DDG1 and G Protein α Subunit RGA1 Interaction Regulates Plant Height and Senescence in Rice(Oryza sativa)

Many studies have already shown that dwarfism and moderate delayed leaf senescence positively impact rice yield,but the underlying molecular mechanism of dwarfism and leaf senescence remains largely unknown.Here,using map-based cloning,we identified an allele of DEP2,DDG1,which controls plant height and leaf senescence in rice.The ddg1 mutant displayed dwarfism,short panicles,and delayed leaf senescence.Compared with the wild-type,ddg1 was insensitive to exogenous gibberellins(GA)and brassinolide(BR).DDG1 is expressed in various organs,especially in stems and panicles.Yeast two-hybrid assay,bimolecular fluorescent complementation and luciferase complementation image assay showed that DDG1 interacts with theα-subunit of the heterotrimeric G protein.Disruption of RGA1 resulted in dwarfism,short panicles,and darker-green leaves.Furthermore,we found that ddg1 and the RGA1 mutant was more sensitive to salt treatment,suggesting that DDG1 and RGA1 are involved in regulating salt stress response in rice.Our results show that DDG1/DEP2 regulates plant height and leaf senescence through interacting with RGA1.

USEVis:Visual analytics of attention-based neural embedding in information retrieval

Neural attention-based encoders,which effectively attend sentence tokens to their associated context without being restricted by long-term distance or dependency,have demonstrated outstanding performance in embedding sentences into meaningful representations(embeddings).The Universal Sentence Encoder(USE)is one of the most well-recognized deep neural network(DNN)based solutions,which is facilitated with an attention-driven transformer architecture and has been pre-trained on a large number of sentences from the Internet.Besides the fact that USE has been widely used in many downstream applications,including information retrieval(IR),interpreting its complicated internal working mechanism remains challenging.In this work,we present a visual analytics solution towards addressing this challenge.Specifically,focused on semantics and syntactics(concepts and relations)that are critical to domain clinical IR,we designed and developed a visual analytics system,i.e.,USEVis.The system investigates the power of USE in effectively extracting sentences’semantics and syntactics through exploring and interpreting how linguistic properties are captured by attentions.Furthermore,by thoroughly examining and comparing the inherent patterns of these attentions,we are able to exploit attentions to retrieve sentences/documents that have similar semantics or are closely related to a given clinical problem in IR.By collaborating with domain experts,we demonstrate use cases with inspiring findings to validate the contribution of our work and the effectiveness of our system.