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.

Flesh firmness (FF) is an important and complex trait for melon breeders and consumers.  However, the genetic mechanism underlying FF is unclear.  Here, a soft fruit melon (P5) and a hard fruit melon (P10) were crossed to generate F₂, and the FF and fruit-related traits were recorded for two years.  By performing quantitative trait locus (QTL) specific-locus amplified fragment (SLAF) (QTL-SLAF) sequencing and molecular marker-linkage analysis, 112 844 SLAF markers were identified, and 5 919 SNPs were used to construct a genetic linkage map with a total genetic distance of 1 356.49 cM.  Ten FF- and fruit-related QTLs were identified.  Consistent QTLs were detected for fruit length (FL) and fruit diameter (FD) in both years, and QTLs for single fruit weight (SFW) were detected on two separate chromosomes in both years.  For FF, the consistent major locus (ff2.1) was located in a 0.17-Mb candidate region on chromosome 2.  Using 429 F₂ individuals derived from a cross between P5 and P10, we refined the ff2.1 locus to a 28.3-kb region harboring three functional genes.  These results provide not only a new candidate QTL for melon FF breeding but also a theoretical foundation for research on the mechanism underlying melon gene function.

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.

  To investigate the roles of Escherichia coli in the pathogenesis of postpartum uterine diseases in dairy cows, a total of 145 E. coli isolates were recovered from 18 healthy cows (61 isolates) and 25 cows with clinical endometritis (84 isolates) at 25–35 days after parturition. Genomic characteristics including phylogenetic grouping, genetic diversity and virulence genes of E. coli isolates were screened to profile the characteristics related to uterine infections. The susceptibility of the bacteria against 23 antibiotics was also evaluated to support prevention and treatment of clinical cases. Genetic diversity of E. coli identified by random amplification of polymorphic DNA (RAPD) revealed 103 clonal types, including 3 common types to unaffected cows and endometritis cows, 39 types specific to healthy cows and 61 types in endometritis subjects. In addition, the isolates from endometritis uteri showed more genetic variability compared with that of healthy cows. According to the findings of phylogenetic grouping, the E. coli isolates were assigned to group A (35.9%), B1 (59.3%) and D (4.8%). The expression of 10 of 20 virulence gens were detected positively, and only fimH gene was revealed significantly (P<0.05) associated with endometritis. From antimicrobial susceptibility test, E. coli was found highly resistant to tetracycline, ampicillin, carbenicillin and amoxicillin, but sensitive to amikacin, netilmicin, tobramycin, cefepime and ceftazidime. In conclusion, E. coli were extensively observed in both healthy and endometritis cows, and presented a large clonal types, however, fimH was the only gene observed associated with clinical endometritis. Our results suggest that the drugs like amikacin, netilmicin, tobramycin and cefepime could be considered for preventing and treating clinical endometritis in the practical management of dairy cow.