Genome Sequences Provide Insights into the Reticulate Origin and Unique Traits of Woody Bamboos

Polyploidization is a major driver of speciation and its importance to plant evolution has been well recognized.Bamboos comprise one diploid herbaceous and three polyploid woody lineages,and are members of the only major subfamily in grasses that diversified in forests,with the woody members having a tree-like lignified culm.In this study,we generated four draft genome assemblies of major bamboo lineages with three different ploidy levels(diploid,tetraploid,and hexaploid).We also constructed a high-density genetic linkage map for a hexaploid species of bamboo,and used a linkage-map-based strategy for genome assembly and identification of subgenomes in polyploids.Further phylogenomic analyses using a large dataset of syntenic genes with expected copies based on ploidy levels revealed that woody bamboos originated subsequent to the divergence of the herbaceous bamboo lineage,and experienced complex reticulate evolution through three independent allopolyploid events involving four extinct diploid ancestors.A shared but distinct subgenome was identified in all polyploid forms,and the progenitor of this subgenome could have been critical in ancient polyploidizations and the origin of woody bamboos.Important genetic clues to the unique flowering behavior and woody trait in bamboos were also found.Taken together,our study provides significant insights into ancient reticulate evolution at the subgenome level in the absence of extant donor species,and offers a potential model scenario for broad-scale study of angiosperm origination by allopolyploidization.

Layer-Wise Pre-Training Low-Rank NMF Model for Mammogram-Based Breast Tumor Classification

Image-based breast tumor classification is an active and challenging problem.In this paper,a robust breast tumor classification framework is presented based on deep feature representation learning and exploiting available information in existing samples.Feature representation learning of mammograms is fulfilled by a modified nonnegative matrix factorization model called LPML-LRNMF,which is motivated by hierarchical learning and layer-wise pre-training(LP)strategy in deep learning.Low-rank(LR)constraint is integrated into the feature representation learning model by considering the intrinsic characteristics of mammograms.Moreover,the proposed LPML-LRNMF model is optimized via alternating direction method of multipliers and the corresponding convergence is analyzed.For completing classification,an inverse projection sparse representation model is introduced to exploit information embedded in existing samples,especially in test ones.Experiments on the public dataset and actual clinical dataset show that the classification accuracy,specificity and sensitivity achieve the clinical acceptance level.

Vitreoscilla hemoglobin promotes Salecan production by Agrobacterium sp. ZX09

Salecan is a novel exopolysaccharide produced by the strain Agrobacterium sp. 7__X09, and it is composed of only glucose monomers. The unique chemical composition and excellent physicochemical properties make Salecan a promising material for applications in coagulation, lubrication, protection against acute liver injury, and alleviating constipation. In this study, we cloned the Vitreosci/la hemoglobin gene into a broad-host-range plasmid pCM158. Without antibiotic selection, there was negligible loss of the plasmid in the host Agrobacterium sp. ZX09 after one passage of cultivation. The expression of Vitreoscilla hemoglobin was demonstrated by carbon monoxide （CO） dif- ference spectrum. The engineered strain Agrobacterium sp. ZX09 increased Salecan yield by 30%. The other physi- ological changes included its elevated respiration rate and cellular invertase activity.