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Abscisic acid (ABA) and auxin are important hormones controlling the ripening progression of grape berry, and both the initiation and duration of ripening can dramatically affect the berry quality. However, the responses of flavor compounds to the hormones are inadequately understood. In this study, ABA and synthetic auxin α-naphthaleneacetic acid (NAA) were sprayed on Cabernet Sauvignon berries before véraison, and comparative transcriptomic and metabolic analysis were conducted to investigate the influence on berry quality-related metabolites. The 1000 mg/L ABA (ABA1000) and 200 mg/L NAA (NAA200) treated grapes exhibited shorter and longer phenological intervals compared to the control, respectively. The transcriptomic comparison between pre-véraison and véraison revealed that the varied ripening initiation and duration significantly affected the expression of genes related to specific metabolism, particularly in the biosynthetic metabolism of anthocyanin and volatile compounds. The up-regulated VviF3’H in both ABA1000-treated and NAA200-treated berries increased the proportion of 3′-substituted anthocyanins, and the 3′5′-substituted anthocyanins were largely reduced in the NAA200-treated berries. Concurrently, VviCCD4a and VviCCD4b were up-regulated, and the norisoprenoids were correspondingly elevated in the NAA200-treated berries. These data suggest that ABA and NAA applications may be useful in controlling the ripening and improving the flavor of the grape berry.

Purpose The purpose of this paper is to provide further insight into Chinese wine consumers’ preference, grasp the regional sensory preference differences of China and build up a predictive model for wine consumers’ sensory preferences. Design/methodology/approach The study involved 3,421 Chinese wine consumers in the survey. Classified statistics were conducted to excavate regional differences of wine consumers’ sensory preferences. By analyzing influencing factors, prediction models for consumers’ sensory attribute preferences were constructed on the basis of multivariate disorder logistic regression method. Findings Empirical research showed that the wine with the following sensory attributes was the most preferred by Chinese consumers: dry red, refreshing and soft taste, still type, moderate aroma degree and mellow aroma, and sweet wine was also popular. Consumers’ preference varied from region to region. The proposed predicting method of the study realized more than 70 percent accuracy when conducting prediction for color, sweetness, aroma type and flavor preferences. Social implications By shedding light on the latest sensory attribute preferences of Chinese wine consumers, this study will help wine industry participants conduct market segmentation based on the diversification of consumers’ preferences. The wine enterprises can gain guidance from the results to conduct market positioning, adjust strategies and provide specific production for target wine consumers. Originality/value Based on the actual situation of Chinese wine market, this study defines sensory attribute indexes of wine from the perspective of wine consumers and presents the most recent comprehensive research on the sensory preferences of Chinese wine consumers through a nationwide survey.

Small-scale farms represent about 80% of the farming area of China, in a context where they need to produce economic and environmentally sustainable food. The objective of this work was to define management zone (MZs) for a village by comparing the use of crop yield proxies derived from historical satellite images with soil information derived from remote sensing, and the integration of these two data sources. The village chosen for the study was Wangzhuang village in Quzhou County in the North China Plain (NCP) (30°51′55″ N; 115°02′06″ E). The village was comprised of 540 fields covering approximately 177 ha. The subdivision of the village into three or four zones was considered to be the most practical for the NCP villages because it is easier to manage many fields within a few zones rather than individually in situations where low mechanization is the norm. Management zones defined using Landsat satellite data for estimation of the Green Normalized Vegetation Index (GNDVI) was a reasonable predictor (up to 45%) of measured variation in soil nitrogen (N) and organic carbon (OC). The approach used in this study works reasonably well with minimum data but, in order to improve crop management (e.g., sowing dates, fertilization), a simple decision support system (DSS) should be developed in order to integrate MZs and agronomic prescriptions.

Purpose The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the price prediction issue of China’s table grape. Design/methodology/approach The approaches follows the framework of “decomposition and ensemble,” using ensemble empirical mode decomposition (EEMD) to optimize the conventional price forecasting methods, and, integrating the multiple linear regression and support vector machine to build a hybrid model which could be applied in solving price series predicting problems. Findings The proposed EEMD-ADD optimized hybrid model is validated to be considered satisfactory in a case of China’ grape price forecasting in terms of its statistical measures and prediction performance. Practical implications This study would resolve the difficulties in grape price forecasting and provides an adaptive strategy for other agricultural economic predicting problems as well. Originality/value The paper fills the vacancy of concerning researches, proposes an optimized hybrid model integrating both classical econometric and artificial intelligence models to forecast price using time series method.

Purpose The purpose of this paper is to develop a common remaining shelf life prediction model that is generally applicable for postharvest table grape using an optimized radial basis function (RBF) neural network to achieve more accurate prediction than the current shelf life (SL) prediction methods. Design/methodology/approach First, the final indicators (storage temperature, relative humidity, sensory average score, peel hardness, soluble solids content, weight loss rate, rotting rate, fragmentation rate and color difference) affecting SL were determined by the correlation and significance analysis. Then using the analytic hierarchy process (AHP) to calculate the weight of each indicator and determine the end of SL under different storage conditions. Subsequently, the structure of the RBF network redesigned was 9-11-1. Ultimately, the membership degree of Fuzzy clustering (fuzzy c-means) was adopted to optimize the center and width of the RBF network by using the training data. Findings The results show that this method has the highest prediction accuracy compared to the current the kinetic–Arrhenius model, back propagation (BP) network and RBF network. The maximum absolute error is 1.877, the maximum relative error (RE) is 0.184, and the adjusted R2 is 0.911. The prediction accuracy of the kinetic–Arrhenius model is the worst. The RBF network has a better prediction accuracy than the BP network. For robustness, the adjusted R2 are 0.853 and 0.886 of Italian grape and Red Globe grape, respectively, and the fitting degree are the highest among all methods, which proves that the optimized method is applicable for accurate SL prediction of different table grape varieties. Originality/value This study not only provides a new way for the prediction of SL of different grape varieties, but also provides a reference for the quality and safety management of table grape during storage. Maybe it has a further research significance for the application of RBF neural network in the SL prediction of other fresh foods.

Alphaherpesviruses are large dsDNA viruses with an ability to establish persistent infection in hosts, which rely partly on their ability to evade host innate immune responses, notably the type I IFN response. However, the relevant molecular mechanisms are not well understood. In this study, we report the UL42 proteins of alphaherpesvirus pseudorabies virus (PRV) and HSV type 1 (HSV1) as a potent antagonist of the IFN-I–induced JAK-STAT signaling pathway. We found that ectopic expression of UL42 in porcine macrophage CRL and human HeLa cells significantly suppresses IFN-α–mediated activation of the IFN-stimulated response element (ISRE), leading to a decreased transcription and expression of IFN-stimulated genes (ISGs). Mechanistically, UL42 directly interacts with ISRE and interferes with ISG factor 3 (ISGF3) from binding to ISRE for efficient gene transcription, and four conserved DNA-binding sites of UL42 are required for this interaction. The substitution of these DNA-binding sites with alanines results in reduced ISRE-binding ability of UL42 and impairs for PRV to evade the IFN response. Knockdown of UL42 in PRV remarkably attenuates the antagonism of virus to IFN in porcine kidney PK15 cells. Our results indicate that the UL42 protein of alphaherpesviruses possesses the ability to suppress IFN-I signaling by preventing the association of ISGF3 and ISRE, thereby contributing to immune evasion. This finding reveals UL42 as a potential antiviral target.

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