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Structural damage detection is crucial for maintaining the health and safety of buildings. However, achieving high accuracy in damage detection remains challenging, especially in noisy environments. To improve the accuracy and noise robustness of damage detection, this study proposes a novel method that combines the Conformer model and the dual-channel pseudo-supervised (DCPS) learning strategy for structural damage detection. The DCPS learning strategy improves the stability and accuracy of the model in noisy environments. It enables the model to input acceleration signals with different noise levels into each branch of the dual-channel network, thereby learning noise-robust features. The Conformer model, as the backbone network, integrates the advantages of convolutional neural networks (CNNs) and Transformers to effectively extract both local and global features from acceleration signals. The proposed method is validated using a four-story single-span steel-frame building model and the IASC-ASCE simulated benchmark structure. The results show that the proposed method achieves a higher classification accuracy than existing structural damage detection methods. Compared to the single Conformer-based method, this method improves the accuracy by 1.57% and 4.93% for the two validation structures, respectively. Moreover, the proposed method benefits from the DCPS learning strategy's ability to achieve superior noise robustness compared to other methods. The proposed method holds potential value for improving the accuracy of damage detection and noise robustness in scenarios such as maintenance and extreme events. [ABSTRACT FROM AUTHOR]

Structural damage detection is of significance for maintaining the structural health. Currently, data-driven deep learning approaches have emerged as a highly promising research field. However, little progress has been made in studying the relationship between the global and local information of structural response data. In this paper, we have presented an innovative Convolutional Enhancement and Graph Features Fusion in Transformer (CGsformer) network for structural damage detection. The proposed CGsformer network introduces an innovative approach for hierarchical learning from global to local information to extract acceleration response signal features for structural damage representation. The key advantage of this network is the integration of a graph convolutional network in the learning process, which enables the construction of a graph structure for global features. By incorporating node learning, the graph convolutional network filters out noise in the global features, thereby facilitating the extraction to more effective local features. In the verification based on the experimental data of four-story steel frame model experiment data and IASC-ASCE benchmark structure simulated data, the CGsformer network achieved damage identification accuracies of 92.44% and 96.71%, respectively. It surpassed the existing traditional damage detection methods based on deep learning. Notably, the model demonstrates good robustness under noisy conditions. [ABSTRACT FROM AUTHOR]

Aim: This article aimed to find appropriate pancreatic cancer (PC) patients to treat with Gemcitabine with better survival outcomes by detecting hENT1 levels. Methods: We collected surgical pathological tissues from PC patients who received radical surgery in our hospital from September 2004 to December 2014. A total of 375 PC tissues and paired adjacent nontumor tissues were employed for the construction of 4 tissue microarrays (TMAs). The quality of the 4 TMAs was examined by HE staining. We performed immunohistochemistry analysis to evaluate hENT1 expression in the TMAs. Moreover, we detected hENT1 expression level and proved the role of hENT1 in cell proliferation, drug resistance, migration and invasion in vivo and vitro. Results: The results indicated that low hENT1 expression indicated a significantly poor outcome in PC patients, including shortened DFS (21.6± 2.8 months versus 36.9± 4.0 months, p< 0.001) and OS (33.6± 3.9 versus 39.6± 3.9, p=0.004). Meanwhile, patients in stage I/II of TNM stage had a longer OS (40.2± 3.4 versus 15.4± 1.7, p=0.002) and DFS (31.0± 3.1 versus 12.4± 1.9, p=0.016) than patients in stage III/IV. Patients in M0 stage had a longer OS (39.7± 3.4 versus 16.2± 1.9, p=0.026) and DFS(30.7± 3.0 versus 11.8± 2.2, p=0.031) than patients in M1 stage, and patients with tumors not invading the capsule had a better DFS than those with tumor invasion into the capsule (30.8± 3.0 versus 12.6± 2.3, p=0.053). Patients with preoperative CA19-9 values ≤ 467 U/mL have longer DFS than that of patients who had preoperative CA19-9 values > 467 U/mL (37.9± 4.1 versus 22.9± 4.0, p=0.04). In the subgroup analysis, a high hENT1 expression level was related to a longer OS(39.4± 4.0 versus 31.5± 3.9, p=0.001) and DFS(35.7± 4.0 versus 20.6± 2.7; p< 0.0001) in the Gemcitabine subgroup. Conclusion: PC patients with high hENT1 expression have a better survival outcomes when receiving Gemcitabine. hENT1 expression can be a great prognostic indicator for PC patients to receive Gemcitabine treatment. [ABSTRACT FROM AUTHOR]

Toxoplasma gondii ( T. gondii) is one of the most successful intracellular protozoa in that it can infect the majority of mammalian cell types during the acute phase of infection. Furthermore, it is able to establish a chronic infection for the host's entire lifespan by developing an encysted parasite form, primarily in the muscles and brain of the host, to avoid the host immune system. The infection affects one third of the world population and poses an increased risk for people with a suppressed immune system. Despite the dormant characteristics of chronic T. gondii infection, there is much evidence suggesting that this infection leads to specific behavior changes in both humans and rodents. Although numerous hypotheses have been put forth, the exact mechanisms underlying these behavior changes have yet to be understood. In recent years, several studies revealed a strong connection between the gut microbiome and the different organ systems that are affected in T. gondii infection. While it is widely studied and accepted that acute T. gondii infection can lead to a dramatic disruption of the host's normal, well-balanced microbial ecosystem (microbial dysbiosis), changes in the gut microbiome during the chronic stage of infection has not been well characterized. This review is intended to briefly inspect the different hypotheses that attempt to explain the behavior changes during T. gondii infection. Furthermore, this review proposes to consider the potential link between gut microbial dysbiosis, and behavior changes in T. gondii infection as a novel way to describe the underlying mechanism., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Prandovszky, Severance, Splan, Liu, Xiao and Yolken.)