Genome-Wide Identification and Characterization of the CCT Gene Family in Rapeseed (Brassica napus L.).

The CCT gene family is present in plants and is involved in biological processes such as flowering, circadian rhythm regulation, plant growth and development, and stress resistance. We identified 87, 62, 46, and 40 CCTs at the whole-genome level in B. napus, B. rapa, B. oleracea, and A. thaliana, respectively. The CCTs can be classified into five groups based on evolutionary relationships, and each of these groups can be further subdivided into three subfamilies (COL, CMF, and PRR) based on function. Our analysis of chromosome localization, gene structure, collinearity, cis-acting elements, and expression patterns in B. napus revealed that the distribution of the 87 BnaCCTs on the chromosomes of B. napus was uneven. Analysis of gene structure and conserved motifs revealed that, with the exception of a few genes that may have lost structural domains, the majority of genes within the same group exhibited similar structures and conserved domains. The gene collinearity analysis identified 72 orthologous genes, indicating gene duplication and expansion during the evolution of BnaCCTs. Analysis of cis-acting elements identified several elements related to abiotic and biotic stress, plant hormone response, and plant growth and development in the promoter regions of BnaCCTs. Expression pattern and protein interaction network analysis showed that BnaCCTs are differentially expressed in various tissues and under stress conditions. The PRR subfamily genes have the highest number of interacting proteins, indicating their significant role in the growth, development, and response to abiotic stress of B. napus.

Overlapping Community Detection Based on Attribute Augmented Graph.

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.