The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

Traditional Chinese medicine (TCM) herbal formulae can be valuable therapeutic strategies and drug discovery resources. However, the active ingredients and action mechanisms of most TCM formulae remain unclear. Therefore, the identification of potent ingredients and their actions is a major challenge in TCM research. In this study, we used a network pharmacology approach we previously developed to help determine the potential antidiabetic ingredients from the traditional Ge-Gen-Qin-Lian decoction (GGQLD) formula. We predicted the target profiles of all available GGQLD ingredients to infer the active ingredients by clustering the target profile of ingredients with FDA-approved antidiabetic drugs. We also applied network target analysis to evaluate the links between herbal ingredients and pharmacological actions to help explain the action mechanisms of GGQLD. According to the predicted results, we confirmed that a novel antidiabetic ingredient from Puerariae Lobatae radix (Ge-Gen), 4-Hydroxymephenytoin, increased the insulin secretion in RIN-5F cells and improved insulin resistance in 3T3-L1 adipocytes. The network pharmacology strategy used here provided a powerful means for identifying bioactive ingredients and mechanisms of action for TCM herbal formulae, including Ge-Gen-Qin-Lian decoction.

Gambogic acid (GA) is a caged xanthone that is derived from Garcinia hanburyi and functions as a strong apoptotic inducer in many types of cancer cells. The distinct effectiveness of GA has led to its characterization as a novel anti-cancer agent. There is an increasing number of research studies focused on elucidating the molecular mechanisms of GA-induced anti-cancer effects, and several critical signaling pathways have been reported to be influenced by GA treatment. In this review, we summarize the multiple functional effects of GA administration in cancer cells including the induction of apoptosis, the inhibition of proliferation and the prevention of cancer metastasis and tumor angiogenesis.