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Globally, 91% of plant production encounters diverse environmental stresses that adversely affect their growth, leading to severe yield losses of 50–60%. In this case, monitoring the connection between the environment and plant health can balance population demands with environmental protection and resource distribution. Fluorescent chemosensors have shown great progress in monitoring the health and environment of plants due to their high sensitivity and biocompatibility. However, to date, no comprehensive analysis and systematic summary of fluorescent chemosensors used in monitoring the correlation between plant health and their environment have been reported. Thus, herein, we summarize the current fluorescent chemosensors ranging from their design strategies to applications in monitoring plant-environment interaction processes. First, we highlight the types of fluorescent chemosensors with design strategies to resolve the bottlenecks encountered in monitoring the health and living environment of plants. In addition, the applications of fluorescent small-molecule, nano and supramolecular chemosensors in the visualization of the health and living environment of plants are discussed. Finally, the major challenges and perspectives in this field are presented. This work will provide guidance for the design of efficient fluorescent chemosensors to monitor plant health, and then promote sustainable agricultural development. [ABSTRACT FROM AUTHOR]

This study aims to report three new species of Conoideocrella and Moelleriella from Yunnan Province, Southwestern China. Species of Conoideocrella and Moelleriella parasitize scale insects (Coccidae and Lecaniidae, Hemiptera) and whiteflies (Aleyrodidae, Hemiptera). Based on the phylogenetic analyses of the three-gene nrLSU, tef-1α, and rpb1, it showed one new record species (Conoideocrella tenuis) and one new species (Conoideocrella fenshuilingensis sp. nov.) in the genus Conoideocrella, and two new species, i.e., Moelleriella longzhuensis sp. nov. and Moelleriella jinuoana sp. nov. in the genus Moelleriella. The three new species were each clustered into separate clades that distinguished themselves from one another. All of them were distinguishable from their allied species based on their morphology. Morphological descriptions, illustrations, and comparisons of the allied taxa of the four species are provided in the present paper. In addition, calculations of intraspecific and interspecific genetic distances were performed for Moelleriella and Conoideocrella. [ABSTRACT FROM AUTHOR]

In order to solve problem of big error in predicting data sequence with fluctuation while using grey model to predict transformer fault, an improvement scheme of gray GM(1, m) predicting model was proposed. In the scheme, original data sequence is processed for better exponential rule to meet smoothness requirement of the predicting model, and the processed data sequence is analyzed by grey relational degree method to get relationship between variables. Background value of the predicting model is optimized and used for establishing the model, and equal-dimension new-information model is used to predict data. The improved GM(1, m) model was used to predict volume fraction of seven kinds of characteristic gases in some transformer oil and both average differences and posteriori relative error of predicted data were smaller than the ones of GM(1, 1) model and traditional GM(1, m) model, which showed the improved grey GM(1, m) has better prediction accuracy.

Nitrogen and phosphorus are limiting and essential macronutrients to maintain the growth of phytoplankton, and monitoring their changes are fundamental to lake management. The relationship of TN and phytoplankton chlorophyll a (Chl. a) was reported in some research; however, TN may be underestimated in some research due to measurement errors in bloom-occurring water bodies. The alkaline potassium persulfate digestion-UV spectrophotometry method is widely used for water quality determination for TN. The organic nitrogen in cyanobacterial cells may be difficult to be completely degraded, which should result in the underestimation of TN. However, there is no detailed study on TN determination of bloom water containing high-density algal cells. 　　The TN was underestimated by directly measuring the samples from Taihu Lake, although the TN was within the allowable concentration range of the standard method (<7.0 mg/L, HJ636-2012). In order to explore the effect of cell densities on TN determination, the lab-cultured cyanobacterium and green algae was measured. The results showed that TN of high-density cells was significantly underestimated. The density of cells must be less than 108 /L to meet the requirements of accurate TN determination. We measured several samples from natural blooms to estimate the TN in the Microcystis blooms. The TN value obtained from the original water sample was underestimated by 26.59%—40.42% compared to the measured value of the water sample diluted 10—30 times. Our results demonstrated that sufficient dilution is necessary for the TN measuring of cyanobacterial blooms. 　　In order to estimate the contribution of Microcystis biomass to TN, the TN and Chl. a in four bloom samples collected from Taihu Lake were measured. The mean value was 0.15 mg N/μg Chl. a (spiked recovery rate was 98.84%—109.39%). The average nitrogen content of dry Microcystis blooms from Taihu Lake was about 16%. These data should be useful for the ecological research of and the control of Microcystis blooms. [ABSTRACT FROM AUTHOR]