Integrated proteome and pangenome analysis revealed the variation of microalga Isochrysis galbana and associated bacterial community to 2,6-Di-tert-butyl-p-cresol (BHT) stress.

The phenolic antioxidant 2,6-Di-tert-butyl-p-cresol (BHT) has been detected in various environments and is considered a potential threat to aquatic organisms. Algal-bacterial interactions are crucial for maintaining ecosystem balance and elemental cycling, but their response to BHT remains to be investigated. This study analyzed the physiological and biochemical responses of the microalga Isochrysis galbana and the changes of associated bacterial communities under different concentrations of BHT stress.

The Growth Inhibition of Polyethylene Nanoplastics on the Bait-Microalgae Based on the Transcriptome Analysis.

The adverse effects of microplastics on microalgae species have been extensively studied, but their impact on the bait microalgae entering the food chain has not been well understood. This study investigated the cytological and physiological response of to polyethylene microplastics (PE-MPs, 10 μm) and nanoplastics (PE-NPs, 50 nm). The results showed that PE-MPs had no significant impact on , while PsE-NPs obviously inhibited cell growth, reduced chlorophyll content, and caused a decline in carotenoids and soluble protein.

Age-Stratified COVID-19 Spread Analysis and Vaccination: A Multitype Random Network Approach.

The risk of severe illness and mortality from COVID-19 significantly increases with age. As a result, age-stratified modeling for COVID-19 dynamics is the key to study how to reduce hospitalizations and mortality from COVID-19. By taking advantage of network theory, we develop an age-stratified epidemic model for COVID-19 in complex contact networks.

Efficient Privacy-preserving Machine Learning in Hierarchical Distributed System.

With the dramatic growth of data in both amount and scale, distributed machine learning has become an important tool for the massive data to finish the tasks as prediction, classification, etc. However, due to the practical physical constraints and the potention privacy leakage of data, it is infeasible to aggregate raw data from all data owners for the learning purpose. To tackle this problem, the distributed privacy-preserving learning approaches are introduced to learn over all distributed data without exposing the real information.