Multi-hop knowledge graph question answer (KGQA) is a challenging task because it requires reasoning over multiple edges of the knowledge graph (KG) to arrive at the right answer. However, KGs are often incomplete with many missing links, posing additional challenges for multi-hop KGQA. Recent research on multi-hop KGQA attempted to deal with KG sparsity with relevant external texts. In our work, we propose a multi-hop KGQA model based on relation knowledge enhancement (RKE-KGQA), which fuses both label and text relations through global attention for relation knowledge augmentation. It is well known that the relation between entities can be represented by labels in the knowledge graph or texts in the text corpus, and multi-hop KGQA needs to jump across different entities through relations. First, we assign an activation probability to each entity, then calculate a score for the enhancement relation, and then transfer the score through the activated relations and, finally, obtain the answer. We carry out extensive experiments on three datasets and demonstrate that RKE-KGQA achieves the outperformance result.

Chinese Named Entity Recognition is the fundamental technology in the field of the Chinese Natural Language Process. It is extensively adopted into information extraction, intelligent question answering, and knowledge graph. Nevertheless, due to the diversity and complexity of Chinese, most Chinese NER methods fail to sufficiently capture the character granularity semantics, which affects the performance of the Chinese NER. In this work, we propose DSKE-Chinese NER: Chinese Named Entity Recognition based on Dictionary Semantic Knowledge Enhancement. We novelly integrate the semantic information of character granularity into the vector space of characters and acquire the vector representation containing semantic information by the attention mechanism. In addition, we verify the appropriate number of semantic layers through the comparative experiment. Experiments on public Chinese datasets such as Weibo, Resume and MSRA show that the model outperforms characterbased LSTM baselines.

Background: Gastric cancer(GC) refers to malignant tumor that derived from gastric epithelial cells. Ferroptosis is another programmed cell demise mode that is Fe-dependent, unique concerning apoptosis, cell necrosis, and autophagy. Current research demonstrates that ferroptosis assumes a basic part of cancer biology. Extracellular matrix(ECM) has been confirmed to play an essential role in the proliferation, apoptosis, metabolism and differentiation of tumor cells. As an important component of the tumor microenvironment, ECM interacts with the immune microenvironment and affects tumor development and progression. Methods: GC data were downloaded from the TCGA database. Furthermore, 259 ferroptosis-related genes were acquired with the FerrDb database. COX regression analysis was used to screen ferroptosis-related genes related to GC's prognosis, and these genes constructed the prediction model. The risk score of the model and clinical data of GC were further analyzed to get the correlation between the model and the overall survival(OS) rate and clinicopathological features. Finally, GO and KEGG enrichment analysis was carried out on the genes of the model. To further analyze the correlation between the genes in the model and tumor immunity, ssGSEA was used to score immune cells and calculate immune-related pathways' activity quantitatively. Results: A prognosis model was constructed according to the 11 ferroptosis-related genes related to prognosis to predict the prognosis of GC patients better. According to univariate and multivariate, risk score can be regarded as an independent predictor.Conclusions: we identified 11 ferroptosis-related genes (NOX4, NOX5, SLC1A5, GLS2, MYB, TGFBR1, NF2, ZFP36, DUSP1, SLC1A4, SP1), which affected the prognosis of GC patients.

It’s important to discover new resources and expand knowledge of the distribution range of Apis cerana for the conservation and utilization of genetic resources. Here, we newly sequenced genomes and apply population genetic and morphological analysis methods using publicly available genome-wide datasets to investigate the origin and adaptation of A.cerana sanshasis from Yongxing Island. The results showed SanshaId colony was significantly genetically differentiated from others with an average Fst of 0.2669, indicating a level of subspecies differentiation. Morphological clustering also showed significant differences, especially the tongue length of SanshaId colony was significantly smaller than that of others. In summary, we concluded that A.cerana sanshasis is a subspecies of A.cerana. Population history analysis showed SanshaId population has a recent common ancestral feature with Hainan’s, and subspecies differentiation occurred around 0.57 Ma due to geological movements causing geographic isolation. We examined the genetic variation between the recently marginalized colonies from HainanId and SanshaId, and found high frequency nonsynonymous mutations gene in SanshaId colony involved in Glutathione metabolism and other signaling pathways, among which a gene potentially related to tongue length morphology, Cuticular, was subject to evolutionary selection; meanwhile, differential tissue expression profiles showed a correlation with glucose metabolism genes are highly expressed in the midgut, suggesting these genes may be associated with adaptation to tropical island environments. Our results expand the understanding of the distribution range of Asian honeybee and provide the basis for understanding the population dynamics of A.cerana sanshasis and the molecular evolutionary mechanisms of adaptation to tropical island environments.