In this study, the complete mitochondrial genome of the medicinal mushroom
Hericium coralloides
(Hericiaceae, Basidiomycota) was sequenced. This mitochondrial genome is 72,961 bp in length and consisted of 14 protein-coding genes, 21 hypothetical open reading frames, 2 ribosomal RNA subunits and 27 transfer RNAs. The overall nucleotide composition of is 41.33% A, 40.71% T, 9.06% C and 8.90% G, with GC content of 17.96%. A phylogenetic tree with the complete mitochondrial genome sequences of
Hericium coralloides
together with 9 other affinis mushrooms was constructed. The newly achieved mitochondrial genome sequence seem to be useful for addressing taxonomic issues and studying related evolution events, which would contribute to enrich the fungal mitochondrial genome resource and promote the biological research.
Recently, context-dependent text-to-SQL semantic parsing which translates natural language into SQL in an interaction process has attracted a lot of attention. Previous works leverage context-dependence information either from interaction history utterances or the previous predicted SQL queries but fail in taking advantage of both since of the mismatch between natural language and logicform SQL. In this work, we propose a History Information Enhanced text-to-SQL model (HIE-SQL) to exploit context-dependence information from both history utterances and the last predicted SQL query. In view of the mismatch, we treat natural language and SQL as two modalities and propose a bimodal pretrained model to bridge the gap between them. Besides, we design a schema-linking graph to enhance connections from utterances and the SQL query to the database schema. We show our history information enhanced methods improve the performance of HIE-SQL by a significant margin, which achieves new state-of-theart results on the two context-dependent textto-SQL benchmarks, the SparC and CoSQL datasets, at the writing time.
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