Physiological and transcriptome analysis of Dendrobium officinale under low nitrogen stress.

Nitrogen (N) is the main nutrient of plants, and low nitrogen usually affects plant growth and crop yield. The traditional Chinese herbal medicine Dendrobium officinale Kimura et. Migo is a typical low nitrogen-tolerant plant, and its mechanism in response to low nitrogen stress has not previously been reported. In this study, physiological measurements and RNA-Seq analysis were used to analyse the physiological changes and molecular responses of D. officinale under different nitrogen concentrations. The results showed that under low nitrogen levels, the growth, photosynthesis and superoxide dismutase activity were found to be significantly inhibited, while the activities of peroxidase and catalase, the content of polysaccharides and flavonoids significantly increased. Differentially expressed genes (DEGs) analysis showed that nitrogen and carbon metabolisms, transcriptional regulation, antioxidative stress, secondary metabolite synthesis and signal transduction all made a big difference in low nitrogen stress. Therefore, copious polysaccharide accumulation, efficient assimilation and recycling of nitrogen, as well as rich antioxidant components play critical roles. This study is helpful for understanding the response mechanism of D. officinale to low nitrogen levels, which might provide good guidance for practical production of high quality D. officinale .

A Dynamic Survival Detection and Analysis System for Mosquito Larvae Viability in Drug Assays.

Bioinformatics studies have emerged in the domain of larval behavior analysis in recent years. A dynamic survival detection and analysis system for automatically monitoring a large amount of mosquito larvae in bioassays with multiwell plates by acquiring and processing videos is proposed in this article. In our system, equipment is designed for acquiring the video of the mosquito larvae in several multiwell plates simultaneously by a camera, and a video analysis module is developed for detecting the survival states of larvae in each well in real time. Also, a novel model and a new image registration algorithm are proposed to accurately obtain the survival state by analyzing the larval motion activities and the weights of larvae in each well. In our experiments, several spinosad bioassays against 2-instar Aedes aegypti with 96-well plates are used to evaluate the proposed system, and the accuracy of the larval survival state in our system is more than 85%. Moreover, this investigation has indicated that the developed system not only can be used in the mosquito larval bioassays but also can be suitable to detect and analyze the behaviors of large amount of other larvae.