. Together, our findings reconcile the competitive and default repression models of VGLL4 function and provide an integrated framework for how VGLL4 fine-tunes TEAD output to control tissue growth in vivo.
58 8 - Research DataAI-based Monitoring of European Hamster Activity2026-02-03In order to ensure the effective conservation of the critically endangered European hamster (Cricetus cricetus), there is a necessity for the implementation of targeted conservation measures and reliable monitoring methods. This study explores the potential of employing artificial intelligence (AI) to assist with camera trap monitoring for the purpose of tracking hamster activity. To this end, a deep learning object detection model (YOLO) was trained to efficiently analyze large volumes of video data from summer 2023 with high reliability. The model achieved a weighted average F1-score of 0.93 and an accuracy of 0.93 for the detection of European hamsters, effectively differentiating them from other species. A comparison between AI-based and human evaluations confirmed that AI can reliably depict hamster activity patterns. The findings of this study suggest that European hamsters exhibit peak activity levels at dusk, with the highest peak in activity occurring around sunset. In contrast, activity levels were lowest around midday. Autocorrelation analysis revealed a biphasic activity pattern, with a secondary peak occurring approximately before sunrise. This study underscores the potential of employing artificial intelligence for long-term conservation efforts and its applicability in assessing the success of reintroduction programs.
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