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Abstract Vernalization-responsive plants use cold weather, or low temperature, as a cue to monitoring the passing of winter. Winter cereals can remember the extent of coldness they have experienced, even when winter is punctuated by warm days. However, in a seemingly unnatural process called “devernalization,” hot temperatures can erase winter memory. Previous studies in bread wheat (Triticum aestivum) have implicated the MADS-box transcription factor VEGETATIVE TO REPRODUCTIVE TRANSITION 2 (VRT2) in vernalization based on transcriptional behavior and ectopic expression. Here, we characterized 3 BdVRT2 loss-of-function alleles in the temperate model grass Brachypodium distachyon. In addition to extended vernalization requirements, mutants showed delayed flowering relative to wild-type plants when exposed only briefly to warm temperatures after partial vernalization, with flowering being unaffected when vernalization was saturating. Together, these data suggest a role for BdVRT2 in both vernalization and in its reinitiation when interrupted by warm temperatures. In controlled constant conditions, BdVRT2 transcription was not strongly affected by vernalization or devernalization. Yet, by monitoring BdVRT2 expression in seasonally varying and fluctuating conditions in an unheated greenhouse, we observed strong upregulation, suggesting that its transcription is regulated by fluctuating vernalizing–devernalizing conditions. Our data suggest that devernalization by hot temperatures is not a peculiarity of domesticated cereal crops but is the extreme of the reversibility of vernalization by warm temperatures and has broader biological relevance across temperate grasses. 

Spatial non wide-sense stationarities cause partial visibility regions (VRs), and it is a unique propagation characteristic of emerging extra-large aperture arrays (ELAAs). Thus, classification of VRs is a necessity for accurate estimation of channels and efficient design of VR-aware precoders for ELAAs. In this paper, a deep learning framework is proposed to classify VRs in ELAAs. Our objective is to boost the accuracy of classifying VRs based on the uplink pilots received at the ELAAs. Consequently, we focus on guaranteeing user-fairness in the presence of wholly/partial VRs and improving the achievable rates by adopting VR-aware channel estimation and precoding. We propose a hybrid deep learning architecture comprising one dimensional convolutional neural networks and long-short term memory to classify VRs of each user at the ELAA. To achieve a higher accuracy, we generate a diverse dataset through Monte-Carlo simulations that captures numerous combinations of VRs at the ELAA. A transmit power allocation algorithm is also proposed to achieve a common downlink rate for all users irrespective of the different VRs, and its computational complexity is discussed. A set of numerical results is presented to evaluate the performance of our proposed framework. It is efficient and accurate in classifying VRs. Thus, it can be used to enhance the estimation accuracy of ELAA channels with VRs and thereby to design VR-aware precoders to boost spectral/energy efficiency of the next-generation wireless systems.