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The effect of hydrogen bonding on the intermolecular photoinduced ICT and TICT processes for coumarin 500 has been demonstrated, and a reliable mechanism has been revealed to explain the unusual behavior of C500 in dioxane and methanol.

In C. elegans, the transcription factor skinhead-1 (SKN-1), the ortholog of human NF-E2-related factor 2 (Nrf-2), plays important roles in oxidative stress defense and aging processes. It has been documented that the activity of SKN-1 is regulated by its phosphorylation modification. However, whether other posttranslational modifications of SKN-1 affect its function remains unclear to date. Here we report, for the first time, that SKN-1 is O-GlcNAcylated at Ser470 and Thr493 by O-GlcNActransferase OGT-1. By generating the double mutations of Ser470/Thr493 in the wild type and skn-1(zu67) worms, respectively, we found that disruption of O-GlcNAc modification on SKN-1 repressed the accumulation of SKN-1 in the intestinal nuclei, and decreased the activities of SKN-1 in modulating lifespan and oxidative stress resistance. Moreover, under oxidative stress, SKN-1 was highly O-GlcNAcylated, resulting in the decrease of GSK-3-mediated phosphorylation at Ser483 adjacent to the O-GlcNAcylated residues (Ser470 and Thr493). These data suggest that O-GlcNAcylation of SKN-1 is crucial for regulating lifespan and oxidative stress resistance via the crosstalk with its phosphorylation in C. elegans. These findings have important implications for studying the functions of O-GlcNAcylation on Nrf-2 in human aging-related diseases.

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To investigate the performance of extreme gradient boosting (XGBoost) in remote sensing image classification tasks, XGBoost was first introduced and comparatively investigated for the spectral-spatial classification of hyperspectral imagery using the extended maximally stable extreme-region-guided morphological profiles (EMSER_MPs) proposed in this study. To overcome the potential issues of XGBoost, meta-XGBoost was proposed as an ensemble XGBoost method with classification and regression tree (CART), dropout-introduced multiple additive regression tree (DART), elastic net regression and parallel coordinate descent-based linear regression (linear) and random forest (RaF) boosters. Moreover, to evaluate the performance of the introduced XGBoost approach with different boosters, meta-XGBoost and EMSER_MPs, well-known and widely accepted classifiers, including support vector machine (SVM), bagging, adaptive boosting (AdaBoost), multi class AdaBoost (MultiBoost), extremely randomized decision trees (ExtraTrees), RaF, classification via random forest regression (CVRFR) and ensemble of nested dichotomies with extremely randomized decision tree (END-ERDT) methods, were considered in terms of the classification accuracy and computational efficiency. The experimental results based on two benchmark hyperspectral data sets confirm the superior performance of EMSER_MPs and EMSER_MPs with mean pixel values within region (EMSER_MPsM) compared to that for morphological profiles (MPs), morphological profile with partial reconstruction (MPPR), extended MPs (EMPs), extended MPPR (EMPPR), maximally stable extreme-region-guided morphological profiles (MSER_MPs) and MSER_MPs with mean pixel values within region (MSER_MPsM) features. The proposed meta-XGBoost algorithm is capable of obtaining better results than XGBoost with the CART, DART, linear and RaF boosters, and it could be an alternative to the other considered classifiers in terms of the classification of hyperspectral images using advanced spectral-spatial features, especially from generalized classification accuracy and model training efficiency perspectives.

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