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Abstract Recent works have introduced prompt learning for Event Argument Extraction (EAE) since prompt-based approaches transform downstream tasks into a more consistent format with the training task of Pre-trained Language Model (PLM). This helps bridge the gap between downstream tasks and model training. However, these previous works overlooked the complex number of events and their relationships within sentences. In order to address this issue, we propose Event Co-occurrences Prefix Event Argument Extraction (ECPEAE). ECPEAE utilizes the co-occurrences events prefixes module to incorporate template information corresponding to all events present in the current input as prefixes. These co-occurring event knowledge assist the model in handling complex event relationships. Additionally, to emphasize the template corresponding to the current event being extracted and enhance its constraint on the output format, we employ the present event bias module to integrate the template information into the calculation of attention at each layer of the model. Furthermore, we introduce an adjustable copy mechanism to overcome potential noise introduced by the additional information in the attention calculation at each layer. We validate our model using two widely used EAE datasets, ACE2005-EN and ERE-EN. Experimental results demonstrate that our ECPEAE model achieves state-of-the-art performance on both the ACE2005-EN dataset and the ERE dataset. Additionally, according to the results, our model also can be adapted to the low resource environment of different training sizes effectively.

Abstract Visible-Infrared Person Re-identification (VI-ReID) has been consistently challenged by the significant intra-class variations and cross-modality differences between different cameras. Therefore, the key lies in how to extract discriminative modality-shared features. Existing VI-ReID methods based on Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have shortcomings in capturing global features and controlling computational complexity, respectively. To tackle these challenges, we propose a hybrid network framework called ReMamba. Specifically, we first use a CNN as the backbone network to extract multi-level features. Then, we introduce the Visual State Space (VSS) model, which is responsible for integrating the local features output by the CNN from lower to higher levels. These local features serve as a complement to global information and thereby enhancing the local details clarity of the global features. Considering the potential redundancy and semantic differences between local and global features, we design an adaptive feature aggregation module that automatically filters and effectively aggregates both types of features, incorporating an auxiliary aggregation loss to optimize the aggregation process. Furthermore, to better constrain cross-modality features and intra-modal features, we design a modal consistency identity constraint loss to alleviate cross-modality differences and extract modality-shared information. Extensive experiments conducted on the SYSU-MM01, RegDB, and LLCM datasets demonstrate that our proposed ReMamba outperforms state-of-the-art VI-ReID methods.

Background Chronic obstructive pulmonary disease (COPD) is considered related to chronic systemic inflammation. Renin angiotensin system (RAS) inhibitor, exerting an anti-inflammatory action in many systems, has been demonstrated relevant to the pathogenesis of COPD. However, the association between RAS inhibitor use and prognosis of patients with COPD remains controversial. Therefore, we conducted a meta-analysis and systematic review to summarise current evidence.Material and methods Databases, including Medline, Embase, Web of Science and Cochran Library, were searched for eligible studies by the end of 30 September 2022. Observational studies or randomised controlled trials (RCTs) that investigated the association of RAS inhibitor use with prognosis of COPD (mortality or risk of acute exacerbation) were selected. The Newcastle-Ottawa Scale was used for quality assessment of observational studies, while the Cochrane risk-of-bias tool was used to assess the quality of RCTs. Statistical analyses were performed using Stata V.15. We selected relative risk (RR) with 95% CI as the effect measure. Heterogeneity was assessed by I-squared (I2) statistics. The funnel plot was used for visual assessment of publication bias.Results A total of 20 studies with 5 51 649 subjects were included in the meta-analysis. The overall analysis indicated that RAS inhibitor use decreased the risk of death in patients with COPD (RR: 0.69, 95% CI: 0.61 to 0.78). Subgroup analyses were conducted according to comorbidities, race and type of RAS inhibitors, and the results kept consistent. However, in the pooled analysis of prospective studies, RAS inhibitor use did not significantly decrease the mortality (RR: 0.89, 95% CI: 0.78 to 1.02). Additionally, the risk of exacerbations of COPD did not decrease in patients who were prescribed RAS inhibitors (RR: 0.99, 95% CI: 0.80 to 1.23). The funnel plot indicated significant publication bias.Conclusion RAS inhibitor use seemed to be associated with a reduction of mortality in patients with COPD. However, the available evidence is weak due to potential biases from retrospective studies and the heterogeneity across included studies.

Long non-coding RNAs (lncRNAs) have previously been reported to be involved in cancer invasion, proliferation and apoptosis. However, the association between the lncRNA, H19, and esophageal cancer (EC) has remained elusive. In the present study, reverse transcription quantitative-polymerase chain reaction revealed that the expression of H19 was significantly increased and associated with tumor depth and metastasis in 133 EC samples. Furthermore, MTT and Transwell assays revealed that overexpression of H19 in vitro promoted the proliferation and invasion of EC cell lines, whereas knockdown of H19 inhibited the proliferation and invasion of EC cell lines. In addition, it was identified that an upregulation of H19 induced epithelial-to-mesenchymal transition, while the opposite effect was observed following the downregulation of H19. In conclusion, H19 has a significant role in the development of EC and may serve as a potential prognostic marker and therapeutic target for EC. [ABSTRACT FROM AUTHOR]