Effects of Potassium Deficiency on the Growth of Tea (Camelia sinensis) and Strategies for Optimizing Potassium Levels in Soil: A Critical Review

Potassium is among the three essential macronutrients for tea plants, along with nitrogen and phosphorous, and plays important roles in growth and stress response. Potassium is absorbed by plants in larger amounts than any other mineral element except nitrogen and, in some cases, calcium. At present, more than 59% of China’s tea gardens are in a state of potassium deficiency, which negatively affects tea quality and yield. This paper reviews the effects of potassium deficiency on tea plant growth and stress response, details factors affecting potassium supply and demand in tea gardens, examines the interactions between potassium and other elements in soils, and provides strategies for optimizing potassium levels in soils. Potassium is positively correlated with the elements nitrogen, copper, and zinc. Sufficient potassium dramatically improves the yield and quality of tea: it accelerates metabolism, promotes synthesis of catechins, and strengthens biotic and abiotic resistance by activating and regulating different enzymes. Moderate application of potassium fertilizers, along with potassium-solubilizing bacteria, can regulate the ratio of different forms of potassium and increase available potassium in soils of tea gardens. We suggest that research on potassium occurring in soils and its interaction with other elements be strengthened, so as to improve the efficient use of potassium fertilizers in tea gardens and maintain the balance of elements in soils

De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach

Due to their potential in the treatment of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attention. However, the existing caspase-6 inhibitors showed more or less inevitable deficiencies that restrict their clinical development and applications. Therefore, there is an urgent need to develop novel caspase-6 candidate inhibitors. Herein, a gated recurrent unit (GRU)-based recurrent neural network (RNN) combined with transfer learning was used to build a molecular generative model of caspase-6 inhibitors. The results showed that the GRU-based RNN model can accurately learn the SMILES grammars of about 2.4 million chemical molecules including ionic and isomeric compounds and can generate potential caspase-6 inhibitors after transfer learning of the known 433 caspase-6 inhibitors. Based on the novel molecules derived from the molecular generative model, an optimal logistic regression model and Surflex-dock were employed for predicting and ranking the inhibitory activities. According to the prediction results, three potential caspase-6 inhibitors with different scaffolds were selected as the promising candidates for further research. In general, this paper provides an efficient combinational strategy for de novo molecular design of caspase-6 inhibitors