TP-GCL: graph contrastive learning from the tensor perspective.

Graph Neural Networks (GNNs) have demonstrated significant potential as powerful tools for handling graph data in various fields. However, traditional GNNs often encounter limitations in information capture and generalization when dealing with complex and high-order graph structures. Concurrently, the sparse labeling phenomenon in graph data poses challenges in practical applications. To address these issues, we propose a novel graph contrastive learning method, TP-GCL, based on a tensor perspective. The objective is to overcome the limitations of traditional GNNs in modeling complex structures and addressing the issue of sparse labels. Firstly, we transform ordinary graphs into hypergraphs through clique expansion and employ high-order adjacency tensors to represent hypergraphs, aiming to comprehensively capture their complex structural information. Secondly, we introduce a contrastive learning framework, using the original graph as the anchor, to further explore the differences and similarities between the anchor graph and the tensorized hypergraph. This process effectively extracts crucial structural features from graph data. Experimental results demonstrate that TP-GCL achieves significant performance improvements compared to baseline methods across multiple public datasets, particularly showcasing enhanced generalization capabilities and effectiveness in handling complex graph structures and sparse labeled data.

Three-dimensional sonography has satisfied accuracy for detecting rotator cuff tears.

Background: Rotator cuff injuries and tears are common causes of shoulder pain and dysfunction, necessitating accurate diagnostic methods to guide clinical decision-making. This study evaluates the diagnostic utility of three-dimensional (3D) shoulder sonography in identifying rotator cuff injury and tear patterns. Methods: A comprehensive search across seven electronic databases, which included Cochrane Library, Embase, PubMed, Cochrane Library, China Biology Medicine (CBM) database, CNKI, Wanfang, and VIP database. These databases were utilized to retrieve articles that assess the diagnostic value of 3D shoulder sonography for identifying rotator cuff injuries and tear patterns. The effectiveness of 3D shoulder sonography was assessed in terms of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). For each parameter, the 95% confidence intervals were calculated. Additionally, summary receiver operating characteristic curves (SROCs) were constructed, allowing for a comprehensive evaluation of diagnostic accuracy, which is reflected in the area under the SROC curve (AUC). Results: Screening of 8,508 identified nine literatures eligible for inclusion in the meta-analysis, encompassing a total of 366 patients. The analysis of detecting any rotator cuff tear revealed a sensitivity of 0.97 and specificity of 0.87, yielding a DOR of 90.03 and an AUC of 0.98. Furthermore, 3D shoulder sonography demonstrated satisfactory accuracy in detecting both full and partial-thickness rotator cuff tears (Sensitivity: 0.92 vs. 0.83, specificity: 0.94 vs. 097, and AUC: 0.96 vs. 0.95). Conclusion: This study indicates that three-dimensional sonography has satisfied accuracy for detecting rotator cuff tears.

Can Regulatory Fit Improve Elementary School Students' Performance? Effects of Different Types of Regulatory Fit.

Previous studies have demonstrated the effect of regulatory fit on Japanese elementary school students (aged 9-12 years). The hypotheses were that promotion focused students tend to show good performance related to speed when they use an eager manner; on the other hand, prevention focused students tend to show good performance related to accuracy when they use a vigilant manner. In Study 1, the class teacher assessed their student's regulatory focus and then manipulated the students' manner of solving a calculation task by asking the students to engage speedily so that they solve many tasks in eager manner condition, or accurately so that they can avoid making errors in the vigilant manner condition. The results indicated that students with a promotion focus tended to solve more tasks in the eager manner condition. In addition, the same result was replicated in Study 2. These combined results suggest that regulatory fit is experienced not only by adults but also by young children. Moreover, the influence of regulatory fit depended on the type of regulatory fit. It is suggested that regulatory fit theory could be applied to educational settings to efficiently enhance the performance of students.

DUSP11 Attenuates Lipopolysaccharide-Induced Macrophage Activation by Targeting TAK1.

Dual-specificity phosphatase 11 (DUSP11, also named as PIR1) is a member of the atypical DUSP protein tyrosine phosphatase family. DUSP11 is only known to be an RNA phosphatase that regulates noncoding RNA stability. To date, the role of DUSP11 in immune cell signaling and immune responses remains unknown. In this study, we generated and characterized the immune cell functions of DUSP11-deficient mice. We identified TGF-ß-activated kinase 1 (TAK1) as a DUSP11-targeted protein. DUSP11 interacted directly with TAK1, and the DUSP11-TAK1 interaction was enhanced by LPS stimulation in bone marrow-derived macrophages. DUSP11 deficiency enhanced the LPS-induced TAK1 phosphorylation and cytokine production in bone marrow-derived macrophages. Furthermore, DUSP11-deficient mice were more susceptible to LPS-induced endotoxic shock. The LPS-induced serum levels of IL-1ß, TNF-α, and IL-6 were significantly elevated in DUSP11-deficient mice compared with those of wild-type mice. The data indicate that DUSP11 inhibits LPS-induced macrophage activation by targeting TAK1.