Switching it up: algal insights into sexual transitions

While the process of meiosis is highly conserved across eukaryotes, the sexual systems that govern life cycle phase transitions are surprisingly labile. Switches between sexual systems have profound evolutionary and ecological consequences, in particular for plants, but our understanding of the fundamental mechanisms and ultimate causes underlying these transitions is still surprisingly incomplete. We explore here the idea that brown and green algae may be interesting comparative models that can increase our understanding of relevant processes in plant reproductive biology, from evolution of gamete dimorphism, gametogenesis, sex determination and transitions in sex-determining systems.

Proceedings of the 7th Series of Seminars on Advances in Apomixis Research

These proceedings contain the abstracts for the presentations given at the 7th biennial Seminars on Advances in Apomixis Research, held virtually on 2–3 and 9 December 2020. The first day hosted the kick-off meeting of the EU-funded Mechanisms of Apomictic Development (MAD) project, while the remaining days were dedicated to oral presentations and in-depth exchanges on the latest progress in the field of apomixis and plant reproductive biology research.

Deep learning-based high-throughput phenotyping can drive future discoveries in plant reproductive biology

Key message Advances in deep learning are providing a powerful set of image analysis tools that are readily accessible for high-throughput phenotyping applications in plant reproductive biology. High-throughput phenotyping systems are becoming critical for answering biological questions on a large scale. These systems have historically relied on traditional computer vision techniques. However, neural networks and specifically deep learning are rapidly becoming more powerful and easier to implement. Here, we examine how deep learning can drive phenotyping systems and be used to answer fundamental questions in reproductive biology. We describe previous applications of deep learning in the plant sciences, provide general recommendations for applying these methods to the study of plant reproduction, and present a case study in maize ear phenotyping. Finally, we highlight several examples where deep learning has enabled research that was previously out of reach and discuss the future outlook of these methods.