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Winter School & Symposium 2025

Save the date for the upcoming PSC Winter School & Symposium 2025: Harnessing Machine Learning for Breakthroughs in Plant and Environmental Sciences (2 ECTS)

Lecturers: A lineup of national and international experts

Location: ETH Zürich Center, tbd

Dates: 10-14th March, 2025

Flyer (PDF, 498 KB)

Exploring Generative AI for Responsible Scientific Work

Generative AI emerges as a powerful tool - one that PhD students can harness to unlock new possibilities in their scientific work. Through practical exploration guided by our expert lecturers and tutors, attendees will discover how generative AI can create texts, images, and audio - each with applications spanning diverse scientific contexts, from extracting data and data analysis to scientific illustrations and scholarly publications. As we traverse this transformative landscape, ethics and privacy become paramount. This course goes beyond mere technical proficiency; it emphasizes responsible and ethical usage of Large Language Models (LLMs) embedded in scientific workflows.

Join us on this profound journey - one where generative AI meets ethics, and innovation meets responsibility.

Registration and detailed course description: Limited places available.

Location: ETH Zurich, tba

Dates: 12.11.2024 - 19.11.2024 (4days)

Do you have specific questions related to the use of generative AI for scientific purposes? Are you currently customizing GPTs to facilitate your own workflows? We’d love to hear from you! Contact:Bojan Gujas and get involved in creating a course that will benefit our community.

Systems Thinking and Design for Social Change and in Policymaking

Get to know the transformative potential of systems thinking and relevant tools and methods in policymaking. Society, scientists, and policymakers have to deal with wicked problems and complex societal needs that can be assessed and solved only if seen in a systems context. Systems thinking can inform, model and impact policymaking. This workshop will offer theoretical and practical insights into a toolbox of techniques used in system mapping and system design while being guided by different experts. Participants are asked to bring their own problems and cases to be worked on.

Lecturers: Melanie Paschke (ETHZ), Tobias Luthe (ETHZ), Laura Ferrarello (EPFL), Anaïs Sägesser (scaling4good & innosuisse), Swen Bos, Empa

Location: ETH Zurich

Dates: 20.01.2025 - 22.01.2025, 3 days (9:00-17:00)

Detailed course description (PDF, 333 KB) and registration: here

Contact: Luisa Last

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Science Highlights

Defective pollen tube tip growth induces neo-polyploid infertility

Westermann, J., et al., Science, Vol 383, Issue 6686 (2024)

Genome duplication in Arabidopsis arenosa causes low fertility due to pollen tube defects like slow growth and premature bursting. These issues improve in evolved polyploids, with specific genes linked to better performance, highlighting pollen tube growth as a key fertility challenge.

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Escarpment evolution drives diversification of the Madagascar flora

Yi Liu, Y.W., et al., Science, Vol 383, Issue 6686 (2024)

Madagascar's rich endemic biodiversity evolved over millions of years, especially along the eastern escarpment. Precipitation-driven erosion and topographic changes create transient habitats, isolating species and driving allopatric speciation, contributing to its biodiversity.

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Soil health associated with higher primary productivity across Europe

Romero, F., et al., Nature Ecology & Evolution (2024)

This pan-European study of 588 sites shows that soil health, higher in woodlands than in grasslands and croplands, is positively linked to productivity in croplands and grasslands. Soil biodiversity, particularly nitrogen-fixing bacteria and mycorrhizal fungi, is crucial for maintaining productivity.

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A paternal signal induces endosperm proliferation ...

Simonini, S., et al., Science, Vol 383, Issue 6683 (2024)

Fertilization in multicellular organisms resumes the gametes' cell cycle, requiring both to be in the same phase. In Arabidopsis, a sperm signal triggers female central cell proliferation by degrading the RBR1 protein, allowing S phase progression, crucial for endosperm and seed development.

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Multispecies deep learning using citizen science data produces more ..

Brun, P., et al., Nature Communications 15, 4421 (2024)

Using deep neural networks (DNNs) and citizen science data, researchers modeled the spatiotemporal distributions of 2477 plant species inSwitzerland. Multispecies DNNs outperformed traditional methods in predicting distributions and community composition, offering insights into phenology and future changes.

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Weiterführende Informationen

Paschke, Melanie, with contributions by: Petterson, Alexander, Mihalka, Réka and Manuel Sudau (2024). Teaching Collection: Exercises and hands-on examples for ethical use of generative AI. Zurich Basel Plant Science Center: ETH Zurich.

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