Polyamines (PAs) play diverse roles in plant growth and development, as well as responses to environmental stimuli.  In this study, the effects of PAs on rice salt tolerance were investigated.  Salt stress resulted in the alteration of endogenous PAs levels in rice roots and leaves, where spermidine (Spd) and spermine (Spm) contents were increased, and putrescine (Put) content was decreased.  RT-qPCR analysis revealed that PAs biosynthesis-related genes ADC1, ODC, and Arginase were significantly downregulated by salt treatment; however, SAMDC transcription was significantly upregulated.  Exogenous Spm enhanced rice salt tolerance remarkably; however, exogenous Put and Spd undermined rice salt tolerance.  Transgenic rice plants overexpressing SAMDC display a higher ratio of Spm/(Put+Spd) and enhanced salt tolerance.  Salt stress also increased polyamine oxidase activities in rice, resulting in elevated reactive oxygen species (ROS) production.  Our findings revealed that accumulation of Put and Spd substantially reduced salt tolerance in rice, likely by facilitating ROS production; whereas, conversion of Put and Spd to Spm contributes to rice salt tolerance.

In addition to soil samples, conventional soil maps, and experienced soil surveyors, text about soils (e.g., soil survey reports) is an important potential data source for extracting soil–environment relationships.  Considering that the words describing soil–environment relationships are often mixed with unrelated words, the first step is to extract the needed words and organize them in a structured way.  This paper applies natural language processing (NLP) techniques to automatically extract and structure information from soil survey reports regarding soil–environment relationships.  The method includes two steps: (1) construction of a knowledge frame and (2) information extraction using either a rule-based method or a statistic-based method for different types of information.  For uniformly written text information, the rule-based approach was used to extract information.  These types of variables include slope, elevation, accumulated temperature, annual mean temperature, annual precipitation, and frost-free period.  For information contained in text written in diverse styles, the statistic-based method was adopted.  These types of variables include landform and parent material.  The soil species of China soil survey reports were selected as the experimental dataset.  Precision (P), recall (R), and F₁-measure (F1) were used to evaluate the performances of the method.  For the rule-based method, the P values were 1, the R values were above 92%, and the F1 values were above 96% for all the involved variables.  For the method based on the conditional random fields (CRFs), the P, R and F1 values for the parent material were, respectively, 84.15, 83.13, and 83.64%; the values for landform were 88.33, 76.81, and 82.17%, respectively.  To explore the impact of text types on the performance of the CRFs-based method, CRFs models were trained and validated separately by the descriptive texts of soil types and typical profiles.  For parent material, the maximum F1 value for the descriptive text of soil types was 90.7%, while the maximum F1 value for the descriptive text of soil profiles was only 75%.  For landform, the maximum F1 value for the descriptive text of soil types was 85.33%, which was similar to that of the descriptive text of soil profiles (i.e., 85.71%).  These results suggest that NLP techniques are effective for the extraction and structuration of soil–environment relationship information from a text data source.

In order to precisely assess gene expression level, a suitable internal reference gene must be chosen to quantify real-time reverse transcription polymerase chain reaction (RT-qPCR) data.  For greenbug, Schizaphis graminum, a suitable reference gene for assessing the level of transcriptional expression of target genes has yet to be explored.  In our study, eight reference genes, elongation fator 1 beta (Ef1β), TATA box binding protein (TBP), alpha-tubulin (α-TUB), 18S ribosomal (18S), 28S ribosomal (28S), glyceraldehyde-3-phosphate (GAPDH), actin (ACT), and ribosomal protein L18 (RPL18) were evaluated in S. graminum at different developmental stages, tissues, and insecticide treatments.  To further explore whether these genes are suitable to serve as internal control, three software-based approaches (geNorm, BestKeeper, and NormFinder), ?Ct method, and one web-based comprehensive tool (RefFinder) were employed to analyze and rank the tested genes.  The optimal number of reference genes was determined using the geNorm program, and the suitability of particular reference genes was empirically validated according to normalized gene expression data of three target genes, heat shock protein gene (HSP70), cytocrome P450 gene (SgraCYP18A1), and glutathione S-transferase (GST).  We found that the most suitable reference genes varied considerably under different experimental conditions.  For developmental stages, α-TUB and 28S were the optimal reference genes; for different tissues, 18S and ACT were suitable reference genes; for insecticide treatments, 28S and α-TUB were suitable for normalizations of expression data.  In addition, 28S and α-TUB were the suitable reference gene as they had the most stable expression among different developmental stages, tissues and insecticide treatments.  This should be useful for the selection of the suitable reference genes to obtain reliable RT-qPCR data in the gene expression of S. graminum.