We developed an H5/H7 trivalent inactivated vaccine by using Re-11, Re-12, and H7-Re2 vaccine seed viruses, which were generated by reverse genetics and derived their HA genes from A/duck/Guizhou/S4184/2017(H5N6) (DK/GZ/S4184/17) (a clade 2.3.4.4d virus), A/chicken/Liaoning/SD007/2017(H5N1) (CK/LN/SD007/17) (a clade 2.3.2.1d virus), and A/chicken/Guangxi/SD098/2017(H7N9) (CK/GX/SD098/17), respectively.  The protective efficacy of this novel vaccine and that of the recently used H5/H7 bivalent inactivated vaccine against different H5 and H7N9 viruses was evaluated in chickens.  We found that the H5/H7 bivalent vaccine provided solid protection against the H7N9 virus CK/GX/SD098/17, but only 50–60% protection against different H5 viruses.  In contrast, the novel H5/H7 trivalent vaccine provided complete protection against the H5 and H7 viruses tested.  Our study underscores the importance of timely updating of vaccines for avian influenza control.

The aim of this study was to elucidate the effects of different machine-harvested cotton-planting patterns on defoliation, yield, and fiber quality in cotton and to provide support for improving the quality of machine-harvested cotton.  In the 2015 and 2016 growing seasons, the Xinluzao 45 (XLZ45) and Xinluzao 62 (XLZ62) cultivars, which are primarily cultivated in northern Xinjiang, were used as study materials.  Conventional wide-narrow row (WNR), wide and ultra-narrow row (UNR), wide-row spacing with high density (HWR), and wide-row spacing with low density (LWR) planting patterns were used to assess the effects of planting patterns on defoliation, yield, and fiber quality.  Compared with WNR, the seed cotton yields were significantly decreased by 2.06–5.48% for UNR and by 2.50–6.99% for LWR, respectively.  The main cause of reduced yield was a reduction in bolls per unit area.  The variation in HWR yield was –1.07–1.07% with reduced bolls per unit area and increased boll weight, thus demonstrating stable production.  In terms of fiber quality indicators, the planting patterns only showed significant effects on the micronaire value, with wide-row spacing patterns showing an increase in the micronaire values.  The defoliation and boll-opening results showed that the number of leaves and dried leaves in HWR was the lowest among the four planting patterns.  Prior to the application of defoliating agent and before machine-harvesting, the numbers of leaves per individual plant in HWR were decreased by 14.45 and 25.00% on average, respectively, compared with WNR, while the number of leaves per unit area was decreased by 27.44 and 36.21% on average, respectively.  The rates of boll-opening and defoliation in HWR were the highest.  Specifically, the boll-opening rate before defoliation and machine-harvesting in HWR was 44.54 and 5.94% higher on average than in WNR, while the defoliation rate prior to machine-harvesting was 3.45% higher on average than in WNR.  The numbers of ineffective defoliated leaves and leaf trash in HWR were the lowest, decreased by 33.40 and 32.43%, respectively, compared with WNR.  In conclusion, the HWR planting pattern is associated with a high and stable yield, does not affect fiber quality, promotes early maturation, and can effectively decrease the amount of leaf trash in machine-picked seed cotton, and thus its use is able to improve the quality of machine-harvested cotton.

Leguminous crops play a vital role in enhancing crop yield and improving soil fertility.  Therefore, it can be used as an organic N source for improving soil fertility.  The purpose of this study was to (i) quantify the amounts of N derived from rhizodeposition, root and above-ground biomass of peanut residue in comparison with wheat and (ii) estimate the effect of the residual N on the wheat-growing season in the subsequent year.  The plants of peanut and wheat were stem fed with ¹⁵N urea using the cotton-wick method at the Wuqiao Station of China Agricultural University in 2014.  The experiment consisted of four residue-returning strategies in a randomized complete-block design: (i) no return of crop residue (CR0); (ii) return of above-ground biomass of peanut crop (CR1); (iii) return of peanut root biomass (CR2); and (iv) return of all residue of the whole peanut plant (CR3).  The 31.5 and 21% of the labeled ¹⁵N isotope were accumulated in the above-ground tissues (leaves and stems) of peanuts and wheat, respectively.  N rhizodeposition of peanuts and wheat accounted for 14.91 and 3.61% of the BG15N, respectively.  The ¹⁵N from the below-ground ¹⁵N -labeled of peanuts were supplied 11.3, 5.9, 13.5, and 6.1% of in the CR0, CR1, CR2, and CR3 treatments, respectively.  Peanut straw contributes a significant proportion of N to the soil through the decomposition of plant residues and N rhizodeposition.  With the current production level on the NCP, it is estimated that peanut straw can potentially replace 104 500 tons of synthetic N fertilizer per year.  The inclusion of peanut in rotation with cereal can significantly reduce the use of N fertilizer and enhance the system sustainability.

 

Understanding of moisture changes in fresh ear corn (Zea mays L.) during storage is imperative for maintaining fresh corn quality.  The changes of moisture distribution and migration in fresh ear corn during storage were investigated using low-field nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI).  Water loss was greater than water migration in fresh ear corn within the first hour of storage; thereafter, water loss was weaker than water migration.  With the extension of storage time, the signal intensity of MRI in different parts of sliced fresh corn with cob showed a downward trend, and the rate of signal intensity reduction was higher in the peripheral area than at the central part of sliced fresh corn with cob.  The relative proportion of bound water increased with a concomitant drop in that of free water, when the total water content reduced in fresh ear corn under storage.  In conclusion, NMR and MRI are useful and non-destructive tools for real-time monitoring of moisture distribution, migration, and loss in fresh ear corn during storage to assess its quality.  These results can be used for future design of the preserving and processing conditions for fresh ear corn.

Synonymous codon usage pattern presumably reflects gene expression optimization as a result of molecular evolution.  Though much attention has been paid to various model organisms ranging from prokaryotes to eukaryotes, codon usage has yet been extensively investigated for model legume Medicago truncatula.  In present study, 39 531 available coding sequences (CDSs) from M. truncatula were examined for codon usage bias (CUB).  Based on analyses including neutrality plots, effective number of codons plots, and correlations between optimal codons frequency and codon adaptation index, we conclude that natural selection is a major driving force in M. truncatula CUB.  We have identified 30 optimal codons encoding 18 amino acids based on relative synonymous codon usage.  These optimal codons characteristically end with A or T, except for AGG and TTG encoding arginine and leucine respectively.  Optimal codon usage is positively correlated with the GC content at three nucleotide positions of codons and the GC content of CDSs.  The abundance of expressed sequence tag is a proxy for gene expression intensity in the legume, but has no relatedness with either CDS length or GC content.  Collectively, we unravel the synonymous codon usage pattern in M. truncatula, which may serve as the valuable information on genetic engineering of the model legume and forage crop.