Spike density and processing quality are important traits in modern wheat production and are controlled by multiple gene loci. The associated genes have been intensively studied and new discoveries have been constantly reported during the past few decades. However, no gene playing a significant role in the development of these two traits has been identified. In the current study, a common wheat mutant with extremely compact spikes and good processing quality was isolated and characterized. A new allele (Qc1) of the Q gene (an important domestication gene) responsible for the mutant phenotype was cloned, and the molecular mechanism for the mutant phenotype was studied. Results revealed that Qc1 originated from a point mutation that interferes with the miRNA172-directed cleavage of Q transcripts, leading to its overexpression. It also reduces the longitudinal cell size of rachises, resulting in an increased spike density. Furthermore, Qc1 increases the number of vascular bundles, which suggests a higher efficiency in the transportation of assimilates in the spikes of the mutant than that of wild type. This accounts for the improved processing quality. The effects of Qc1 on spike density and wheat processing quality were confirmed by analyzing nine common wheat mutants possessing four different Qc alleles. These results deepen our understanding of the key roles of Q gene, and provide new insights for the potential application of Qc alleles in wheat quality breeding.

Basis for the effects of nitrogen (N) on wheat grain storage proteins (GSPs) and on the establishment of processing quality are far from clear. The response of GSPs and processing quality parameters to four N levels of four common wheat cultivars were investigated at two sites over two growing seasons. Except gluten index (GI), processing quality parameters as well as GSPs quantities were remarkably improved by increasing N level. N level explained 4.2~59.2% and 10.4~80.0% variability in GSPs fractions and processing quality parameters, respectively. The amount of N remobilized from vegetative organs except spike was significantly increased when enhancing N application. GSPs fractions and processing quality parameters except GI were only highly and positively correlated with the amount of N remobilized from stem with sheath. N reassimilation in grain was remarkably strengthened by the elevated activity and expression level of glutamine synthetase. Transcriptome analysis showed the molecular mechanism of seeds in response to N levels during 10~35 days post anthesis. Collectively, we provided comprehensive understanding of N-responding mechanisms with respect to wheat processing quality from N source to GSPs biosynthesis at the agronomic, physiological and molecular levels, and screened candidate genes for quality breeding.

Wireless sensor network (WSN) has been paid more attention due to its efficient system of communication devices for transferring information from a target environment to the base station (BS) through wireless links. Precise collecting information from sensor nodes for aggregating data in Cluster Head (CH) is an essential demand for a successful WSN application. This paper proposes a new scheme of identifying collected information correctness for aggregating data in CHs in hierarchical WSN based on improving classification of Support vector machine (SVM). The optimal parameter SVM is implemented by an improved flower pollination algorithm (IFPA) to achieve classification accuracy. The collecting environmental information like temperature, humidity, etc., from sensor nodes to CHs that classify data fault, aggregate, and transfer them to the BS. Compared with some existing methods, the proposed method offers an effective way of forwarding the correct data in WSN applications.

Background Superoxide dismutase (SOD) proteins, as one kind of the antioxidant enzymes, play critical roles in plant response to various environment stresses. Even though its functions in the oxidative stress were very well characterized, the roles of SOD family genes in regulating alkaline stress response are not fully reported. Methods We identified the potential family members by using Hidden Markov model and soybean genome database. The neighbor-joining phylogenetic tree and exon-intron structures were generated by using software MEGA 5.0 and GSDS online server, respectively. Furthermore, the conserved motifs were analyzed by MEME online server. The syntenic analysis was conducted using Circos-0.69. Additionally, the expression levels of soybean SOD genes under alkaline stress were identified by qRT-PCR. Results In this study, we identified 13 potential SOD genes in soybean genome. Phylogenetic analysis suggested that SOD genes could be classified into three subfamilies, including MnSODs (GmMSD1–2), FeSODs (GmFSD1–5) and Cu/ZnSODs (GmCSD1–6). We further investigated the gene structure, chromosomal locations and gene-duplication, conserved domains and promoter cis-elements of the soybean SOD genes. We also explored the expression profiles of soybean SOD genes in different tissues and alkaline, salt and cold stresses, based on the transcriptome data. In addition, we detected their expression patterns in roots and leaves by qRT-PCR under alkaline stress, and found that different SOD subfamily genes may play different roles in response to alkaline stress. These results also confirmed the hypothesis that the great evolutionary divergence may contribute to the potential functional diversity in soybean SOD genes. Taken together, we established a foundation for further functional characterization of soybean SOD genes in response to alkaline stress in the future.

20Spike density and processing quality are important traits during the evolution of 21 wheat, which is controlled by multiple gene loci. The associated gene loci have been 22