Indexing is an effective way to support efficient query processing in large databases. Recently the concept of learned index has been explored actively to replace or supplement traditional index structures with machine learning models to reduce storage and search costs. However, accurate and efficient similarity query processing in high-dimensional metric spaces remains to be an open challenge. In this paper, a novel indexing approach called LIMS is proposed to use data clustering and pivot-based data transformation techniques to build learned indexes for efficient similarity query processing in metric spaces. The underlying data is partitioned into clusters such that each cluster follows a relatively uniform data distribution. Data redistribution is achieved by utilizing a small number of pivots for each cluster. Similar data are mapped into compact regions and the mapped values are totally ordinal. Machine learning models are developed to approximate the position of each data record on the disk. Efficient algorithms are designed for processing range queries and nearest neighbor queries based on LIMS, and for index maintenance with dynamic updates. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of LIMS compared with traditional indexes and state-of-the-art learned indexes.
Ovarian tumor domain (OTU)-containing deubiquitinating enzymes (DUBs) are an essential DUB to maintain protein stability in plants and play important roles in plant growth development and stress response. However, there is little genome-wide identification and analysis of the OTU gene family in rice. In this study, we identified 20 genes of the OTU family in rice genome, which were classified into four groups based on the phylogenetic analysis. Their gene structures, conserved motifs and domains, chromosomal distribution, and cis elements in promoters were further studied. In addition, OTU gene expression patterns in response to plant hormone treatments, including SA, MeJA, NAA, BL, and ABA, were investigated by RT-qPCR analysis. The results showed that the expression profile of OsOTU genes exhibited plant hormone-specific expression. Expression levels of most of the rice OTU genes were significantly changed in response to rice stripe virus (RSV), rice black-streaked dwarf virus (RBSDV), Southern rice black-streaked dwarf virus (SRBSDV), and Rice stripe mosaic virus (RSMV). These results suggest that the rice OTU genes are involved in diverse hormone signaling pathways and in varied responses to virus infection, providing new insights for further functional study of OsOTU genes.
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