DNASimCLR: a contrastive learning-based deep learning approach for gene sequence data classification.

Background: The rapid advancements in deep neural network models have significantly enhanced the ability to extract features from microbial sequence data, which is critical for addressing biological challenges. However, the scarcity and complexity of labeled microbial data pose substantial difficulties for supervised learning approaches. To address these issues, we propose DNASimCLR, an unsupervised framework designed for efficient gene sequence data feature extraction.

High-Temperature Resistance of Modified Potassium Magnesium Phosphate Cement.

To study the high-temperature mechanical properties of potassium magnesium phosphate cement mortar and the high-temperature resistance of its laminates. Potassium magnesium phosphate cement (MKPC) was prepared by using heavy-burning magnesium oxide and potassium dihydrogen phosphate as the main raw materials, borax as the retarder, and compounded with a certain amount of fly ash and silica fume. The effect of the mass ratio of magnesium to phosphorus (M:P), compounded fly ash and silica fume on the setting time and mechanical properties of MKPC was investigated.