Identifying and selecting high-quality seeds is crucial for improving crop yield.  The purpose of this study was to improve the selection of crop seeds based on separating vital seeds from dead seeds, by predicting the potential germination ability of each seed, and thus improving seed quality.  The methods of oxygen consumption (Q) of seeds and the headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) were evaluated for identifying the viability of individual seeds.  Firstly, the oxygen consumption technique showed clear differences among the values related to respiratory characteristics for seeds that were either vital or not, and the discrimination ability of final oxygen consumption (Q₁₂₀) was achieved not only in sweet corn seeds but also in pepper and wheat seeds.  Besides, Q_t was established as a new variable to shorten the measuring process in the Q2 (oxygen sensor) procedure, which was significantly related to the viability of individual seeds.  To minimize seed damage during measurement, the timing for viability evaluation was pinpointed at the 12, 6 and 9 h for pepper, sweet corn, and wheat seeds based on the new variables concerning oxygen consumption (i.e., Q₁₂, Q₆ and Q₉, respectively).  The accuracies of viability prediction were 91.9, 97.7 and 96.2%, respectively.  Dead seeds were identified and hence discarded, leading to an enhancement in the quality of the seed lot as indicated by an increase in germination percentage, from 86.6, 90.9, and 53.8% to all at 100%.  We then used the HS-GC-IMS to determine the viability of individual sweet corn seeds, noting that corn seed has a heavier weight so the volatile gas components are more likely to be detected.  A total of 48 chromatographic peaks were identified, among which 38 target compounds were characterized, including alcohols, aldehydes, acids and esters.  However, there were no significant differences between the vital and dead seeds, due to the trace amount volatile composition differences among the individual seeds.  Furthermore, a PCA based on the signal intensities of the target volatile compounds obtained was found to lose its effectiveness, as it was unable to distinguish those two types of sweet corn seeds.  These strategies can provide a reference for the rapid detection of single seed viability.

Deep-sowing is an important method for avoiding drought stress in crop species, including maize.  Identifying candidate genes is the groundwork for investigating the molecular mechanism underlying maize deep-sowing tolerance.  This study evaluated four traits (mesocotyl length at 10 and 20 cm planting depths and seedling emergence rate on days 6 and 12) related to deep-sowing tolerance using a large maize population containing 386 inbred lines genotyped with 0.5 million high-quality single nucleotide polymorphisms (SNPs).  The genome-wide association study detected that 273 SNPs were in linkage disequilibrium (LD) with the genetic basis of maize deep-sowing tolerance.  The RNA-sequencing analysis identified 1 944 and 2 098 differentially expressed genes (DEGs) in two comparisons, which shared 281 DEGs.  By comparing the genomic locations of the 273 SNPs with those of the 281 DEGs, we identified seven candidate genes, of which GRMZM2G119769 encoded a sucrose non-fermenting 1 kinase interactor-like protein.  GRMZM2G119769 was selected as the candidate gene because its homologs in other plants were related to organ length, auxin, or light response.  Candidate gene association mapping revealed that natural variations in GRMZM2G119769 were related to phenotypic variations in maize mesocotyl length.  Gene expression of GRMZM2G119769 was higher in deep-sowing tolerant inbred lines.  These results suggest that GRMZM2G119769 is the most likely candidate gene.  This study provides information on the deep-sowing tolerance of maize germplasms and identifies candidate genes, which would be useful for further research on maize deep-sowing tolerance.

This research aimed to improve selection of pepper seeds for separating high-quality seeds from low-quality seeds. Past research has shown that seed vigor is significantly related to the seed color and size, thus several physical features were identified as candidate predictors of high seed quality. Image recognition software was used to automate recognition of seed feature quality using 400 kernels of pepper cultivar 101. In addition, binary logistic regression and a neural network were applied to determine models with high predictive value of seed germination. Single-kernel germination tests were conducted to validate the predictive value of the identified features. The best predictors of seed vigor were determined by the highest correlation observed between the physical features and the subsequent fresh weight of seedlings that germinated from the 400 seeds. Correlation analysis showed that fresh weight was significantly positively correlated with eight physical features: three color features (R, a*, brightness), width, length, projected area, and single-kernel density, and weight. In contrast, fresh weight significantly negatively correlated with the feature of hue. In analyses of two of the highest correlating single features, germination percentage increased from 59.3 to 71.8% when a*≥3, and selection rate peaked at 57.8%. Germination percentage increased from 59.3 to 79.4%, and the selection rate reached 76.8%, when single-kernel weight ≥0.0064 g. The most effective model was based on a multilayer perceptron (MLP) neural network, consisting of 15 physical traits as variables, and a stability calculated as 99.4%. Germination percentage in a calibration set of seeds was 79.1% and the selection rate was 90.0%. These results indicated that the model was effective in predicting seed germination based on physical features and could be used as a guide for quality control in seed selection. Automated systems based on machine vision and model classifiers can contribute to reducing the costs and labor required in the selection of pepper seeds.

Sucrose non-fermenting-1 related protein kinase 2 (SnRK2) is a unique family of protein kinases associated with abiotic stress signal transduction in plants. In this study, a maize SnRK2 gene ZmSnRK2.11 was cloned and characterized. The results showed that ZmSnRK2.11 is up-regulated by high-salinity and dehydration treatment, and it is expressed mainly in maize mature leaf. A transient expression assay using onion epidermal cells revealed that ZmSnRK2.11-GFP fusion proteins are localized to both the nucleus and cytoplasm. Overexpressing-ZmSnRK2.11 in Arabidopsis resulted in salt and drought sensitivity phenotypes that exhibited an increased rate of water loss, reduced relative water content, delayed stoma closure, accumulated less free proline content and increased malondialdehyde (MDA) content relative to the phenotypes observed in wild-type (WT) control. Furthermore, overexpression of ZmSnRK2.11 up-regulated the expression of the genes ABI1 and ABI2 and decreased the expression of DREB2A and P5CS1. Taken together, our results suggest that ZmSnRK2.11 is a possible negative regulator involved in the salt and drought stress signal transduction pathways in plants.

The function of the 3 040 bp sequence at the upstream translation starting site (ATG) of the ZAG2 gene, isolated from the maize genome, was studied. The sequence analysis showed that the sequence contained a typical class C MADS-box gene regulatory element. The 5´ UTR region of the gene contains a 1 299-bp intron that might have important regulatory functions. To study the sequence function, deletion derivatives of promoter-reporter (uidA) gene fusions were generated and transformed into tobaccos. The GUS staining and fluorescence quantification results showed that the GUS activity was detected only in the third and fourth whorl floral organs of the transgenic tobaccos under driving the promoter including the first intron, while detected in all the organs and was stronger under driving the promoter without the first intron. However, the GUS activity was just detected in one whorl of the fourth or third floral organs under driving of the 35S promoter. These results suggested that the first intron of the ZAG2 gene contains functional regulatory elements, which turned out to be important for gene expression in the heterologous systems. Moreover, the GUS activity was decreased when the reporter gene driven by the promoters with 5´-deletions, respectively, from -1 606 to -951 and -951 to -426 nts, which indicates that positive regulatory elements are present in these two sequence stretches.