Dynamic Phytohormone Trajectories Reveal Predictive Markers of Ozone Exposure Through Longitudinal Sampling and Machine Learning
Summary statement Longitudinal sap measurements of Solanum Lycopersicum combined with machine learning reveal tZR as a predictive marker for elevated ozone levels.
Authors
- Jaime Sebastian‐Azcona (ORCID: https://orcid.org/0000-0003-2819-1825)
- Niclas Roxhed (ORCID: https://orcid.org/0000-0002-7147-6730)
- Heiko Hamann (ORCID: https://orcid.org/0000-0002-2458-8289)
- Virginia Hernández‐Santana (ORCID: https://orcid.org/0000-0001-9018-8622)
- Till Aust (ORCID: https://orcid.org/0000-0003-2863-1341)
- Antonio Díaz‐Espejo (ORCID: https://orcid.org/0000-0002-4711-2494)
- Göran Stemme (ORCID: https://orcid.org/0000-0001-9552-4234)
- Ellinor Hedberg (ORCID: https://orcid.org/0009-0003-0031-5117)
Institutions
- Karolinska University Hospital (SE)
- University of Konstanz (DE)
- Instituto de Recursos Naturales y Agrobiología de Sevilla (ES)
- KTH Royal Institute of Technology (SE)
Publication Details
- Journal
- Plant Cell & Environment
- Published
- 2026-10-08
- DOI
- https://doi.org/10.1111/pce.70967
- Primary Topic
- Plant responses to elevated CO2
- Type
- article
- Field-Weighted Citation Impact
- 0.00