Publications by authors named "Yuliang Gu"

Semi-supervised image segmentation has attracted great attention recently. The key is how to leverage unlabeled images in the training process. Most methods maintain consistent predictions of the unlabeled images under variations (e.

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Objective: An investigation into whether Thunder-Fire Moxibustion improves Meibomian Gland Dysfunction by activating peroxisome proliferator-activated receptor gamma (PPARγ)-related signaling pathway.

Methods: C57BL/6 mice were randomly divided into a Control Group (CG), model group (MG), Experimental Group (EG), Treatment Group (TG), and GW9662 (GW), with 10 mice in each group. The obstruction of the meibomian gland opening, tear film rupture time, and corneal fluorescein sodium staining were observed.

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Purely data-driven deep neural networks (DNNs) applied to physical engineering systems can infer relations that violate physics laws, thus leading to unexpected consequences. To address this challenge, we propose a physics-knowledge-enhanced DNN framework called Phy-Taylor, accelerating learning-compliant representations with physics knowledge. The Phy-Taylor framework makes two key contributions; it introduces a new architectural physics-compatible neural network (PhN) and features a novel compliance mechanism, which we call physics-guided neural network (NN) editing.

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Article Synopsis
  • - Accurate prediction of protein-ligand binding affinity is essential for drug design, but current methods fail to consider crucial edge information and have limitations in capturing binding interactions due to small datasets.
  • - The new method, SEGSA_DTA, utilizes SuperEdge Graph convolution and a multi-supervised attention module to enhance the prediction accuracy by effectively incorporating both node and edge data.
  • - SEGSA_DTA demonstrates better performance than existing techniques and is also applied to find potential COVID-19 treatments from FDA-approved drugs, while providing interpretable results through SHAP analysis for better lead optimization.
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Background: Labor values are important components of the individual value system and considered to be among the most important values of an individual, especially in China. In studies of values, childhood maltreatment is considered to have an important influence on the formation of individual values. However, there is no previous research about the relationship between childhood maltreatment and labor values.

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To explore the positive and negative effects of labor values on mental health from the aspects of life satisfaction and psychological distress, and further verify the mediating role of social support. A total of 2,691 primary and secondary school students were surveyed by Labor Values Scale, the Multidimensional Scale of Social Support, General Health Questionnaire and Satisfaction with Life Scale, and the results of which showed that as: (1) labor values can positively predict life satisfaction, while they are negatively correlated with psychological distress; (2) social support can play a mediating role between labor values and life satisfaction; and (3) social support can also play a mediating role in the relationship between labor values and psychological distress. This study revealed that the specific path and mechanism of labor values on mental health.

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This study examined the mediating role of altruistic tendency in the association between labor values and subjective well-being (SWB). About 2,691 Chinese students (1,504 males and 1,187 females) completed the labor values scale (LVS), the Positive Affect and Negative Affect Scale, the Satisfaction With Life Scale, and the altruistic tendency scale. Results demonstrated that labor values were positively associated with life satisfaction and positive affect, while negatively with negative affect.

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Background: Drug-drug interaction (DDI) is a serious public health issue. The L1000 database of the LINCS project has collected millions of genome-wide expressions induced by 20,000 small molecular compounds on 72 cell lines. Whether this unified and comprehensive transcriptome data resource can be used to build a better DDI prediction model is still unclear.

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In this study, the major factors affecting sonolytic degradation of sulfamethazine (SMT), a typical pharmaceutically active compound, in water were evaluated. The factors tested included two operational parameters (i.e.

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Photochemical degradation of fluoroquinolone ciprofloxacin (CIP) in water by UV and UV/H₂O₂ were investigated. The degradation rate of CIP was affected by pH, H₂O₂ dosage, as well as the presence of other inorganic components. The optimized pH value and H₂O₂ concentration were 7.

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