Hailong Yu

Pepper (Capsicum spp.) is a widely consumed vegetable with exceptionally large genomes in Solanaceae, yet its genomic evolutionary history remains largely unknown. Here we present 11 high-quality Capsicum genome assemblies, including two gap-free genomes, covering four wild and all five domesticated pepper species. We reconstructed the ancestral karyotype and inferred the evolutionary trajectory of peppers. The expanded and variable genome sizes were attributed to differential transposable element accumulations, which shaped 3D chromatin architecture and introduced mutations associated with traits such as fruit orientation and colour. Using a chromatin accessibility atlas of Capsicum, we highlight the influence of transposable elements on regulatory element evolution. Furthermore, by constructing a haploblock map of 124 pepper core germplasms, we uncover frequent introgressions that facilitate the formation of sweet blocky pepper and the acquisition of important traits such as resistance to pepper mild mottle virus. These findings on the genomic and functional evolution of Capsicum will benefit pepper breeding.

For geoscience text, rich domain corpora have become the basis of improving the model performance in word segmentation. However, the lack of domain-specific corpus with annotation labelled has become a major obstacle to professional information mining in geoscience fields. In this paper, we propose a corpus augmentation method based on Levenshtein distance. According to the technique, a geoscience dictionary of 20,137 words was collected and constructed by crawling the keywords from published papers in China National Knowledge Infrastructure (CNKI). The dictionary was further used as the main source of synonyms to enrich the geoscience corpus according to the Levenshtein distance between words. Finally, a Chinese word segmentation model combining the BERT, Bi-gated recurrent neural network (Bi-GRU), and conditional random fields (CRF) was implemented. Geoscience corpus composed of complex long specific vocabularies has been selected to test the proposed word segmentation framework. CNN-LSTM, Bi-LSTM-CRF, and Bi-GRU-CRF models were all selected to evaluate the effects of Levenshtein data augmentation technique. Experiments results prove that the proposed methods achieve a significant performance improvement of more than 10%. It has great potential for natural languages processing tasks like named entity recognition and relation extraction.

This paper first defines the concept of GIS-based model(GBM) metadata and analyzes its hierarchy. The second section gives the contents of GBM declarative metadata and services metadata. Then this paper defines the primary mode of GBM metadata based on XML Schema. Finally, this paper discusses the flows of development and application for GBM metadata. This paper improves the GBM attributes information management, which can advance GBM management efficiency and integration ability.