Dexamethasone affects the chemotaxis and phagocytic activity of neutrophils for boar spermatozoa and the quality of liquid preserved boar semen in vitro.

This study aimed to investigate the effect of dexamethasone (DEX) on chemotaxis and phagocytic activity of neutrophils for spermatozoa and semen quality of preserved boar semen. The different concentrations of dexamethasone were added to boar semen dilutions to detect its effects on the chemotaxis of neutrophils and phagocytosis of neutrophils and sperm motility sperm malformation rate, plasma membrane integrity, mitochondrial activity, and spermatozoa motility parameters. The study results showed that the experimental groups of DEX significantly inhibited the phagocytosis and chemotaxis of PMNs for spermatozoa.

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.

Tridirectional Transfer Learning for Predicting Gastric Cancer Morbidity.

Our previous study has constructed a deep learning model for predicting gastrointestinal infection morbidity based on environmental pollutant indicators in some regions in central China. This article aims to adapt the prediction model for three purposes: 1) predicting the morbidity of a different disease in the same region; 2) predicting the morbidity of the same disease in a different region; and 3) predicting the morbidity of a different disease in a different region. We propose a tridirectional transfer learning approach, which achieves the abovementioned three purposes by: 1) developing a combined univariate regression and multivariate Gaussian model for establishing the relationship between the morbidity of the target disease and that of the source disease together with the high-level pollutant features in the current source region; 2) using mapping-based deep transfer learning to extend the current model to predict the morbidity of the source disease in both source and target regions; and 3) applying the pattern of the combined model in the source region to the extended model to derive a new combined model for predicting the morbidity of the target disease in the target region.

Generative adversarial network based telecom fraud detection at the receiving bank.

Recently telecom fraud has become a serious problem especially in developing countries such as China. At present, it can be very difficult to coordinate different agencies to prevent fraud completely. In this paper we study how to detect large transfers that are sent from victims deceived by fraudsters at the receiving bank.

[Study on HPLC fingerprints of Phyllanthus urinaria].

Objective: To establish the HPLC fingerprints of Phyllanthus urinaria L.

Methods: The HPLC was used to establish the fingerprint of Phyllanthus urinaria.

Results: The HPLC fingerprint profiles of Phyllanthus urinaria contains 10 common peaks.