分类： 生物学 >> 植物学 >> 应用植物学 提交时间： 2018-10-26 合作期刊: 《广西植物》
摘要： Cladophora fracta, a filamentous green macroalgal epiphyte on rhodoliths, is described from five karst springs in North China. Although Cladophora species frequently appear in karst system, their genetic diversity, biogeographical affinities and physiological properties have not been well investigated in these environments. The specific objectives of this study were to: 1) describe the habitat of the Cladophora-like algae form the five karst springs; 2) identify the thallus to species level based on a combination of morphological characteristics and molecular sequence; and 3) explore the morphological influence of habitat. To elucidate the biogeographical patterns in Cladophora, both morphological and molecular evidence were compared of Cladophoraspecimens across five study sites. Analyses of partial small subunit (SSU) and large subunit (LSU) genes revealed that the studied 50 Cladophora specimens were genetically identical species and a total of 13 ribotypes were detected. The molecular sequencing results indicated that the examined species was highly homologous with Cladophora vagabunda, though they shared few morphological features. The genus didn't form a monophyletic clade but in three different clades both in SSU and LSU trees. The microscopic structure was more consistent with that of C. fracta. The Cladophora from the five karst springs did not show significant variation in cell dimensions. However, the species exhibited larger cell diameters than those reported from lakes. In addition, the rhizoid-like branches are only observed in two locations (XA and ST). Considering the morphological characteristics, we therefore hold our species as Cladophora fracta.
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-27 合作期刊: 《数据智能（英文）》
摘要： With the popularity of social media, there has been an increasing interest in user profiling and its applications nowadays. This paper presents our system named UIR-SIST for User Profiling Technology Evaluation Campaign in SMP CUP 2017. UIR-SIST aims to complete three tasks, including keywords extraction from blogs, user interests labeling and user growth value prediction. To this end, we first extract keywords from a users blog, including the blog itself, blogs on the same topic and other blogs published by the same user. Then a unified neural network model is constructed based on a convolutional neural network (CNN) for user interests tagging. Finally, we adopt a stacking model for predicting user growth value. We eventually receive the sixth place with evaluation scores of 0.563, 0.378 and 0.751 on the three tasks, respectively.
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