Duckweeds are floating plants of the family Lemnaceae, comprising 5 genera and 36 species. They typically live in ponds or lakes and are found worldwide, except the polar regions. There are two duckweed subfamilies—namely Lemnoidea and Wolffioideae, with 15 and 21 species, respectively. Additionally, they have characteristic reproduction methods. Several metabolites have also been reported in various duckweeds. Duckweeds have a wide range of adaptive capabilities and are particularly suitable for experiments requiring high productivity because of their speedy growth and reproduction rates. Duckweeds have been studied for their use as food/feed resources and pharmaceuticals, as well as for phytoremediation and industrial applications. Because there are numerous duckweed species, culture conditions should be optimized for industrial applications. Here, we review and summarize studies on duckweed species and their utilization, metabolites, and cultivation methods to support the extended application of duckweeds in future.

Rice (Oryza sativa L.) is a widely consumed food source, and its geographical origin has long been a subject of discussion. In our study, we collected 44 and 20 rice samples from different regions of the Republic of Korea and China, respectively, of which 35 and 29 samples were of white and brown rice, respectively. These samples were analyzed using nuclear magnetic resonance (NMR) spectroscopy, followed by analyses with various data normalization and scaling methods. Then, leave-one-out cross-validation (LOOCV) and external validation were employed to evaluate various machine learning algorithms. Total area normalization, with unit variance and Pareto scaling for white and brown rice samples, respectively, was determined as the best pre-processing method in orthogonal partial least squares–discriminant analysis. Among the various tested algorithms, support vector machine (SVM) was the best algorithm for predicting the geographical origin of white and brown rice, with an accuracy of 0.99 and 0.96, respectively. In external validation, the SVM-based prediction model for white and brown rice showed good performance, with an accuracy of 1.0. The results of this study suggest the potential application of machine learning techniques based on NMR data for the differentiation and prediction of diverse geographical origins of white and brown rice.

Background: Tumor necrosis factor (TNF) antagonists are recommended for patients with ulcerative colitis (UC) for the effectiveness in inducing and maintaining clinical remission. We investigated the altered fecal metabolites and lipids by anti-TNF treatment and prediction model of remission in patients with UC. Methods: A prospective, observational multicenter study was conducted at 17 academic hospitals in Korea. Fecal samples were collected from adult patients with moderately to severely active UC (n=116) before and after 8 and 56 weeks of adalimumab treatment and from healthy controls (HC, n=37). Clinical remission was assessed using Mayo score. Metabolome and lipidome analyses were performed using gas chromatography-, and nano electro spray ionization-mass spectrometry, respectively. Prediction models of remission were developed using baseline fecal samples by Fourier transform-infrared (FT-IR) spectroscopy combined with machine learning algorithms. Results: Fecal metabolites and lipids in UC were different from HC at baseline and were changed similarly to HC during treatment. Fecal metabolites and lipids in remitters (RM) after treatment were more grouped and clustered with those of HC compared with non-remitters (NRM). In RM, 2-aminobutyric acid, galactose and dodecanoate levels which were previously decreased at baseline compared to HC increased to the levels of HC, whereas benzoate, stigmasterol, 3-hydroxybutyrate, diacylglycerol and triacylglycerol levels which were previously increased at baseline compared to HC decreased to the levels of HC after 56 weeks of treatment. The best model predicting short-term remission was developed by applying logistic regression (LR) and radial basis functions (rbf) support vector machine (SVM) with an accuracy of 0.99 (95% confidence interval [CI], 0.98–1.01). For long-term remission, the best prediction model was developed by rbf-SVM revealing 0.99 [CI 0.98–1.01]. LR and K-nearest neighbors also showed excellent performance for prediction of long-term remission (accuracy of 0.96 [CI 0.90–1.02] and 0.96 [CI 0.92–1.00], respectively. Conclusion: Fecal characteristics in UC were changed after anti-TNF treatment and became similar to those of HC. Potential therapeutic target compounds were suggested to develop novel therapeutic strategies for UC. Novel remission prediction models by FT-IR spectroscopy were also established.