Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks.

Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a serious threat to the quality of water bodies, the ecological environment, and human health. Therefore, there is an urgent need for a method that can accurately identify various types of agricultural organic pollution to prevent the water ecosystems in the region from significant organic pollution. In this study, a network model called RA-GoogLeNet is proposed for accurately identifying agricultural organic pollution in the river network area of the Jiangnan Plain. RA-GoogLeNet uses fluorescence spectral data of agricultural non-point source water quality in Changzhou Changdang Lake Basin, based on GoogLeNet architecture, and adds an efficient channel attention (ECA) mechanism to its A-Inception module, which enables the model to automatically learn the importance of independent channel features. ResNet are used to connect each A-Reception module. The experimental results show that RA-GoogLeNet performs well in fluorescence spectral classification of water quality, with an accuracy of 96.3%, which is 1.2% higher than the baseline model, and has good recall and F1 score. This study provides powerful technical support for the traceability of agricultural organic pollution.

Identification of differentially expressed genes involved in spore germination of Penicillium expansum by comparative transcriptome and proteome approaches.

In this study, Penicillium expansum, a common destructive phytopathogen and patulin producer was isolated from naturally infected apple fruits and identified by morphological observation and rDNA-internal transcribed spacer analysis. Subsequently, a global view of the transcriptome and proteome alteration of P. expansum spores during germination was evaluated by RNA-seq (RNA sequencing) and iTRAQ (isobaric tags for relative and absolute quantitation) approaches. A total of 3,026 differentially expressed genes (DEGs), 77 differentially expressed predicted transcription factors and 489 differentially expressed proteins (DEPs) were identified. The next step involved screening out 130 overlapped candidates through correlation analysis between the RNA-seq and iTRAQ datasets. Part of them showed a different expression trend in the mRNA and protein levels, and most of them were involved in metabolism and genetic information processing. These results not only highlighted a set of genes and proteins that were important in deciphering the molecular processes of P. expansum germination but also laid the foundation to develop effective control methods and adequate environmental conditions.