Transcriptomic Analysis and Salt-Tolerance Gene Mining during Rice Germination.

Salt stress is an important environmental factor affecting crop growth and development. One of the important ways to improve the salt tolerance of rice is to identify new salt-tolerance genes, reveal possible mechanisms, and apply them to the creation of new germplasm and the breeding of new varieties. In this study, the salt-sensitive japonica variety Tong 35 (T35) and salt-tolerant japonica variety Ji Nongda 709 (JND709) were used. Salt stress treatment with a 150 mmol/L NaCl solution (the control group was tested without salt stress treatment simultaneously) was continued until the test material was collected after the rice germination period. Twelve cDNA libraries were constructed, and 5 comparator groups were established for transcriptome sequencing. On average, 9.57G of raw sequencing data were generated per sample, with alignment to the reference genome above 96.88% and alignment to guanine-cytosine (GC) content above 53.86%. A total of 16,829 differentially expressed genes were present in the five comparison groups, of which 2390 genes were specifically expressed in T35 (category 1), 3306 genes were specifically expressed in JND709 (category 2), and 1708 genes were differentially expressed in both breeds (category 3). Differentially expressed genes were subjected to gene ontology (GO), functional enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, which revealed that these genes belonged to three main classes: molecular function, cellular components, and biological processes. KEGG pathway analysis showed that the significantly enriched pathways for these differentially expressed genes included phenylpropane biosynthesis, phytohormone signaling, and the interaction of plants with pathogens. In this study, we provided a reference for studying the molecular mechanism underlying salt tolerance during germination.

Research on Rice Yield Prediction Model Based on Deep Learning.

Food is the paramount necessity of the people. With the progress of society and the improvement of social welfare system, the living standards of people all over the world are constantly improving. The development of medical industry improves people's health level constantly, and the world population is constantly climbing to a new peak. With the continuous development of deep learning in recent years, its advantages are constantly displayed, especially in the aspect of image recognition and processing, it drives into the distance. Thanks to the superiority of deep learning in image processing, the combination of remote sensing images and deep learning has attracted more attention. To simulate the four key factors of rice yield, this article tries a regression model with a combination of various characteristic independent variables. In this article, the selection of the best linear and nonlinear regression models is discussed, the prediction performance and significance of each regression model are analyzed, and some thoughts are given on estimation of actual rice yield.

Impact of reactor configuration on treatment performance and microbial diversity in treating high-strength dyeing wastewater: Anaerobic flat-sheet ceramic membrane bioreactor versus upflow anaerobic sludge blanket reactor.

In this study, an anaerobic flat-sheet ceramic membrane bioreactor (AnCMBR) was used to treat high-strength dyeing wastewater, and compared with an upflow anaerobic sludge blanket (UASB) reactor. The start-up phases of the AnCMBR and UASB reactor were accomplished within 60 d by using cultivated seed sludge. The results showed that the AnCMBR had better COD, TN, and TP removal rates than the UASB reactor. The CH4 production of the AnCMBR was higher than that of the UASB reactor. The AnCMBR was operated with low energy consumption due to good water permeability of the flat-sheet ceramic membrane. The AnCMBR and UASB reactor had similar CH4-producing Archaea; Methanosaeta, Methanosarcina, and Methanomassiliicoccus were the most abundant. The AnCMBR had a higher proportion of Desulfovibrio sp. and Desulfomicrobium sp., which are reported to have the potential to degrade reactive dyes. A large number of sulfate-reducing enzymes were deduced to contribute to the sulfate-reducing pathway.