Predicting Demands of COVID-19 Prevention and Control Materials via Co-Evolutionary Transfer Learning.

The novel coronavirus pneumonia (COVID-19) has created great demands for medical resources. Determining these demands timely and accurately is critically important for the prevention and control of the pandemic. However, even if the infection rate has been estimated, the demands of many medical materials are still difficult to estimate due to their complex relationships with the infection rate and insufficient historical data. To alleviate the difficulties, we propose a co-evolutionary transfer learning (CETL) method for predicting the demands of a set of medical materials, which is important in COVID-19 prevention and control. CETL reuses material demand knowledge not only from other epidemics, such as severe acute respiratory syndrome (SARS) and bird flu but also from natural and manmade disasters. The knowledge or data of these related tasks can also be relatively few and imbalanced. In CETL, each prediction task is implemented by a fuzzy deep contractive autoencoder (CAE), and all prediction networks are cooperatively evolved, simultaneously using intrapopulation evolution to learn task-specific knowledge in each domain and using interpopulation evolution to learn common knowledge shared across the domains. Experimental results show that CETL achieves high prediction accuracies compared to selected state-of-the-art transfer learning and multitask learning models on datasets during two stages of COVID-19 spreading in China.

Crosstalk Between the Gut Microbiota and Epithelial Cells Under Physiological and Infectious Conditions.

The gastrointestinal tract (GIT) is considered the largest immunological organ, with a diverse gut microbiota, that contributes to combatting pathogens and maintaining human health. Under physiological conditions, the crosstalk between gut microbiota and intestinal epithelial cells (IECs) plays a crucial role in GIT homeostasis. Gut microbiota and derived metabolites can compromise gut barrier integrity by activating some signaling pathways in IECs. Conversely, IECs can separate the gut microbiota from the host immune cells to avoid an excessive immune response and regulate the composition of the gut microbiota by providing an alternative energy source and releasing some molecules, such as hormones and mucus. Infections by various pathogens, such as bacteria, viruses, and parasites, can disturb the diversity of the gut microbiota and influence the structure and metabolism of IECs. However, the interaction between gut microbiota and IECs during infection is still not clear. In this review, we will focus on the existing evidence to elucidate the crosstalk between gut microbiota and IECs during infection and discuss some potential therapeutic methods, including probiotics, fecal microbiota transplantation (FMT), and dietary fiber. Understanding the role of crosstalk during infection may help us to establish novel strategies for prevention and treatment in patients with infectious diseases, such as C. difficile infection, HIV, and COVID-19.