Background: High intake of sugar-sweetened beverages has been linked to a range of physical, psychological, and emotional issues. Although there were various factors influencing sugar-sweetened beverage intake, the relationship between body esteem and sugar-sweetened beverage intake remains unclear. This study aimed to investigate the association between three dimensions of body esteem (body esteem-appearance, body esteem-attribution, and body esteem-weight) and the likelihood of high sugar-sweetened beverage intake.
View Article and Find Full Text PDFThe aim of present study was to evaluate the impact of perimenopause on insulin resistance. Specifically, insulin sensitivity was assessed in a perimenopausal mouse model treated with 4-vinylcyclohexene diepoxide (VCD), together with the changes in exosomal miRNA and hepatic mRNA expression profiles. Homeostasis model assessment of insulin resistance (HOMA-IR) was utilized to assess the status of insulin resistance, and insulin action was evaluated during menopausal transition.
View Article and Find Full Text PDFGenes (Basel)
November 2021
Transcription factors are key molecules in the regulation of gene expression in all organisms. The transcription factor LEAFY COTYLEDON 2 (LEC2), which belongs to the DNA-binding protein family, contains a B3 domain. The transcription factor is involved in the regulation of important plant biological processes such as embryogenesis, somatic embryo formation, seed storage protein synthesis, fatty acid metabolism, and other important biological processes.
View Article and Find Full Text PDFThe promoter of the Arabidopsis thaliana β-glucosidase 19 gene directs GUS expression in a seed-specific manner in transgenic Arabidopsis and tobacco. In the present study, an 898-bp putative promoter of the Arabidopsis β-glucosidase 19 (AtBGLU19) gene was cloned. The bioinformatics analysis of the cis-acting elements indicated that this putative promoter contains many seed-specific elements, such as RY elements.
View Article and Find Full Text PDFIt is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm.
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