FOAL: Fine-grained Contrastive Learning for Cross-domain Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) has achieved promising results while relying on sufficient annotation data in a specific domain. However, it is infeasible to annotate data for each individual domain. We propose to explore ASTE in the cross-domain setting, which transfers knowledge from a resource-rich source domain to a resource-poor target domain, thereby alleviating the reliance on labeled data in the target domain. To effectively transfer the knowledge across domains and extract the sentiment triplets accurately, we propose a method named Fine-grained cOntrAstive Learning (FOAL) to reduce the domain discrepancy and preserve the discriminability of each category. Experiments on six transfer pairs show that FOAL achieves 6% performance gains and reduces the domain discrepancy significantly compared with strong baselines. Our code will be publicly available once accepted

Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) is widely used in various applications. However, existing ASTE datasets are limited in their ability to represent real-world scenarios, hindering the advancement of research in this area. In this paper, we introduce a new dataset, named DMASTE, which is manually annotated to better fit real-world scenarios by providing more diverse and realistic reviews for the task. The dataset includes various lengths, diverse expressions, more aspect types, and more domains than existing datasets. We conduct extensive experiments on DMASTE in multiple settings to evaluate previous ASTE approaches. Empirical results demonstrate that DMASTE is a more challenging ASTE dataset. Further analyses of in-domain and cross-domain settings provide promising directions for future research. Our code and dataset are available at https://github.com/NJUNLP/DMASTE.Comment: 15pages, 5 figures, ACL202

WNT/β-catenin signaling promotes VSMCs to osteogenic transdifferentiation and calcification through directly modulating Runx2 gene expression

AbstractArterial medial calcification (AMC) is prevalent in patients with chronic kidney disease (CKD) and contributes to elevated risk of cardiovascular events and mortality. Vascular smooth muscle cells (VSMCs) to osteogenic transdifferentiation (VOT) in a high-phosphate environment is involved in the pathogenesis of AMC in CKD. WNT/β-catenin signaling is indicated to play a crucial role in osteogenesis via promoting Runx2 expression in osteoprogenitor cells, however, its role in Runx2 regulation and VOT remains incompletely clarified. In this study, Runx2 was induced and β-catenin was activated by high-phosphate in VSMCs. Two forms of active β-catenin, dephosphorylated on Ser37/Thr41 and phosphorylated on Ser675 sites, were upregulated by high-phosphate. Activation of β-catenin, through ectopic expression of stabilized β-catenin, inhibition of GSK-3β, or WNT-3A protein, induced Runx2 expression, whereas blockade of WNT/β-catenin signaling with Porcupine (PORCN) inhibitor or Dickkopf-1 (DKK1) protein inhibited Runx2 induction by high-phosphate. WNT-3A promoted osteocalcin expression and calcium deposition in VSMCs, whereas DKK1 ameliorated calcification of VSMCs induced by high-phosphate. Two functional T cell factor (TCF)/lymphoid enhancer-binding factor binding sites were identified in the promoter region of Runx2 gene in VSMCs, which interacted with TCF upon β-catenin activation. Site-directed mutation of each of them attenuated Runx2 response to β-catenin, and deletion or destruction of both of them completely abolished this responsiveness. In the aortic tunica media of rats with chronic renal failure, followed by AMC, Runx2 and β-catenin was induced, and the Runx2 mRNA level was positively associated with the abundance of phosphorylated β-catenin (Ser675). Collectively, our study suggested that high-phosphate may activate WNT/β-catenin signaling through different pathways, and the activated WNT/β-catenin signaling, through direct downstream target Runx2, could play an important role in promoting VOT and AMC

Meta contrastive label correction for financial time series

Financial applications such as stock price forecasting, usually face an issue that under the predefined labeling rules, it is hard to accurately predict the directions of stock movement. This is because traditional ways of labeling, taking Triple Barrier Method, for example, usually gives us inaccurate or even corrupted labels. To address this issue, we focus on two main goals. One is that our proposed method can automatically generate correct labels for noisy time series patterns, while at the same time, the method is capable of boosting classification performance on this new labeled dataset. Based on the aforementioned goals, our approach has the following three novelties: First, we fuse a new contrastive learning algorithm into the meta-learning framework to estimate correct labels iteratively when updating the classification model inside. Moreover, we utilize images generated from time series data through Gramian angular field and representative learning. Most important of all, we adopt multi-task learning to forecast temporal-variant labels. In the experiments, we work on 6% clean data and the rest unlabeled data. It is shown that our method is competitive and outperforms a lot compared with benchmarks

Network pharmacology-based elucidation of the molecular mechanism underlying the anti-migraine effect of Asari Radix et Rhizoma

Purpose: To determine the molecular mechanism involved in the anti-migraine effect of Asari Radix et Rhizoma (ARR) using network pharmacology. Methods: The compounds present in ARR were identified through information retrieval from literature and public databases, and were screened based on absorption, distribution, metabolism, excretion and toxicity. Target genes related to the selected compounds and migraine were identified or predicted from public databases. Hub genes in ARR against migraine were identified through analysis of interactions in overlapping genes between compounds and migraine target genes, based on STRING database. Gene enrichment analysis of overlapping genes was performed using Database for Annotation, Visualization and Integrated Discovery. Results: A total of 138 compounds were selected as potential bioactive compounds in ARR. Target genes related to the selected compounds (611 genes) and migraine (278 genes) were obtained, including 71 overlapping genes. The hub genes in the anti-migraine effect of ARR were BDNF, IL6, COMT, APP and TNF. Gene enrichment analysis showed the top 10 biological processes or pathways involved in the mechanism of anti-migraine action of ARR. The tissue source of the overlapping genes was not limited to the brain. The results from gene enrichment analysis revealed that the effect of ARR on migraine was holistic, which is characteristic of traditional Chinese medicines. Conclusion: Network pharmacology has been used to decipher the molecular mechanism involved in the action of ARR against migraine. The results provide a scientific basis for the clinical effect of ARR on migraine