TNFSF15 facilitates the differentiation of CD11b^(+) myeloid cells into vascular pericytes in tumors

Objective:Immature vasculature lacking pericyte coverage substantially contributes to tumor growth,drug resistance,and cancer cell dissemination.We previously demonstrated that tumor necrosis factor superfamily 15(TNFSF15)is a cytokine with important roles in modulating hematopoiesis and vascular homeostasis.The main purpose of this study was to explore whether TNFSF15 might promote freshly isolated myeloid cells to differentiate into CD11b^(+) cells and further into pericytes.Methods:A model of Lewis lung cancer was established in mice with red fluorescent bone marrow.After TNFSF15 treatment,CD11b^(+) myeloid cells and vascular pericytes in the tumors,and the co-localization of pericytes and vascular endothelial cells,were assessed.Additionally,CD11b^(+) cells were isolated from wild-type mice and treated with TNFSF15 to determine the effects on the differentiation of these cells.Results:We observed elevated percentages of bone marrow-derived CD11b^(+)myeloid cells and vascular pericytes in TNFSF15-treated tumors,and the latter cells co-localized with vascular endothelial cells.TNFSF15 protected against CD11b^(+)cell apoptosis and facilitated the differentiation of these cells into pericytes by down-regulating Wnt3a-VEGFR1 and up-regulating CD49e-FN signaling pathways.Conclusions:TNFSF15 facilitates the production of CD11b^(+) cells in the bone marrow and promotes the differentiation of these cells into pericytes,which may stabilize the tumor neovasculature.

Multi-head attention graph convolutional network model:End-to-end entity and relation joint extraction based on multi-head attention graph convolutional network

At present,the entity and relation joint extraction task has attracted more and more scholars'attention in the field of natural language processing(NLP).However,most of their methods rely on NLP tools to construct dependency trees to obtain sentence structure information.The adjacency matrix constructed by the dependency tree can convey syntactic information.Dependency trees obtained through NLP tools are too dependent on the tools and may not be very accurate in contextual semantic description.At the same time,a large amount of irrelevant information will cause redundancy.This paper presents a novel end-to-end entity and relation joint extraction based on the multihead attention graph convolutional network model(MAGCN),which does not rely on external tools.MAGCN generates an adjacency matrix through a multi-head attention mechanism to form an attention graph convolutional network model,uses head selection to identify multiple relations,and effectively improve the prediction result of overlapping relations.The authors extensively experiment and prove the method's effectiveness on three public datasets:NYT,WebNLG,and CoNLL04.The results show that the authors’method outperforms the state-of-the-art research results for the task of entities and relation extraction.

Knowledge-Enhanced Bilingual Textual Representations for Cross-Lingual Semantic Textual Similarity

Joint learning of words and entities is advantageous to various NLP tasks, while most of the works focus on single language setting. Cross-lingual representations learning receives high attention recently, but is still restricted by the availability of parallel data. In this paper, a method is proposed to jointly embed texts and entities on comparable data. In addition to evaluate on public semantic textual similarity datasets, a task (cross-lingual text extraction) was proposed to assess the similarities between texts and contribute to this dataset. It shows that the proposed method outperforms cross-lingual representations methods using parallel data on cross-lingual tasks, and achieves competitive results on mono-lingual tasks.