Tumor microenvironment contributes to tumor angiogenesis. However, the role of the activated cancer associated-fibroblasts (CAFs) in angiogenesis is still unclear. Here we report that miR-205/YAP1 signaling in the activated stromal fibroblasts plays a critical role in VEGF-independent angiogenesis in breast tumor. Methods: miR-205 expression was assessed by quantitative real-time polymerase chain reaction (qRT-PCR); YAP1 expression by qRT-PCR, western blotting and immunohistochemistry; IL11 and IL15 expression by qRT-PCR, western blotting and ELISA. Tube formation and three-dimensioned sprouting assays in vitro, and orthotopic Xenografts in vivo were conducted as angiogenesis experiments. The mechanism of miR-205/YAP1-mediated tumor angiogenesis was analyzed via overexpression and shRNA, siRNA, or antibody neutralization experiments in combination with anti-VEGF antibody or Axitinib. Results: miR-205/YAP1 signaling axis activates breast normal fibroblasts (NFs) into CAFs, promotes tubule formation and sprouting of Human Umbilical Vein Endothelial Cells (HUVECs). Rescue of miR-205 in CAFs blunts angiogenesis processes. YAP1, a target of miR-205, does not regulate VEGF expression but specifically enhances IL11 and IL15 expressions, maintaining tumor angiogenesis even in the presence of Axitinib or after exhaustion of VEGF by neutralizing VEGF antibody. IL11 and IL15 released from CAFs activate STAT3 signaling in HUVECs. Blockage of IL11 and IL15 expression in CAFs results in the inactivation of STAT3-signaling in HUVECs and repression of the CAF-induced angiogenesis. The blunt angiogenesis halts the invasion and metastasis of breast cancer cells in vivo. Conclusions: These results provide a novel insight into breast CAF-induced tumor angiogenesis in a VEGF-independent manner.

The gelation phenomenon often occurs in the solution crystallization process, which would seriously affect the process robustness and product quality. In this paper, the gelation phenomenon during the solution crystallization of perindopril erbumine (PDP) was systematically studied by spectroscopy and microscopy techniques. The results suggest that the PDP gel does not have the characteristic diffraction peak of the PDP crystal and the infrared spectrum has changed significantly compared with that of the crystal product. Accordingly, the mechanism of the gelation phenomenon of PDP was inferred to follow the status of aggregates, amorphous, crystalline fibers, and jelly-like phases during the gelation process development. Based on the proposed gelation mechanism, three controlling strategies, namely, direct cooling crystallization, seeding-assisted crystallization, and oscillatory temperature crystallization, were carried out to evaluate the effectiveness of process optimization. Inspired by direct nucleation control theory, through controlling the seed crystal number before the cooling step, the oscillatory temperature crystallization strategy effectively avoided the gelation problem, and the crystal product has good filtration properties and powder properties, which provides a feasible process method for practical production, avoiding the gelation phenomenon and important changes of the production line.

The construction industry is characterized by long production cycles, poor mobility of workers, various kinds of outdoor operations and complex construction processes, leading to frequent safety accidents. To explore the occurrence rule of the construction accidents in building construction, this paper applied knowledge graph technology in the field of artificial intelligence to analyze construction accidents. Firstly, defining the conceptual architecture of the domain knowledge graph. Secondly, extracting key knowledge elements from construction accident data. The knowledge graph of construction accidents has been established by using the Neo4j graph database. Further, a construction accident analysis process based on the knowledge graph has been proposed. The intelligent analysis, such as query, statistical analysis and correlation path analysis for accident information have been conducted. The results shows that based on knowledge graph technology, construction accidents in visual graphics or tables could be visualized. The accident information in the form of knowledge could be saved and queried quickly. The study can provide knowledge support for accident prevention and improve the efficiency of accident analysis. Besides, it can provide innovative ideas as well as decision support for safety management.

Flow-based generative models are a family of exact log-likelihood models with tractable sampling and latent-variable inference, hence conceptually attractive for modeling complex distributions. However, flow-based models are limited by density estimation performance issues as compared to state-of-the-art autoregressive models. Autoregressive models, which also belong to the family of likelihoodbased methods, however suffer from limited parallelizability. In this paper, we propose Dynamic Linear Flow (DLF), a new family of invertible transformations with partially autoregressive structure. Our method benefits from the efficient computation of flow-based methods and high density estimation performance of autoregressive methods. We demonstrate that the proposed DLF yields state-of-theart performance on ImageNet 32×32 and 64×64 out of all flow-based methods, and is competitive with the best autoregressive model. Additionally, our model converges 10 times faster than Glow (Kingma and Dhariwal, 2018). The code is available at https://github.com/naturomics/DLF.Preprint. Under review.