The Dark Side of Microbial Processes: Accumulation of Nitrate During Storage of Surface Water in the Dark and the Underlying Mechanism

Microbial communities play an essential role in maintaining a healthy aquatic ecosystem. For example, in surface water reservoirs, microorganisms produce oxygen, break down toxic contaminants, and remove excess nitrogen.

Transcriptome Analysis of Walnut Seedling Roots Under Nitrogen Starvation and Excess Nitrogen Stress

Nitrogen is an essential core element in walnut seedling growth and development. However, nitrogen starvation and excessive nitrogen stress can cause stunted growth and development of walnut seedlings, and environmental pollution is also of concern. Therefore, it is necessary to study the mechanism of walnut seedling resistance to nitrogen stress. In this study, morphological and physiological observations and transcriptome sequencing of walnut seedlings under nitrogen starvation and excess nitrogen stress were performed. The results showed that walnut seedlings under nitrogen starvation and excess stress could adapt to the changes in the nitrogen environment by changing the coordination of their root morphology and physiological indexes. Based on an analysis of transcriptome data, 4911 differential genes (DEGs) were obtained (2180 were upregulated and 2731 were downregulated) in a comparison of nitrogen starvation and control groups. A total of 9497 DEGs (5091 upregulated and 4406 downregulated) were obtained in the comparison between the nitrogen overdose and control groups. When these DEGs were analysed, the differential genes in both groups were found to be significantly enriched in the plant’s circadian pathway. Therefore, we selected the circadian rhythm as the core focus for further analysis. We made some discoveries by analysing the gene co-expression network of nitrogen metabolism, circadian rhythm, and hormone signal transduction. (a) Nitrite nitrogen (NO2-) or Glu may act as a nitrogen signal to the circadian clock. (b) Nitrogen signalling may be input into the circadian clock by regulating changes in the abundance of the CRY1 gene. (c) After the nitrogen signal enters the circadian clock, the expression of the LHY gene is upregulated, which causes a phase shift in the circadian clock. (d) The RVE protein may send information about the circadian clock’s response to nitrogen stress back to the nitrogen metabolic pathway via the hormone transduction pathway. In conclusion, various metabolic pathways in the roots of walnut seedlings coordinated with one another to resist the ill effects of nitrogen stress on the root cells, and these coordination relationships were regulated by the circadian clock. This study is expected to provide valuable information on the circadian clock regulation of plant resistance to nitrogen stress.

Early Detection of Excess Nitrogen Consumption in Cucumber Plants Using Hyperspectral Imaging Based on Hybrid Neural Networks and the Imperialist Competitive Algorithm

To achieve healthy and optimal yields of agricultural products, the principles of nutrition must be observed and appropriate fertilizers must be applied. Nutritional deficiencies or overabundance reduce the quality and yield of the products. Thus, their early detection prevents physiological disorders and associated diseases. Most research efforts have focused on spectroscopy, which extracts only spectral data from a single point of the product. The present study aims to detect early excess nitrogen in cucumber plants by using a new hyperspectral imaging technique based on a hybrid of artificial neural networks and the imperialist competitive algorithm (ANN-ICA), which can provide spectral and spatial information on the leaves at the same time. First, cucumber seeds were planted in 18 pots. The same inputs were applied to all the pots until the plants grew; after that, 30% excess nitrogen was applied to nine pots with irrigation water, while it remained constant in the other nine pots. Each day, six leaves were collected from each pot, and their images were captured using a hyperspectral camera (in the range of 400–1100 nm). The wavelengths of 715, 783 and 821 nm were determined as the most effective for early detection of excess nitrogen using a hybrid of artificial neural networks and the artificial bee colony algorithm (ANN-ABC). The parameter of days of treatment was classified using ANN-ICA. The performance of the classifier was evaluated using different criteria, namely recall, accuracy, specificity, precision and the F-measure. The results indicate that the differences between different days were statistically significant. This means that the hyperspectral imaging technique was able to detect plants with excess nitrogen in the near-infrared range (NIR), with a correct classification rate of 96.11%.