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SUMMARYReplication factor C1 (RFC1), which is conserved in eukaryotes, is involved in DNA replication and checkpoint control. However, a RFC1 product participating in DNA repair at meiosis has not been reported in Arabidopsis. Here, we report functional characterization of AtRFC1 through analysis of the rfc1-2 mutant. The rfc1-2 mutant displayed normal vegetative growth but showed silique sterility because the male gametophyte was arrested at the uninucleus microspore stage and the female at the functional megaspore stage. Expression of AtRFC1 was concentrated in the reproductive organ primordia, meiocytes and developing gametes. Chromosome spreads showed that pairing and synapsis were normal, and the chromosomes were broken when desynapsis began at late prophase I, and chromosome fragments remained in the subsequent stages. For this reason, homologous chromosomes and sister chromatids segregated unequally, leading to pollen sterility. Immunolocalization revealed that the AtRFC1 protein localized to the chromosomes during zygotene and pachytene in wild-type but were absent in the spo11-1 mutant. The chromosome fragmentation of rfc1-2 was suppressed by spo11-1, indicating that AtRFC1 acted downstream of AtSPO11-1. The similar chromosome behavior of rad51 rfc1-2 and rad51 suggests that AtRFC1 may act with AtRAD51 in the same pathway. In summary, AtRFC1 is required for DNA double-strand break repair during meiotic homologous recombination of Arabidopsis.

This study explores new ways of tag-based personalized recommendation by relieving the assumption that tags assigned by a user occur independently of each other. The new methods profile users using tag co-occurrence networks, upon which link-based node weighting methods (e.g. PageRank and HITS) are applied to refine the weights of tags. A diffusion process is then performed upon the tag-item bipartite graph to transform the weights of tags into recommendation scores for items. Experiments on three datasets showed improvements of the proposed method over the tag-based collaborative filtering in terms of precision and recall in the datasets with dense user-tag networks and in terms of inter-diversity in all datasets. In addition, the user-tag network is found to be a necessary instrument for the improvements. The findings of this study contribute to more accurate user profiling and personalized recommendations using network theory and have practical implications for tag-based recommendation systems.

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