Open-domain targeted sentiment analysis aims to detect opinion targets along with their sentiment polarities from a sentence. Prior work typically formulates this task as a sequence tagging problem. However, such formulation suffers from problems such as huge search space and sentiment inconsistency. To address these problems, we propose a span-based extract-then-classify framework, where multiple opinion targets are directly extracted from the sentence under the supervision of target span boundaries, and corresponding polarities are then classified using their span representations. We further investigate three approaches under this framework, namely the pipeline, joint, and collapsed models. Experiments on three benchmark datasets show that our approach consistently outperforms the sequence tagging baseline. Moreover, we find that the pipeline model achieves the best performance compared with the other two models.
Background: Metabolic syndrome (MS) is one of the major causes of coronary artery diseases (CAD). Gut microbiome diversity and its natural fermentation products are not only correlated with MS and CAD, but their correlations also appear to be stronger than the associations with traditional risk factors. Therefore, the aim of this study was to provide a new potential pathway for the natural fermentation product butyrate to improve MS and to examine whether it is associated with serum metabolic profiles and gut flora composition.
Methods: C57BL/6J mice fed a high-fat diet (HFD) were treated with 400 mg/kg of sodium butyrate for 16 weeks. Blood and fecal samples were collected, and the metabolite concentrations and 16s rRNA were measured with liquid chromatography–MS and Illumina platform, respectively. The plasma differential metabolites and gut microbiome composition were analyzed with XCMS online and QIIME 2, respectively.
Results: Gut microbiome-derived butyrate reduced glucose intolerance and insulin resistance, resisting HFD-induced increase in the relative abundance of f_Lachnospiraceae, f_Rikenellaceae, and f_Paraprevotellaceae. Meanwhile, sodium butyrate increased the levels of α-linolenate, all-trans-retinal, resolvin E1, and leukotriene in the plasma, and the differential pathways showed enrichment in mainly resolvin E biosynthesis, histidine degradation, lipoxin biosynthesis, and leukotriene biosynthesis. Moreover, sodium butyrate increased the levels of phosphorylated-adenosine 5′-monophosphate-activated protein kinase (p-AMPK) and facilitated glucose transporter member 4 (GLUT4) in the adipose tissue.
Conclusion: Butyrate can induce AMPK activation and GLUT4 expression in the adipose tissue, improving cardiovascular disease (CVD)-related metabolic disorder, resisting HFD-induced gut microbiome dysbiosis, and promoting resolvin E1 and lipoxin biosynthesis. Oral supplement of the natural fermentation product butyrate can be a potential strategy for preventing CVD.
Mass spectrometry imaging (MSI) has become a powerful tool to probe molecule events in biological tissue. However, it is a widely held viewpoint that one of the biggest challenges is an easy-to-use data processing software for discovering the underlying biological information from complicated and huge MSI dataset. Here, a user-friendly and full-featured MSI software including three subsystems, Solution, Visualization and Intelligence, named MassImager, is developed focusing on interactive visualization, in-situ biomarker discovery and artificial intelligent pathological diagnosis. Simplified data preprocessing and high-throughput MSI data exchange, serialization jointly guarantee the quick reconstruction of ion image and rapid analysis of dozens of gigabytes datasets. It also offers diverse self-defined operations for visual processing, including multiple ion visualization, multiple channel superposition, image normalization, visual resolution enhancement and image filter. Regions-of-interest analysis can be performed precisely through the interactive visualization between the ion images and mass spectra, also the overlaid optical image guide, to directly find out the region-specific biomarkers. Moreover, automatic pattern recognition can be achieved immediately upon the supervised or unsupervised multivariate statistical modeling. Clear discrimination between cancer tissue and adjacent tissue within a MSI dataset can be seen in the generated pattern image, which shows great potential in visually in-situ biomarker discovery and artificial intelligent pathological diagnosis of cancer. All the features are integrated together in MassImager to provide a deep MSI processing solution at the in-situ metabolomics level for biomarker discovery and future clinical pathological diagnosis.
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