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Guo, Ran

Publications and source records attributed to Guo, Ran.

Comparative genomics and transcriptomics analysis reveals evolution patterns of selection in the Salix phylogeny

Background: Willows are widely distributed in the northern hemisphere and have good adaptability to different living environment. The increasing of genome and transcriptome data provides a chance for comparative analysis to study the evolution patterns with the different origin and geographical distributions in the Salix phylogeny. Results: Transcript sequences of 10 Salicaceae species were downloaded from public databases. All pairwise of orthologues were identified by comparative analysis in these species, from which we constructed a phylogenetic tree and estimated the rate of diverse. Divergence times were estimated in the 10 Salicaceae using comparative transcriptomic analysis. All of the fast-evolving positive selection sequences were identified, and some cold-, drought-, light-, universal-, and heat- resistance genes were discovered. Conclusions: The divergence time of subgenus Vetrix and Salix was about 17.6–16.0 Mya during the period of Middle Miocene Climate Transition (21–14 Mya). Subgenus Vetrix diverged to migratory and resident groups when the climate changed to the cool and dry trend by 14 Mya. Cold- and light- stress genes were involved in positive selection among the resident Vetrix, and which would help them to adapt the cooling stage. Universal- stress genes exhibited positive selection among the migratory group and subgenus Salix. These data are useful for comprehending the adaptive evolution and speciation in the Salix lineage.

59 BASIC BIOLOGICAL SCIENCES↗

Unsupervised classification-based hyperspectral data processing: lossy compression

The existing lossy compression algorithms play an important role in reducing the cost of storage equipment and bandwidth for hyperspectral (HS) application. However, none of the lossy compression algorithms considers the real-time classification of HS data. In this paper, we present a new lossy compression method for HS data that aims to optimally compress in both spatial and spectral domains and simultaneously maximize classification performance. For this target, Harsanyi–Farrand–Chang (HFC) and k-means++ algorithms are applied to achieve a spectral library and an index matrix for HS image. Spectral angle mapping and Euclidean distance are used to update the spectral library and the index matrix. The experiment results indicate that the proposed method has a good classification performance. The results also reveal that the proposed method works well in real-time classification and compression of HS data with a large volume and achieves a high compression ratio. It is noteworthy to mention that the superiority of our method in compression becomes more apparent as the volume of HS data grows. Consequently, the proposed method has a strong advantage in HS applications that require both compression and classification.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗