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  1. Abstract Although the seed is a key morphological innovation, its origin remains unknown and molecular data outside angiosperms is still limited. Ginkgo biloba, with a unique place in plant evolution, being one of the first extant gymnosperms where seeds evolved, can testify to the evolution and development of the seed. Initially, to better understand the development of the ovules in Ginkgo biloba ovules, we performed spatio-temporal expression analyses in seeds at early developing stages, of six candidate gene homologues known in angiosperms: WUSCHEL, AINTEGUMENTA, BELL1, KANADI, UNICORN, and C3HDZip . Surprisingly, the expression patterns of most these ovule homologues indicate that they are not wholly conserved between angiosperms and Ginkgo biloba . Consistent with previous studies on early diverging seedless plant lineages, ferns, lycophytes, and bryophytes, many of these candidate genes are mainly expressed in mega- and micro-sporangia. Through in-depth comparative transcriptome analyses of Ginkgo biloba developing ovules, pollen cones, and megagametophytes we have been able to identify novel genes, likely involved in ovule development. Finally, our expression analyses support the synangial or neo-synangial hypotheses for the origin of the seed, where the sporangium developmental network was likely co-opted and restricted during integument evolution. 
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  2. Purugganan, Michael (Ed.)
    Abstract The field of evolutionary developmental biology can help address how morphological novelties evolve, a key question in evolutionary biology. In Arabidopsis thaliana, APETALA2 (AP2) plays a role in the development of key plant innovations including seeds, flowers, and fruits. AP2 belongs to the AP2/ETHYLENE RESPONSIVE ELEMENT BINDING FACTOR family which has members in all viridiplantae, making it one of the oldest and most diverse gene lineages. One key subclade, present across vascular plants is the euAPETALA2 (euAP2) clade, whose founding member is AP2. We reconstructed the evolution of the euAP2 gene lineage in vascular plants to better understand its impact on the morphological evolution of plants, identifying seven major duplication events. We also performed spatiotemporal expression analyses of euAP2/TOE3 genes focusing on less explored vascular plant lineages, including ferns, gymnosperms, early diverging angiosperms and early diverging eudicots. Altogether, our data suggest that euAP2 genes originally contributed to spore and sporangium development, and were subsequently recruited to ovule, fruit and floral organ development. Finally, euAP2 protein sequences are highly conserved; therefore, changes in the role of euAP2 homologs during development are most likely due to changes in regulatory regions. 
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  3. Herbarium sheets present a unique view of the world's botanical history, evolution, and biodiversity. This makes them an all–important data source for botanical research. With the increased digitization of herbaria worldwide and advances in the domain of fine–grained visual classification which can facilitate automatic identification of herbarium specimen images, there are many opportunities for supporting and expanding research in this field. However, existing datasets are either too small, or not diverse enough, in terms of represented taxa, geographic distribution, and imaging protocols. Furthermore, aggregating datasets is difficult as taxa are recognized under a multitude of names and must be aligned to a common reference. We introduce the Herbarium 2021 Half–Earth dataset: the largest and most diverse dataset of herbarium specimen images, to date, for automatic taxon recognition. We also present the results of the Herbarium 2021 Half–Earth challenge, a competition that was part of the Eighth Workshop on Fine-Grained Visual Categorization (FGVC8) and hosted by Kaggle to encourage the development of models to automatically identify taxa from herbarium sheet images. 
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  4. Abstract The large size and complexity of most fern genomes have hampered efforts to elucidate fundamental aspects of fern biology and land plant evolution through genome-enabled research. Here we present a chromosomal genome assembly and associated methylome, transcriptome and metabolome analyses for the model fern species Ceratopteris richardii . The assembly reveals a history of remarkably dynamic genome evolution including rapid changes in genome content and structure following the most recent whole-genome duplication approximately 60 million years ago. These changes include massive gene loss, rampant tandem duplications and multiple horizontal gene transfers from bacteria, contributing to the diversification of defence-related gene families. The insertion of transposable elements into introns has led to the large size of the Ceratopteris genome and to exceptionally long genes relative to other plants. Gene family analyses indicate that genes directing seed development were co-opted from those controlling the development of fern sporangia, providing insights into seed plant evolution. Our findings and annotated genome assembly extend the utility of Ceratopteris as a model for investigating and teaching plant biology. 
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  5. Premise

    Plant biodiversity is threatened, yet many species remain undescribed. It is estimated that >50% of undescribed species have already been collected and are awaiting discovery in herbaria. Robust automatic species identification algorithms using machine learning could accelerate species discovery.

    Methods

    To encourage the development of an automatic species identification algorithm, we submitted our Herbarium 2019 data set to the Fine‐Grained Visual Categorization sub‐competition (FGVC6) hosted on the Kaggle platform. We chose to focus on the flowering plant family Melastomataceae because we have a large collection of imaged herbarium specimens (46,469 specimens representing 683 species) and taxonomic expertise in the family. As is common for herbarium collections, some species in this data set are represented by few specimens and others by many.

    Results

    In less than three months, the FGVC6 Herbarium 2019 Challenge drew 22 teams who entered 254 models for Melastomataceae species identification. The four best algorithms identified species with >88% accuracy.

    Discussion

    The FGVC competitions provide a unique opportunity for computer vision and machine learning experts to address difficult species‐recognition problems. The Herbarium 2019 Challenge brought together a novel combination of collections resources, taxonomic expertise, and collaboration between botanists and computer scientists.

     
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