The occurrence of cancer entails a series of genetic mutations that favor uncontrollable tumor growth. It is believed that various factors collectively contribute to cancer, and there is no one single explanation for tumorigenesis. Epigenetic changes such as the dysregulation of enzymes modifying DNA or histones are actively involved in oncogenesis and inflammatory response. The methylation of lysine residues on histone proteins represents a class of post-translational modifications. The human Jumonji C domain-containing (JMJD) protein family consists of more than 30 members. The JMJD proteins have long been identified with histone lysine demethylases (KDM) and histone arginine demethylases activities and thus could function as epigenetic modulators in physiological processes and diseases. Importantly, growing evidence has demonstrated the aberrant expression of JMJD proteins in cancer and inflammatory diseases, which might serve as an underlying mechanism for the initiation and progression of such diseases. Here, we discuss the role of key JMJD proteins in cancer and inflammation, including the intensively studied histone lysine demethylases, as well as the understudied group of JMJD members. In particular, we focused on epigenetic changes induced by each JMJD member and summarized recent research progress evaluating their therapeutic potential for the treatment of cancer and inflammatory diseases.

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To perform accurate and synchronous detection of the soluble solid contents (SSC) in fresh jujubes at different stages of maturity, hyperspectral imaging was used to establish robust models. The combined data constituting four maturation stages were used to build the grid-search least squares support vector machine (GS-LS-SVM) model. The determination coefficient (Rp2), the root-mean-square error (RMSEP), and the residual predictive deviation (RPD) of the prediction set for samples of the overall stages were 0.98, 1.10%, and 7.85, respectively. Furthermore, a successive projections algorithm (SPA) was used to extract the characteristic wavelengths of the combined data. An artificial bee colony (ABC) algorithm (for the prediction set, Rp2 = 0.98, RMSEP = 1.19%, RPD = 7.25) was used to improve the SPA-LS-SVM model, which was better than the SPA-GS-LS-SVM model (for the prediction set, Rp2 = 0.98, RMSEP = 1.24%, RPD = 6.96). Lastly, visualization of the SSC distribution map was performed based on the SPA-ABC-LS-SVM model, which clearly showed that the SSC gradually increased during maturation. The results indicated that it was realistic to construct a detection model of the multimaturity stage. This research also demonstrated that the combination of hyperspectral imaging and the ABC had good application values in the testing of agricultural products.

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This study revealed the effect of the use of different temperature and calcium chloride (CaCl2) treatments on the storage quality of fresh-cut “Xuebai” cauliflowers. Fresh-cut “Xuebai” cauliflowers were soaked with 2% CaCl2 solution at different temperatures. The change in the firmness, color, and ascorbic acid (ASA), total glucosinolates (TGLS), polygalacturonase (PG), and lipoxygenase (LOX) content of fresh-cut “Xuebai” cauliflowers during the cold storage period was assessed. In addition, the sensory quality was also evaluated. The results show that the combined treatments with CaCl2 at different temperatures could effectively maintain the storage quality of fresh-cut “Xuebai” cauliflowers. Then, a method based on factor analysis with comprehensive quality evaluation was proposed. A factor analysis with a principal component analysis (PCA) was conducted on nine indicators of cauliflowers. Two principal components were extracted with a cumulative contribution rate of 97.513%. The results demonstrated that the treatment with the best fresh-keeping effect of cauliflowers in storage was the combination treatment at 40 °C with 2% CaCl2 solution, while the optimal storage period was 15 days.

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