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Wild rice ( Zizania spp.), an aquatic grass belonging to the subfamily Gramineae, has a high economic value. Zizania provides food (such as grains and vegetables), a habitat for wild animals, and paper-making pulps, possesses certain medicinal values, and helps control water eutrophication. Zizania is an ideal resource for expanding and enriching a rice breeding gene bank to naturally preserve valuable characteristics lost during domestication. With the Z. latifolia and Z. palustris genomes completely sequenced, fundamental achievements have been made toward understanding the origin and domestication, as well as the genetic basis of important agronomic traits of this genus, substantially accelerating the domestication of this wild plant. The present review summarizes the research results on the edible history, economic value, domestication, breeding, omics research, and important genes of Z. latifolia and Z. palustris over the past decades. These findings broaden the collective understanding of Zizania domestication and breeding, furthering human domestication, improvement, and long-term sustainability of wild plant cultivation.

The silverleaf whitefly is an important and invasive crop pest in many countries. Previous laboratory studies with demonstrated that can suppress jasmonic acid (JA) defenses and thereby enhance performance. Whether can suppress JA-regulated host plant defenses in field is unknown. In the present study, we found that, relative to wild-type (WT) tomato plants, transgenic tomato mutants that activated JA defenses ( ) or impaired JA defenses ( and ) did not affect the survival or reproduction of adults in growth chamber experiments. In contrast, tomato mutants that activated JA defenses slowed nymphal development, while mutants that impaired JA defenses accelerated nymphal development. These effects of JA defenses on nymphal development were also documented under semi-field conditions. Changes in the expression of defense genes and in the production of phytohormones indicated that adults can suppress JA-dependent defenses after infestation for >72 h. The suppression of JA was correlated with the induction of salicylic acid (SA) in -infested leaves under laboratory and under semi-field conditions. If SA signaling was blocked, JA accumulation increased in infested leaves and nymphal development was delayed. These results indicate that, although JA signaling helps in mediating tomato responses against nymphs, can inhibit JA biosynthesis and its action in an SA-dependent manner.

Bean pod mottle virus (BPMV) is a destructive virus that causes serious economic losses in many countries every year, highlighting the importance of its effective detection. In this study, we developed a fast reverse transcription-cross-priming amplification (RT-CPA) coupled with lateral flow dipstick (LFD) diagnostic method for BPMV detection. The RT-CPA-LFD assay that targets the coat protein gene of BPMV was highly specific against diagnosing four other common viruses transmitted by soybean seeds, i.e., Southern bean mosaic virus (SBMV), Tomato ringspot virus  (ToRSV), Arabis mosaic virus  (ArMV), and Tobacco ringspot virus  (TRSV). The sensitivities of the real-time fluorescent RT-CPA and the RT-CPA-LFD assay were at least 50 pg/μl and 500 pg/μl, respectively. Despite a compromise in the limit of detection of the RT-CPA method compared with TaqMan-MGB real-time RT-PCR, our results demonstrated a notably better performance in the detection of field samples of BPMV-infested soybean seeds. With the advantages of efficiency and convenience by visual determination, the RT-CPA-LFD assay presents a potential application for the rapid and accurate detection of BPMV in routine tests.

Ethylene response factors (ERFs) are members of the APETALA2/ERF transcription factor family, and they play important roles in plant growth, development, and multiple environmental stress responses. In our present study, an ERF transcription factor gene designated as BoERF1 was isolated from broccoli, and its expression was induced by both NaCl and Sclerotinia sclerotiorum. Transgenic plants over-expressing BoERF1 were generated by Agrobacterium tumefaciens-mediated transformation, and they exhibited higher seed germination rates and less chlorophyll loss under salt stress as compared to wild-type (WT) broccoli plants, and an approximately two-fold increase in chlorophyll content was observed in three transgenic lines. Over-expression of BoERF1 in broccoli dramatically decreased hydrogen peroxide (H2O2), relative electrical conductivity (REC), and malondialdehyde (MDA) contents, but increased free proline, the activities of catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD), resulting in less cell death in the leaves of transgenic plants. Moreover, broccoli plants over-expressing BoERF1 exhibited significant resistance to Sclerotinia stem rot as compared to the WT line. Qualitative real-time PCR (qRT-PCR) results confirmed that the expression levels of BoERF1 in transgenic lines were higher than those in WT plants, and the peak expression levels were seen at 24 and 12 h with 3.21- and 4.66-fold changes after treatments with S. sclerotiorum and NaCl, respectively. Taken together, our results indicate that BoERF1 acts as a positive regulator in resistance to both salt stress and Sclerotinia stem rot, suggesting its potential utility in molecular breeding of broccoli.

This work presents a reliable approach to trace teas’ geographical origins despite changes in teas caused by different harvest years. A total of 1447 tea samples collected from various areas in 2014 (660 samples) and 2015 (787 samples) were detected by FT-NIR. Seven classifiers trained on the 2014 dataset all succeeded to trace origins of samples collected in 2014; however, they all failed to predict origins for the 2015 samples due to different data distributions and imbalanced dataset. Three outlier detection based undersampling approaches—one-class SVM (OC-SVM), isolation forest and elliptic envelope—were then proposed; as a result, the highest macro average recall (MAR) for the 2015 dataset was improved from 56.86% to 73.95% (by SVM). A model updating approach was also applied, and the prediction MAR was significantly improved with increase in the updating rate. The best MAR (90.31%) was first achieved by the OC-SVM combined SVM classifier at a 50% rate.

This paper focused on an effective method to discriminate the geographical origin of Wuyi-Rock tea by the stable isotope ratio (SIR) and metallic element profiling (MEP) combined with support vector machine (SVM) analysis. Wuyi-Rock tea (n=99) collected from nine producing areas and non-Wuyi-Rock tea (n=33) from eleven nonproducing areas were analysed for SIR and MEP by established methods. The SVM model based on coupled data produced the best prediction accuracy (0.9773). This prediction shows that instrumental methods combined with a classification model can provide an effective and stable tool for provenance discrimination. Moreover, every feature variable in stable isotope and metallic element data was ranked by its contribution to the model. The results show that δ2H, δ18O, Cs, Cu, Ca, and Rb contents are significant indications for provenance discrimination and not all of the metallic elements improve the prediction accuracy of the SVM model.

This investigation was aimed at developing a rapid analysis method for authentication of Chinese sesame oils by FTIR spectrometry and chemometrics. Ninety-five sesame oil samples were collected from the six main producing areas of China to include most if not all of the significant spectral variations likely to be encountered in future authentic materials. Two class modeling techniques, the soft independent modeling of class analogy (SIMCA) and the partial least squares class model (PLSCM) were investigated and the data preprocessing techniques including smoothing, derivative and standard normal variate (SNV) tests were performed to improve the classification performance. It was demonstrated that SIMCA and PLSCM can detect various adulterated sesame oils doped with 3% or more (w/w) of other cheaper oils, including rapeseed, soybean, palm and peanut oils. First derivative, second derivative and SNV tests significantly enhanced the class models by reducing baseline and background shifts. Smoothing of raw spectra led to inferior identification performance and proved itself to be unsuitable because some of the detailed frequency details were lost during smoothing. The best model performance was obtained with second derivative spectra by SIMCA (sensitivity 0.905 and specificity 0.944) and PLSCM (sensitivity 0.952 and specificity 0.937). Although it is difficult to perform an exhaustive sampling of all types of pure sesame oils and potential adulterations, PLS and SIMCA combined with FTIR spectrometry can detect most of current adulterations of sesame oils on the Chinese market.

This paper focuses on a rapid and nondestructive way to discriminate the geographical origin of Anxi-Tieguanyin tea by near-infrared (NIR) spectroscopy and chemometrics. 450 representative samples were collected from Anxi County, the original producing area of Tieguanyin tea, and another 120 Tieguanyin samples with similar appearance were collected from unprotected producing areas in China. All these samples were measured by NIR. The Stahel-Donoho estimates (SDE) outlyingness diagnosis was used to remove the outliers. Partial least squares discriminant analysis (PLSDA) was performed to develop a classification model and predict the authenticity of unknown objects. To improve the sensitivity and specificity of classification, the raw data was preprocessed to reduce unwanted spectral variations by standard normal variate (SNV) transformation, taking second-order derivatives (D2) spectra, and smoothing. As the best model, the sensitivity and specificity reached 0.931 and 1.000 with SNV spectra. Combination of NIR spectrometry and statistical model selection can provide an effective and rapid method to discriminate the geographical producing area of Anxi-Tieguanyin.

This paper reports the application of near infrared (NIR) spectroscopy and pattern recognition methods to rapid and automatic discrimination of the genotypes (parent, transgenic, and parent-transgenic hybrid) of cotton plants. Diffuse reflectance NIR spectra of representative cotton seeds (n=120) and leaves (n=123) were measured in the range of 4000–12000 cm−1. A practical problem when developing classification models is the degradation and even breakdown of models caused by outliers. Considering the high-dimensional nature and uncertainty of potential spectral outliers, robust principal component analysis (rPCA) was applied to each separate sample group to detect and exclude outliers. The influence of different data preprocessing methods on model prediction performance was also investigated. The results demonstrate that rPCA can effectively detect outliers and maintain the efficiency of discriminant analysis. Moreover, the classification accuracy can be significantly improved by second-order derivative and standard normal variate (SNV). The best partial least squares discriminant analysis (PLSDA) models obtained total classification accuracy of 100% and 97.6% for seeds and leaves, respectively.

The feasibility of electronic tongue and multivariate analysis was investigated for discriminating the specific geographical origins of a Chinese green tea with Protected Designation of Origin (PDO). 155 Longjing tea samples from three subareas were collected and analyzed by an electronic tongue array of 7 sensors. To remove the influence of abnormal measurements and samples, robust principal component analysis (ROBPCA) was used to detect outliers in each class. Partial least squares discriminant analysis (PLSDA) was then used to develop a classification model. The prediction sensitivity/specificity of PLSDA was 1.000/1.000, 1.000/0.967, and 0.950/1.000 for longjing from Xihu, Qiantang, and Yuezhou, respectively. Electronic tongue and chemometrics can provide a rapid and reliable tool for discriminating the specific producing areas of Longjing.