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To explore the relationship between consumer expectation and repurchase intention, four variables that may affect repurchase intention, including price discount, waiting time, brand image and clothing product design were proposed. Clothing quality perception and value perception were considered as intermediary variables, and a theoretical model was constructed. Empirical analysis was conducted by using regression analysis, structural equation model, and Bootstrap test. The results show that price discount, waiting time, brand image and clothing product design have positive influence on repurchase intention. The clothing quality perception and clothing value perception play an intermediary role between consumer expectation difference and repurchase intention. The results provide a theoretical reference for clothing brand to improve positive expectations of consumers. [ABSTRACT FROM AUTHOR]

(1) Background: Anthocyanins, the main pigments in plants, are influenced by both temperature and photoperiod. However, the specific mechanisms underlying anthocyanin accumulation in tea plants in response to these two environmental factors remain unclear. (2) Methods: This study subjected potted 'Ziyan' tea plants to four types of day-length and temperature treatments (HL (28 °C, 16 h), HS (28 °C, 8 h), LL (18 °C, 16 h), and LS (18 °C, 8 h)), and then conducted targeted metabolomic and transcriptomic analyses of the samples. (3) Results: Long photoperiods and low temperatures both promoted anthocyanin accumulation in the new shoots of the tea plants. Furthermore, the enhancing effects of these two environmental factors on anthocyanin accumulation are additive and exhibit interactive effects. Through a combined analysis of metabolomics and transcriptomics, five key differentially accumulated metabolites (DAMs) and twenty-two key differentially expressed genes (DEGs) were identified, the latter being found to participate in the regulation of anthocyanin biosynthesis pathways under varying light and temperature conditions. In summary, extended photoperiods primarily increase the content levels of ten metabolites, including cyanidin and naringenin-7-O-glucoside, by upregulating CHS, F3H, and ANS genes. In contrast, low temperatures primarily enhance the synthesis of seven anthocyanins, including cyanidin and cyanidin-3-O-rutinoside, by upregulating the ANS and UFGT genes. (4) Conclusions: Collectively, the differences in the expression levels of CHS, F3H, ANS, and UFGT might be responsible for 'Ziyan' tea plants' purple shoot coloration, providing important data towards the discovery of candidate genes and molecular bases controlling the purple leaves of these tea plants under varied photoperiods and temperatures. [ABSTRACT FROM AUTHOR]

Nitrogen (N) is a key regulator in the growth of tea plants and the synthesis of amino acids (AAs) and related secondary metabolites, thus affecting the yield and quality of tea leaves. Increased N fertilization significantly improves the yield and quality of tea plants, but applying excess nitrogen wastes resources and causes pollution problems. Herein, we employed morphological, physiological, metabolomic, and RNA-seq methods to study the response of 'Ziyan' roots to high N. High N supply induced an increase in arginine (Arg), asparagine (Asn), and glutamine (Gln) in roots and simultaneously decreased sucrose, polyphenols, and caffeine contents. High N reduced the length, volume, number, and activity of the roots by 10.63%, 25.00%, 26.95%, and 14.50%, respectively, which inhibited 'Ziyan' root growth, probably by disturbing the regulation of carbon and nitrogen metabolism in the tea plant. According to the Kyoto Encyclopedia of Genes and Genome (KEGG) enrichment analysis, AAs, flavonoids, and flavonol-related pathways were relatively active after supplying high N. In addition, the transcriptome analysis identified NRT1/PTR and GOGAT as key genes, and the transcription factors (e.g., AP2/ERF, MYB, and WRKY) and auxins were actively involved in the high N stress response of 'Ziyan' roots. These findings will help us understand the adaptive mechanism of high N supply in tea tree roots and provide a reference for guiding the fertilization of 'Ziyan' tea plants. [ABSTRACT FROM AUTHOR]

Identifying the potential compound–protein interactions (CPIs) plays an essential role in drug development. The computational approaches for CPI prediction can reduce time and costs of experimental methods and have benefited from the continuously improved graph representation learning. However, most of the network-based methods use heterogeneous graphs, which is challenging due to their complex structures and heterogeneous attributes. Therefore, in this work, we transformed the compound–protein heterogeneous graph to a homogeneous graph by integrating the ligand-based protein representations and overall similarity associations. We then proposed an Inductive Graph AggrEgator-based framework, named CPI-IGAE, for CPI prediction. CPI-IGAE learns the low-dimensional representations of compounds and proteins from the homogeneous graph in an end-to-end manner. The results show that CPI-IGAE performs better than some state-of-the-art methods. Further ablation study and visualization of embeddings reveal the advantages of the model architecture and its role in feature extraction, and some of the top ranked CPIs by CPI-IGAE have been validated by a review of recent literature. The data and source codes are available at https://github.com/wanxiaozhe/CPI-IGAE. [ABSTRACT FROM AUTHOR]

Lung cancer is the leading cause of cancer deaths worldwide, with a high morbidity and less than 20% survival rate. Therefore, new treatment strategies and drugs are needed to reduce the mortality of patients with lung cancer. α7 nicotinic acetylcholine receptor (α7 nAChR), as a receptor of nicotine and its metabolites, is a potential target for lung cancer treatment. Our previous studies revealed that sinomenine plays anti‐inflammation roles via α7 nAChR and down‐regulates the expression of this receptor, thus increasing the inflammatory response. Hence, sinomenine is possibly a natural ligand of this receptor. In the present study, the effects of sinomenine on lung cancer A549 cells and tumor‐bearing mice were determined to investigate whether this alkaloid has an inhibitory effect on lung cancer via α7 nAChR. CCK‐8 assay, wound‐healing test, and flow cytometry were performed for cell proliferation, cell migration, and apoptosis analysis in vitro, respectively. Xenograft mice were used to evaluate the effects of sinomenine in vivo. Results showed that sinomenine decreased cell proliferation and migration abilities but increased the percentage of apoptotic cells. Tumor volume in tumor‐bearing mice was significantly reduced after sinomenine treatment compared with that in the vehicle group mice (p < 0.05). Furthermore, the effects of sinomenine were abolished by the α7 nAChR antagonist mecamylamine and the allosteric modulator PNU‐120596, but no change occurred when the mice were pretreated with the muscarinic acetylcholine receptor antagonist atropine. Meanwhile, sinomenine suppressed α7 nAChR expression in vitro and in vivo, as well as the related signaling molecules pERK1/2 and ERK1/2 and the transcription factors TTF‐1 and SP‐1. By contrast, sinomenine up‐regulated the expression of another transcription factor, Egr‐1. These effects were restricted by mecamylamine and PNU but not by atropine. Results suggested that sinomenine can inhibit lung cancer via α7 nAChR in a negative feedback mode. [ABSTRACT FROM AUTHOR]