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An unwelcoming climate and culture at scientific conferences is an obstacle to retaining scientists with marginalized identities. Here we describe how a number of professional societies in the plant sciences, mostly based in the United States, collaborated on a project called ROOT & SHOOT (short for Rooting Out Oppression Together and SHaring Our Outcomes Transparently) to make conferences in the field more inclusive. The guidelines we developed, and our efforts to implement them in 2023 and 2024, are summarized here to assist other conference organizers with creating more inclusive conferences. 

Abstract BackgroundThe La-related proteins (LARPs) are a superfamily of RNA-binding proteins associated with regulation of gene expression. Evidence points to an important role for post-transcriptional control of gene expression in germinating pollen tubes, which could be aided by RNA-binding proteins. ResultsIn this study, a genome-wide investigation of the LARP proteins in eight plant species was performed. The LARP proteins were classified into three families based on a phylogenetic analysis. The gene structure, conserved motifs,cis-acting elements in the promoter, and gene expression profiles were investigated to provide a comprehensive overview of the evolutionary history and potential functions ofZmLARPgenes in maize. Moreover,ZmLARP6c1was specifically expressed in pollen and ZmLARP6c1 was localized to the nucleus and cytoplasm in maize protoplasts. Overexpression ofZmLARP6c1enhanced the percentage pollen germination compared with that of wild-type pollen. In addition, transcriptome profiling analysis revealed that differentially expressed genes includedPABPhomologous genes and genes involved in jasmonic acid and abscisic acid biosynthesis, metabolism, signaling pathways and response in aZmlarp6c1::Dsmutant andZmLARP6c1-overexpression line compared with the corresponding wild type. ConclusionsThe findings provide a basis for further evolutionary and functional analyses, and provide insight into the critical regulatory function ofZmLARP6c1in maize pollen germination. 

Abstract 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. 

SUMMARY High‐throughput phenotyping systems are powerful, dramatically changing our ability to document, measure, and detect biological phenomena. Here, we describe a cost‐effective combination of a custom‐built imaging platform and deep‐learning‐based computer vision pipeline. A minimal version of the maize (Zea mays) ear scanner was built with low‐cost and readily available parts. The scanner rotates a maize ear while a digital camera captures a video of the surface of the ear, which is then digitally flattened into a two‐dimensional projection. Segregating GFP and anthocyanin kernel phenotypes are clearly distinguishable in ear projections and can be manually annotated and analyzed using image analysis software. Increased throughput was attained by designing and implementing an automated kernel counting system using transfer learning and a deep learning object detection model. The computer vision model was able to rapidly assess over 390 000 kernels, identifying male‐specific transmission defects across a wide range of GFP‐marked mutant alleles. This includes a previously undescribed defect putatively associated with mutation of Zm00001d002824, a gene predicted to encode a vacuolar processing enzyme. Thus, by using this system, the quantification of transmission data and other ear and kernel phenotypes can be accelerated and scaled to generate large datasets for robust analyses.