Ligustri lucidi fructus (LLF) is the fruits of Ligustrum lucidum Ait. (Oleaceae). This review based on nearly 80 literary sources discusses the knowledge of chemistry and biological effects of this species. Several types of chemical constituents considered as the characteristic and active constituents from LLF were isolated including 40 triterpenoids, 48 iridoids, 10 flavones, 10 phenylethanoid glycosides and others. Various extracts and individual compounds derived from this species have been found to possess a variety of pharmacological effects, e.g. anti-tumour, hepatoprotective, immune regulating, antioxidative and anti-ageing effects, anti-inflammation and reducing hypercholesterolaemia effects and so on. The results of data analysis on the chemical, pharmacological characteristics of LLF support the view that this species has many therapeutic properties and indicate its potential as an effective herbal remedy. Finally, some suggestions for further research on chemical and pharmacological properties are given in this review. Theoretical basis was given for further exploiting and utilising LLF.

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The main goal of this study is to produce a landslide susceptibility map in the Wanzhou section of the Three Gorges reservoir area (China) with a weighted gradient boosting decision tree (weighted GBDT) model. According to the current research on landslide susceptibility mapping (LSM), the GBDT method is rarely used in LSM. Furthermore, previous studies have rarely considered the imbalance of landslide samples and simply regarded the LSM problem as a binary classification problem. In this paper, we considered LSM as an imbalanced learning problem and obtained a better predictive model using the weighted GBDT method. The innovations of the article mainly include the following two points: introducing the GBDT model into the evaluation of landslide susceptibility; using the weighted GBDT method to deal with the problem of landslide sample imbalance. The logistic regression (LR) model and gradient boosting decision tree (GBDT) model were also used in the study to compare with the weighted GBDT model. Five kinds of data from different data source were used in the study: geology, topography, hydrology, land cover, and triggered factors (rainfall, earthquake, land use, etc.). Twenty nine environmental parameters and 233 landslides were used as input data. The receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC) value, and the recall value were used to estimate the quality of the weighted GBDT model, the GBDT model, and the LR model. The results showed that the GBDT model and the weighted GBDT model had a higher AUC value (0.977, 0.976) than the LR model (0.845); the weighted GBDT model had a little higher AUC value (0.977) than the GBDT model (0.976); and the weighted GBDT model had a higher recall value (0.823) than the GBDT model (0.426) and the LR model (0.004). The weighted GBDT method could be considered to have the best performance considering the AUC value and the recall value in landslide susceptibility mapping dealing with imbalanced landslide data.

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