Layout optimization for multi-bi-modulus materials system under multiple load cases

Financial support from the National Natural Science Foundation of China (Grant No. 51179164) and the Australian Research Council (Grant No. DP140103137) is acknowledged

miR-1258: a novel microRNA that controls TMPRSS4 expression is associated with malignant progression of papillary thyroid carcinoma

Background: MicroRNA-1258 (miR-1258) has been shown to play an anti-cancer role in a variety of cancers, but its relationship with papillary thyroid cancer (PTC) has not been reported. The emphasis of this research was to reveal the biological function of miR-1258 in PTC and its potential mechanisms. Material and methods: We measured miR-1258 expression in PTC cells and the transfection efficiency of miR-1258 mimic and miR-1258 inhibitor by quantitative real-time PCR (qRT-PCR) assay. Cell Counting Kit-8 assay (CCK8) and Transwell experiments were conducted to examine the influences of altering miR-1258 expression on the viability, migration, and invasion of PTC cells. Bioinformatics prediction and dual-luciferase experiment were performed to verify the target gene of miR-1258. Finally, we carried out a rescue assay to verify whether the regulation of miR-1258 on the biological behaviour of PTC cells needs to be achieved by regulating TMPRSS4. Results: The outcomes revealed that miR-1258 was lowly expressed in PTC cell lines and miR-1258 showed the lowest expression in KTC-1 and the highest expression in B-CPAP among all tested PTC cell lines. Overexpression of miR-1258 inhibited KTC-1 cell viability and ability to migrate and invade, whereas inhibition of miR-1258 in B-CPAP cells has the opposite effect. Furthermore, we affirmed that miR-1258 can directly target TMPRSS4, and miR-1258 can reduce the biological malignant behaviour of PTC cells via regulation of TMPRSS4. Conclusion: Taken together, our research raised the possibility that miR-1258 was an anti-oncogene, which exerts its anti-cancer function by targeting TMPRSS4. Hence, it may be possible to treat PTC by targeting the miR-1258/TMPRSS4 axis in the future.

Joint Multimodal Entity-Relation Extraction Based on Edge-enhanced Graph Alignment Network and Word-pair Relation Tagging

Multimodal named entity recognition (MNER) and multimodal relation extraction (MRE) are two fundamental subtasks in the multimodal knowledge graph construction task. However, the existing methods usually handle two tasks independently, which ignores the bidirectional interaction between them. This paper is the first to propose jointly performing MNER and MRE as a joint multimodal entity-relation extraction task (JMERE). Besides, the current MNER and MRE models only consider aligning the visual objects with textual entities in visual and textual graphs but ignore the entity-entity relationships and object-object relationships. To address the above challenges, we propose an edge-enhanced graph alignment network and a word-pair relation tagging (EEGA) for JMERE task. Specifically, we first design a word-pair relation tagging to exploit the bidirectional interaction between MNER and MRE and avoid the error propagation. Then, we propose an edge-enhanced graph alignment network to enhance the JMERE task by aligning nodes and edges in the cross-graph. Compared with previous methods, the proposed method can leverage the edge information to auxiliary alignment between objects and entities and find the correlations between entity-entity relationships and object-object relationships. Experiments are conducted to show the effectiveness of our model.Comment: accepted in AAAI-202