Milling and appearance quality are important contributors to rice grain quality.  Abundant genetic diversity and a suitable environment are crucial for rice improvement.  In this study, we investigated the milling and appearance quality-related traits in a panel of 200 japonica rice cultivars selected from Liaoning, Jilin and Heilongjiang provinces in Northeast China.  Pedigree assessment and genetic diversity analysis indicated that cultivars from Jilin harbored the highest genetic diversity among the three geographic regions.  An evaluation of grain quality indicated that cultivars from Liaoning showed superior milling quality, whereas cultivars from Heilongjiang tended to exhibit superior appearance quality.  Single- and multi-locus genome-wide association studies (GWAS) were conducted to identify loci associated with milling and appearance quality-related traits.  Ninety-nine significant single-nucleotide polymorphisms (SNPs) were detected.  Three common SNPs were detected using the mixed linear model (MLM), mrMLM, and FASTmrMLM methods.  Linkage disequilibrium decay was estimated and indicated three candidate regions (qBRR-1, qBRR-9 and qDEC-3) for further candidate gene analysis.  More than 300 genes were located in these candidate regions.  Gene Ontology (GO) analysis was performed to discover the potential candidate genes.  Genetic diversity analysis of the candidate regions revealed that qBRR-9 may have been subject to strong selection during breeding.  These results provide information that will be valuable for the improvement of grain quality in rice breeding.

Understanding the spatial-temporal dynamics of crop nitrogen (N) use efficiency (NUE) and the relationship with explanatory environmental variables can support land-use management and policymaking.  Nevertheless, the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-researched.  In this study, stepwise multiple linear regression (SMLR) and Random Forest (RF) were used to evaluate the spatial and temporal variation of NUE indicators (i.e., partial factor productivity of N (PFP_N); partial nutrient balance of N (PNB_N)) at county scale in Northeast China (Heilongjiang, Liaoning and Jilin provinces) from 1990 to 2015.  Explanatory variables included agricultural management practices, topography, climate, economy, soil and crop types.  Results revealed that the PFP_N was higher in the northern parts and lower in the center of the Northeast China and PNB_N increased from southern to northern parts during the 1990–2015 period.  The NUE indicators decreased with time in most counties during the study period.  The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFP_N, and 0.67 and 0.89 for PNB_N, respectively.  The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model.  The planting area index of vegetables and beans, soil clay content, saturated water content, enhanced vegetation index in November & December, soil bulk density, and annual minimum temperature were the main explanatory variables for both NUE indicators.  This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR.  This novel study gives reference measurements to improve crop NUE which is one of the most effective means of managing N for sustainable development, ensuring food security, alleviating environmental degradation and increasing farmer’s profitability.

A crop growth model, integrating genotype, environment, and management factor, was developed to serve as an analytical tool to study the influence of these factors on crop growth, production, and agricultural planning.  A major challenge of model application is the optimization and calibration of a considerable number of parameters.  Sensitivity analysis (SA) has become an effective method to identify the importance of various parameters.  In this study, the extended Fourier Amplitude Sensitivity Test (EFAST) approach was used to evaluate the sensitivity of the DSSAT-CERES model output responses of interest to 39 crop genotype parameters and six soil parameters.  The outputs for the SA included grain yield and quality (take grain protein content (GPC) as an indicator) at maturity stage, as well as leaf area index, aboveground biomass, and aboveground nitrogen accumulation at the critical process variables.  The key results showed that: (1) the influence of parameter bounds on the sensitivity results was slight and less than the impacts from the significance of the parameters themselves; (2) the sensitivity parameters of grain yield and GPC were different, and the sensitivity of the interactions between parameters to GPC was greater than those between the parameters to grain yield; and (3) the sensitivity analyses of some process variables, including leaf area index, aboveground biomass, and aboveground nitrogen accumulation, should be performed differently.  Finally, some parameters, which improve the model’s structure and the accuracy of the process simulation, should not be ignored when maturity output as an objective variable is studied.

Sensitivity analysis (SA) is an effective tool for studying crop models; it is an important link in model localization and plays an important role in crop model calibration and application.  The objectives were to (i) determine influential and non-influential parameters with respect to above ground biomass (AGB), canopy cover (CC), and grain yield of winter wheat in the Beijing area based on the AquaCrop model under different water treatments (rainfall, normal irrigation, and over-irrigation); and (ii) generate an AquaCrop model that can be used in the Beijing area by setting non-influential parameters to fixed values and adjusting influential parameters according to the SA results.  In this study, field experiments were conducted during the 2012–2013, 2013–2014, and 2014–2015 winter wheat growing seasons at the National Precision Agriculture Demonstration Research Base in Beijing, China.  The extended Fourier amplitude sensitivity test (EFAST) method was used to perform SA of the AquaCrop model using 42 crop parameters, in order to verify the SA results, data from the 2013–2014 growing season were used to calibrate the AquaCrop model, and data from 2012–2013 and 2014–2015 growing seasons were validated.  For AGB and yield of winter wheat, the total order sensitivity analysis had more sensitive parameters than the first order sensitivity analysis.  For the AGB time-series, parameter sensitivity was changed under different water treatments; in comparison with the non-stressful conditions (normal irrigation and over-irrigation), there were more sensitive parameters under water stress (rainfall), while root development parameters were more sensitive.  For CC with time-series and yield, there were more sensitive parameters under water stress than under no water stress.  Two parameters sets were selected to calibrate the AquaCrop model, one group of parameters were under water stress, and the others were under no water stress, there were two more sensitive parameters (growing degree-days (GDD) from sowing to the maximum rooting depth (root) and the maximum effective rooting depth (rtx)) under water stress than under no water stress.  The results showed that there was higher accuracy under water stress than under no water stress.  This study provides guidelines for AquaCrop model calibration and application in Beijing, China, as well providing guidance to simplify the AquaCrop model and improve its precision, especially when many parameters are used.  

Phenazines are secondary metabolites with broad spectrum antibiotic activity and thus show high potential in biological control of pathogens. In this study, we identified phenazine biosynthesis (phz) genes in two genome-completed plant pathogenic bacteria Pseudomonas syringae pv. tomato (Pst) DC3000 and Xanthomonas oryzae pv. oryzae (Xoo) PXO99^A. Unlike the phz genes in typical phenazine-producing pseudomonads, phz homologs in Pst DC3000 and Xoo PXO99^A consisted of phzC/D/E/F/G and phzC/E1/E2/F/G, respectively, and the both were not organized into an operon. Detection experiments demonstrated that phenazine-1-carboxylic acid (PCA) of Pst DC3000 accumulated to 13.4 μg L^–1, while that of Xoo PXO99^A was almost undetectable. Moreover, Pst DC3000 was resistant to 1 mg mL^–1 PCA, while Xoo PXO99^A was sensitive to 50 μg mL^–1 PCA. Furthermore, mutation of phzF blocked the PCA production and significantly reduced the pathogenicity of Pst DC3000 in tomato, while the complementary strains restored these phenotypes. These results revealed that Pst DC3000 produces low level of and is resistant to phenazines and thus is unable to be biologically controlled by phenazines. Additionally, phz-mediated PCA production is required for full pathogenicity of Pst DC3000. To our knowledge, this is the first report of PCA production and its function in pathogenicity of a plant pathogenic P. syringae strain.

In order to study the molecular mechanism involved in cashmere regeneration, this study investigated the gene expression profile of skin tissue at various stages of the cashmere growth cycle and screen differentially expressed genes at proangen in 10 cashmere goats at 2 years of age using agilent sheep oligo microarray. Significance analysis of microarray (SAM) methods was used to identify the differentially expressed genes, Hierarchical clustering was performed to clarify these genes in association with different cashmere growth stages, and GO (Gene ontology) and the pathway analyses were con-ducted by a free web-based Molecular Annotation System3.0 (MAS 3.0). Approximately 10200 probe sets were detected in skin tissue of 2-yr-old cashmere goat. After SAM analysis of the microarray data, totally 417 genes were shown to be differentially expressed at different cashmere growth stages, and 24 genes are significantly up-regulated (21) or down-regulated (3) at proangen concurrently compared to angen and telogen. Hierarchical clustering analysis clearly distinguished the differentially expressed genes of each stage. GO analysis indicated that these altered genes at proangen were predominantly involved in collagen fibril organization, integrin-mediated signaling pathway, cell-matrix adhesion, cell adhesion, transforming growth factor-β (TGF-β) receptor signaling pathway, regulation of cell growth. Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that the significant pathways involved mainly included focal adhesion and extracellular matrixc (ECM)-receptor interaction. Some important genes involved in these biological processes, such as COL1A1, COL1A2, COL3A1, SPARC, CYR61 and CTGF, were related to tissue remolding and repairing and detected by more than one probe with similar expression trends at different stages of cashmere growth cycle. The different expression of these genes may contribute to understanding the molecular mechanism of cashmere regeneration.

Winter wheat freeze injury is one of the main agro-meteorological disasters affecting wheat production. In order to evaluate the severity of freeze injury on winter wheat systematically, we proposed a grey-system model (GSM) to monitor the degree and the distribution of the winter wheat freeze injury. The model combines remote sensing (RS) and geographic information system (GIS) technology. It gave examples of wheat freeze injury monitoring applications in Gaocheng and Jinzhou of Hebei Province, China. We carried out a quantitative evaluation method study on the severity of winter wheat freeze injury. First, a grey relational analysis (GRA) was conducted. At the same time, the weights of the stressful factors were determined. Then a wheat freezing injury stress multiple factor spatial matrix was constructed using spatial interpolation technology. Finally, a winter wheat freeze damage evaluation model was established through grey clustering algorithm (GCA), and classifying the study area into three sub-areas, affected by severe, medium or light disasters. The evaluation model were verified by the Kappa model, the overall accuracy reached 78.82% and the Kappa coefficient was 0.6754. Therefore, through integration of GSM with RS images as well as GIS analysis, quantitative evaluation and study of winter wheat freeze disasters can be conducted objectively and accurately, making the evaluation model more scientific.

Pale, soft, exudative-like (PSE-like) broiler muscle is a growing problem for meat industry all over the world. However, limited studies have been made to assess broiler meat quality in China. The aim of this study was to investigate the characteristics and incidence of PSE-like broiler muscle commercially produced in China. A total of 1 274 Pectoralis muscles of Arbor Acre broiler were randomly obtained from the processing line to determine the commercial incidence of PSE-like muscle based on color. Furthermore, broiler Pectoralis muscles selected from the 1 274 muscle samples were classified as PSE-like muscle (L*>53, n=33) and normal muscle (L*>48 and L*=53, n=33) to assess meat quality. It was determined that PSE-like muscle had lower muscle pH values, lower water-holding capacity (WHC), lower sarcoplasmic protein solubility, and lower total protein solubility than the normal muscle did. SDS-PAGE profile also showed that bands of approximate 96 and 24 kDa in sarcoplasmic protein and myofibrillar protein varied between these two groups, suggesting partial denaturation of sarcoplasmic proteins and precipitation on myofillarments. Correlation analysis showed that L* values have significant correlation with WHC and protein solubility. Furthermore, the distribution of L* values exhibited a normal curve with range varying from 42.70 to 58.37. It was considered that approximately 23.39% of the population was PSE-like muscle. These results suggest that PSE-like meat can represent a significant portion of commercially processed broiler breast meat in China and that the L* value measurement could be used to sort broiler meat quality using a cut-off point.

The host intestinal microbiota has emerged as the third element in the interactions between hosts and enteric viruses, and potentially affects the infection processes of enteric viruses. However, the interaction of porcine enteric coronavirus with intestinal microorganisms during infection remains unclear. In this study, we used 16S-rRNA-based Illumina NovaSeq high-throughput sequencing to identify the changes in the intestinal microbiota of piglets mediated by porcine epidemic diarrhea virus (PEDV) infection and the effects of the alterations in intestinal bacteria on PEDV infection and its molecular mechanisms. The intestinal microbiota of PEDV-infected piglets had significantly less diversity than the healthy group and different bacterial community characteristics. Among the altered intestinal bacteria, the relative abundance of Clostridium perfringens was significantly increased in the PEDV-infected group. A strain of C. perfringens type A, named DQ21, was successfully isolated from the intestines of healthy piglets. The metabolites of swine C. perfringens type A strain DQ21 significantly enhanced PEDV replication in porcine intestinal epithelial cell clone J2 (IPEC-J2) cells, and PEDV infection and pathogenicity in suckling piglets. Palmitic acid (PA) was identified as one of those metabolites with metabolomic technology, and significantly enhanced PEDV replication in IPEC-J2 cells and PEDV infection and pathogenicity in suckling piglets. PA also increased the neutralizing antibody titer in the immune sera of mice. Furthermore, PA mediated the palmitoylation of the PEDV S protein, which improved virion stability and membrane fusion, thereby enhancing viral infection. Overall, our study demonstrates a novel mechanism of PEDV infection, with implications for PEDV pathogenicity.