Sweet osmanthus (Osmanthus fragrans) is a very popular ornamental tree species throughout Southeast Asia and USA particularly for its extremely fragrant aroma. We constructed a chromosome-level reference genome of O. fragrans to assist in studies of the evolution, genetic diversity, and molecular mechanism of aroma development. A total of over 118 Gb of polished reads was produced from HiSeq (45.1 Gb) and PacBio Sequel (73.35 Gb), giving 100× depth coverage for long reads. The combination of Illumina-short reads, PacBio-long reads, and Hi-C data produced the final chromosome quality genome of O. fragrans with a genome size of 727 Mb and a heterozygosity of 1.45 %. The genome was annotated using de novo and homology comparison and further refined with transcriptome data. The genome of O. fragrans was predicted to have 45,542 genes, of which 95.68 % were functionally annotated. Genome annotation found 49.35 % as the repetitive sequences, with long terminal repeats (LTR) being the richest (28.94 %). Genome evolution analysis indicated the evidence of whole-genome duplication 15 million years ago, which contributed to the current content of 45,242 genes. Metabolic analysis revealed that linalool, a monoterpene is the main aroma compound. Based on the genome and transcriptome, we further demonstrated the direct connection between terpene synthases (TPSs) and the rich aromatic molecules in O. fragrans. We identified three new flower-specific TPS genes, of which the expression coincided with the production of linalool. Our results suggest that the high number of TPS genes and the flower tissue- and stage-specific TPS genes expressions might drive the strong unique aroma production of O. fragrans.

Exploiting the spatial structure in scene images is a key research direction for scene recognition. Due to the large intra-class structural diversity, building and modeling flexible structural layout to adapt various image characteristics is a challenge. Existing structural modeling methods in scene recognition either focus on predefined grids or rely on learned prototypes, which all have limited representative ability. In this paper, we propose Prototype-agnostic Scene Layout (PaSL) construction method to build the spatial structure for each image without conforming to any prototype. Our PaSL can flexibly capture the diverse spatial characteristic of scene images and have considerable generalization capability. Given a PaSL, we build Layout Graph Network (LGN) where regions in PaSL are defined as nodes and two kinds of independent relations between regions are encoded as edges. The LGN aims to incorporate two topological structures (formed in spatial and semantic similarity dimensions) into image representations through graph convolution. Extensive experiments show that our approach achieves state-of-the-art results on widely recognized MIT67 and SUN397 datasets without multi-model or multi-scale fusion. Moreover, we also conduct the experiments on one of the largest scale datasets, Places365. The results demonstrate the proposed method can be well generalized and obtains competitive performance.

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