Genetic variability of rice stripe virus after its pandemic in Jiangsu.

BACKGROUND: Rice stripe virus (RSV) caused a serious disease pandemic in rice in East China between 2001 and 2010. The continuous integrated managements reduced virus epidemic year by year until it was non-epidemic. As an RNA virus, its genetic variability after undergoing a long-term non-epidemic period was meaningful to study. While in 2019, the sudden occurrence of RSV in Jiangsu provided an opportunity for the study. METHODS AND RESULTS: The complete genome of JY2019, an RSV isolate from Jiangyan, was determined. A genotype profile of 22 isolates from China, Japan and Korea indicated that the isolates from Yunnan formed the subtype II, and other isolates clustered the subtype I. RNA 1-3 of JY2019 isolate well-clustered in the subtype I clade, and RNA 4 was also in subtype I, but it had a slight separation from other intra-group isolates. After phylogenetic analyses, it was considered NSvc4 gene contributed to the tendency, because it exhibited an obvious trend towards the subtype II (Yunnan) group. High sequence identity (100%) of NSvc4 between JY2019 and barnyardgrass isolate from different regions demonstrated genetic variation of NSvc4 was consistent in RSV natural populations in Jiangsu in the non-epidemic period. In the phylogenetic tree of all 74 NSvc4 genes, JY2019 belonged to a minor subtype Ib, suggesting the subtype Ib isolates might have existed in natural populations before the non-epidemic period, but not a dominant population. CONCLUSIONS: Our results suggested that NSvc4 gene was susceptible to selection pressure, and the subtype Ib might be more adaptable for the interaction between RSV and hosts in the non-epidemic ecological conditions.

Monitoring green tea fixation quality by intelligent sensors: comparison of image and spectral information.

BACKGROUND: Intelligent monitoring of fixation quality is a prerequisite for automated green tea processing. To meet the requirements of intelligent monitoring of fixation quality in large-scale production, fast and non-destructive detection means are urgently needed. Here, smartphone-coupled micro near-infrared spectroscopy and a self-built computer vision system were used to perform rapid detection of the fixation quality in green tea processing lines. RESULTS: Spectral and image information from green tea samples with different fixation degrees were collected at-line by two intelligent monitoring sensors. Competitive adaptive reweighted sampling and correlation analysis were employed to select feature variables from spectral and color information as the target data for modeling, respectively. The developed least squares support vector machine (LS-SVM) model by spectral information and the LS-SVM model by image information achieved the best discriminations of sample fixation degree, with both prediction set accuracies of 100%. Compared to the spectral information, the image information-based support vector regression model performed better in moisture prediction, with a correlation coefficient of prediction of 0.9884 and residual predictive deviation of 6.46. CONCLUSION: The present study provided a rapid and low-cost means of monitoring fixation quality, and also provided theoretical support and technical guidance for the automation of the green tea fixation process. © 2022 Society of Chemical Industry.

Visualizing chemical indicators: Spatial and temporal quality formation and distribution during black tea fermentation.

Fermentation is a key black tea processing step and makes an important contribution to quality formation. Current approaches to fermentation monitoring are costly or laboratory-based. Here, we first evaluated the potential of at-line computer vision for detecting fermentation quality in a tea factory. A self-built industrial camera was used to collect tea samples at various fermentation durations. The correlations of color variables that were extracted from the images with key quality indicators in the tea samples were verified. Subsequently, partial least-squares regression models based on the color variables showed high prediction accuracy with residual prediction deviation values of 4.13, 3.53, and 3.39 for catechins, theaflavins and chlorophylls, respectively. Finally, the spatial and temporal distributions of indicators during fermentation were mapped to visualize the fermentation quality. This study realized low-cost, at-line and real-time detection for black tea fermentation, which provides technical support for the industrial and intelligent production of black tea.

Evaluation of the effects of solar withering on nonvolatile compounds in white tea through metabolomics and transcriptomics.

The mechanism through which solar withering (SW) affects the quality of white tea is unclear. To address this gap in the literature, in this study, we used metabolomics and transcriptomics to investigate the effect of SW on the quality of WT. WT that underwent SW was slightly more bitter and astringent than WT that underwent natural withering (control group). Specifically, SW considerably increased the concentration of astringent flavonoids and flavone glycosides in WT. This increase was mainly attributed to the upregulated expression of key genes in the shikimic acid, phenylpropanoid, and flavonoid biosynthesis pathways, such as shikimate kinase, chalcone synthase, and flavonol synthase. In addition, SW experienced considerable heat and light stress. The levels of glycerophosphatidylcholine and carbohydrates increased in response to the stress, which also affected the taste of WT. The results of this study indicate the mechanism through which SW affects the quality of WT.

A rice plant expressing viral glycoprotein NSvc2-NS reduces the transmission of rice stripe virus by the small brown planthopper.

BACKGROUND: Plant viruses transmitted by arthropod vectors threaten crop health worldwide. Rice stripe virus (RSV) is one of the most important rice viruses in East Asia and is transmitted by the small brown planthopper (SBPH). Previously, it was demonstrated that the viral glycoprotein NSvs2-N could mediate RSV infection of the vector midgut. Therefore, NSvc2-N protein could potentially be used to reduce RSV transmission by competitively blocking midgut receptors. RESULTS: Here, we report that transgenic rice plants expressing viral glycoprotein can interfere with RSV acquisition and transmission by SBPH. The soluble fraction (30-268 amino acids, designated NSvs2-NS ) of NSvs2-N was transformed into rice calli, which produced plants harboring the exogenous gene. When SBPH was fed on transgenic plants prior to RSV-infected rice (sequential feeding) and when insects were fed on RSV-infected transgenic plants (concomitant feeding), virus acquisition by the insect vector was inhibited, and subsequent viral titers were reduced. Immunofluorescence labeling also indicated that viral infection of the insect midgut was inhibited after SBPH was fed on transgenic plants. The system by which RSV infected insect cells in vitro was used to corroborate the role of NSvc2-NS in reducing viral infection. After the cells were incubated with transgenic rice sap, the virus infection rate of the cells decreased significantly, and viral accumulation in the cells was lower than that in the control group. CONCLUSION: These results demonstrated the negative effect of NSvs2-NS transgenic plants on RSV transmission by insect vectors, which provides a novel and effective way to control plant viral diseases. © 2022 Society of Chemical Industry.

High-speed identification system for fresh tea leaves based on phenotypic characteristics utilizing an improved genetic algorithm.

BACKGROUND: High-quality tea requires leaves of similar size and tenderness. The grade of the fresh leaves determines the quality of the tea. The automated classification of fresh tea leaves improves resource utilization and reduces manual picking costs. The present study proposes a method based on an improved genetic algorithm for identifying fresh tea leaves in high-speed parabolic motion using the phenotypic characteristics of the leaves. During parabolic flight, light is transmitted through the tea leaves, and six types of fresh tea leaves can be quickly identified by a camera. RESULTS: The influence of combinations of morphology, color, and custom corner-point morphological features on the classification results were investigated, and the necessary dimensionality of the model was tested. After feature selection and combination, the classification performance of the Naive Bayes, k-nearest neighbor, and support vector machine algorithms were compared. The recognition time of Naive Bayes was the shortest, whereas the accuracy of support vector machine had the best classification accuracy at approximately 97%. The support vector machine algorithm with only three feature dimensions (equivalent diameter, circularity, and skeleton endpoints) can meet production requirements with an accuracy rate reaching 92.5%. The proposed algorithm was tested by using the Swedish leaf and Flavia data sets, on which it achieved accuracies of 99.57% and 99.44%, respectively, demonstrating the flexibility and efficiency of the recognition scheme detailed in the present study. CONCLUSION: This research provides an efficient tea leaves recognition system that can be applied to production lines to reduce manual picking costs. © 2022 Society of Chemical Industry.

Analyses on the Infection Process of Rice Virus and the Spatiotemporal Expression Pattern of Host Defense Genes Based on a Determined-Part Inoculation Approach.

Rice viral diseases adversely affect crop yield and quality. Most rice viruses are transmitted through insect vectors. However, the traditional whole-plant inoculation method cannot control the initial inoculation site in rice plants because the insect feeding sites in plants are random. To solve this problem, we established a determined-part inoculation approach in this study that restricted the insect feeding sites to specific parts of the rice plant. Rice stripe virus (RSV) was used as the model virus and was inoculated at the bottom of the stem using our method. Quantitative real-time PCR and Western blot analyses detected RSV only present at the bottom of the Nipponbare (NPB) stem at 1 day post-inoculation (dpi), indicating that our method successfully controlled the inoculation site. With time, RSV gradually moved from the bottom of the stem to the leaf in NPB rice plants, indicating that systemic viral spread can also be monitored using this method. In addition, a cultivar resistant to RSV, Zhendao 88 (ZD88), was inoculated using this method. We found that RSV accumulation in ZD88 was significantly lower than in NPB. Additionally, the expression level of the resistant gene STV11 in ZD88 was highly induced at the initial invasion stage of RSV (1 dpi) at the inoculation site, whereas it remained relatively stable at non-inoculated sites. This finding indicated that STV11 directly responded to RSV invasion to inhibit virus accumulation at the invasion site. We also proved that this approach is suitable for other rice viruses, such as Rice black-streaked dwarf virus (RBSDV). Interestingly, we determined that systemic infection with RSV was faster than that with RBSDV in NPB, which was consistent with findings in field trails. In summary, this approach is suitable for characterizing the viral infection process in rice plants, comparing the local viral accumulation and spread among different cultivars, analyzing the spatiotemporal expression pattern of resistance-associated genes, and monitoring the infection rate for different viruses.

Rapid identification of the green tea geographical origin and processing month based on near-infrared hyperspectral imaging combined with chemometrics.

The geographical origin and processing month of green tea greatly affect its economic value and consumer acceptance. This study investigated the feasibility of combining near-infrared hyperspectral imaging (NIR-HSI) with chemometrics for the identification of green tea. Tea samples produced in three regions of Chongqing (southeastern Chongqing, northeastern Chongqing, and western Chongqing) for four months (from May to August 2020) were collected. Principal component analysis (PCA) was used to reduce data dimensionality and visualize the clustering of samples in different categories. Linear partial least squares-discriminant analysis (PLS-DA) and nonlinear support vector machine (SVM) algorithms were used to develop discriminant models. The PCA-SVM models based on the first four and first five principal components (PCs) achieved the best accuracies of 97.5% and 95% in the prediction set for geographical origin and processing month of green tea, respectively. This study demonstrated the feasibility of HSI in the identification of green tea species, providing a rapid and nondestructive method for the evaluation and control of green tea quality.

Rice virus release from the planthopper salivary gland is independent of plant tissue recognition by the stylet.

BACKGROUND: The transmission of plant viruses by arthropod vectors is closely related to feeding behavior. For persistently transmitted viruses, virus release means that virus moves through the salivary gland microvillus barriers of insects into plant via the stylet. However, whether virus release is dependent on plant tissue and component recognition by the stylet is unclear. RESULTS: In this study, the small brown planthopper (SBPH) and two rice viruses transmitted by it were used as a model to explore this question. After the viruliferous insects penetrated a stretched membrane without plant tissue structure and ingested liquid food (rice sap, nutrient solution or water), both viruses were detected in the liquid food after only a 6 min inoculation access period, suggesting that the viruses were released from SBPH salivary gland independent of plant tissue and component recognition by the stylet. In subsequent electrical penetration graph (EPG) analysis, N4a-like and N4b-like waveforms, similar to N4a (phloem salivation before ingestion) and N4b (sieve element ingestion), were observed during SBPH penetrating the membrane, exhibiting normal feeding activity of planthopper on membrane, which further demonstrated that virus release from salivary gland was along with feeding activity, without the stylet sensing plant tissue. EPG analysis and identification of salivary proteins indicated more active feeding behavior and efficient salivation in viruliferous planthoppers. CONCLUSION: These results suggest that the rice virus is released from insect salivary gland independent of plant tissue and component recognition by the stylet, and the simple virus release mode facilitates virus transmission by vectors. © 2020 Society of Chemical Industry.

An Event-Based Neurobiological Recognition System with Orientation Detector for Objects in Multiple Orientations.

A new multiple orientation event-based neurobiological recognition system is proposed by integrating recognition and tracking function in this paper, which is used for asynchronous address-event representation (AER) image sensors. The characteristic of this system has been enriched to recognize the objects in multiple orientations with only training samples moving in a single orientation. The system extracts multi-scale and multi-orientation line features inspired by models of the primate visual cortex. An orientation detector based on modified Gaussian blob tracking algorithm is introduced for object tracking and orientation detection. The orientation detector and feature extraction block work in simultaneous mode, without any increase in categorization time. An addresses lookup table (addresses LUT) is also presented to adjust the feature maps by addresses mapping and reordering, and they are categorized in the trained spiking neural network. This recognition system is evaluated with the MNIST dataset which have played important roles in the development of computer vision, and the accuracy is increased owing to the use of both ON and OFF events. AER data acquired by a dynamic vision senses (DVS) are also tested on the system, such as moving digits, pokers, and vehicles. The experimental results show that the proposed system can realize event-based multi-orientation recognition. The work presented in this paper makes a number of contributions to the event-based vision processing system for multi-orientation object recognition. It develops a new tracking-recognition architecture to feedforward categorization system and an address reorder approach to classify multi-orientation objects using event-based data. It provides a new way to recognize multiple orientation objects with only samples in single orientation.