Identification of sunflower (Helianthus annuus, Asteraceae) hybrids using simple-sequence repeat markers

Identification of the Efficacy of Ex Situ Conservation of Ammopiptanthus nanus Based on Its ETS-SSR Markers

Ammopiptanthus possesses ancestral traits and, as a tertiary relict, is one of the surviving remnants of the ancient Mediterranean retreat and climate drought. It is also the only genus of super xerophytic, evergreen, broad-leaved shrubs. Ammopiptanthus nanus, one of the two species in this genus, is predominantly found in extremely arid and frigid environments, and is increasingly threatened with extinction. Study of the species' genetic diversity is thus beneficial for its survival and the efficacy of ex situ conservation efforts. Based on transcriptome data, 15 pairs of effective EST-SSR were screened to evaluate A. nanus genetic diversity. In all, 87 samples from three populations were evaluated, the results of which show that ex situ conservation in the Wuqia region needs to be supplemented. Conservation and breeding of individual A. nanus offspring should be strengthened in the future to ensure their progeny continue to exhibit high genetic diversity and variation.

Applications of machine learning in pine nuts classification

Pine nuts are not only the important agent of pine reproduction and afforestation, but also the commonly consumed nut with high nutritive values. However, it is difficult to distinguish among pine nuts due to the morphological similarity among species. Therefore, it is important to improve the quality of pine nuts and solve the adulteration problem quickly and non-destructively. In this study, seven pine nuts (Pinus bungeana, Pinus yunnanensis, Pinus thunbergii, Pinus armandii, Pinus massoniana, Pinus elliottii and Pinus taiwanensis) were used as study species. 210 near-infrared (NIR) spectra were collected from the seven species of pine nuts, five machine learning methods (Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB)) were used to identify species of pine nuts. 303 images were used to collect morphological data to construct a classification model based on five convolutional neural network (CNN) models (VGG16, VGG19, Xception, InceptionV3 and ResNet50). The experimental results of NIR spectroscopy show the best classification model is MLP and the accuracy is closed to 0.99. Another experimental result of images shows the best classification model is InceptionV3 and the accuracy is closed to 0.964. Four important range of wavebands, 951–957 nm, 1,147–1,154 nm, 1,907–1,927 nm, 2,227–2,254 nm, were found to be highly related to the classification of pine nuts. This study shows that machine learning is effective for the classification of pine nuts, providing solutions and scientific methods for rapid, non-destructive and accurate classification of different species of pine nuts.

Molecular characterization and validation of sunflower (Helianthus annuus L.) hybrids through SSR markers

Genetic purity is a prerequisite for exploiting the potential of hybrids in cross-pollinated crops, such as sunflower. In this regard DNA-based study was conducted using 110 simple sequence repeat (SSR) markers to check the genetic purity of 23 parents and their 60 hybrids in sunflower. The polymorphism was shown in 92 markers with value 83.63%. The SSR markers ORS-453 and CO-306 showed the highest PIC values of 0.76 and 0.74, respectively. The primer ORS-453 amplified allele size of 310 base pairs (bp) for female parent L6 and 320 bp for L11, while for male parents, T1 and T2 had allele size 350 bp and 340 bp, respectively. The hybrids from these parents showed a similar size of alleles with parents, including hybrids L6×T1 (310 bp and 350 bp), L6×T2 (310 bp and 340 bp), and L11×T2 (320 bp and 340 bp). Similarly, the primer CO-306 amplified allele size 350 bp and 330 bp for female parents L6 and L11, respectively, while, allele size 300 bp and 310 bp for male parents T1 and T2, respectively. The hybrids’ allele size was like the parents viz., L6×T1 (350 bp and 300 bp), L6×T2 (350 bp and 310 bp), and L11×T2 (330 bp and 310 bp). All 60 hybrids and their 23 parents were grouped into three main clusters (A, B and C) based upon DARWIN v.6.0 and STRUCTURE v.2.3 Bayesian analyses using genotypic data. Further, each main cluster was divided into two sub-divisions. Each sub-division showed the relatedness of parents and their hybrids, thus authenticating the genetic purity of hybrids. In conclusion, this study provides useful for accurate and effective identification of hybrids, which will help to improve seed genetic purity testing globally.

A Rapid and Highly Efficient Method for the Identification of Soybean Seed Varieties: Hyperspectral Images Combined with Transfer Learning

Convolutional neural network (CNN) can be used to quickly identify crop seed varieties. 1200 seeds of ten soybean varieties were selected, hyperspectral images of both the front and the back of the seeds were collected, and the reflectance of soybean was derived from the hyperspectral images. A total of 9600 images were obtained after data augmentation, and the images were divided into a training set, validation set, and test set with a 3:1:1 ratio. Pretrained models (AlexNet, ResNet18, Xception, InceptionV3, DenseNet201, and NASNetLarge) after fine-tuning were used for transfer training. The optimal CNN model for soybean seed variety identification was selected. Furthermore, the traditional machine learning models for soybean seed variety identification were established by using reflectance as input. The results show that the six models all achieved 91% accuracy in the validation set and achieved accuracy values of 90.6%, 94.5%, 95.4%, 95.6%, 96.8%, and 97.2%, respectively, in the test set. This method is better than the identification of soybean seed varieties based on hyperspectral reflectance. The experimental results support a novel method for identifying soybean seeds rapidly and accurately, and this method also provides a good reference for the identification of other crop seeds.

The newly developed single nucleotide polymorphism (SNP) markers for a potentially medicinal plant, Crepidiastrum denticulatum (Asteraceae), inferred from complete chloroplast genome data

Medicinal effects of Crepidiastrum denticulatum have been previously reported. However, the genomic resources of this species and its applications have not been studied. In this study, based on the next generation sequencing method (Miseq sequencing system), we characterize the chloroplast genome of C. denticulatum which contains a large single copy (84,112 bp) and a small single copy (18,519 bp), separated by two inverted repeat regions (25,074 bp). This genome consists of 80 protein-coding gene, 30 tRNAs, and four rRNAs. Notably, the trnT_GGU is pseudogenized because of a small insertion within the coding region. Comparative genomic analysis reveals a high similarity among Asteraceae taxa. However, the junctions between LSC, SSC, and IRs locate in different positions within rps19 and ycf1 among examined species. Also, we describe a newly developed single nucleotide polymorphism (SNP) marker for C. denticulatum based on amplification-refractory mutation system (ARMS) technique. The markers, inferred from SNP in rbcL and matK genes, show effectiveness to recognize C. denticulatum from other related taxa through simple PCR protocol. The chloroplast genome-based molecular markers are effective to distinguish a potentially medicinal species, C. denticulatum, from other related taxa. Additionally, the complete chloroplast genome of C. denticulatum provides initial genomic data for further studies on phylogenomics, population genetics, and evolutionary history of Crepidiastrum as well as other taxa in Asteraceae.

Hybrid identification and genetic variation of Elymus sibiricus hybrid populations using EST-SSR markers

Background

Elymus sibiricus is an important native grass in Qinghai-Tibetan Plateau. Seed shattering is a serious problem for E. sibiricus, especially at harvest time. Cross breeding is an effective way to create new varieties with beneficial characteristic or improved traits, and to broaden genetic base.

Results

In this study, we created five hybrid populations by crossing seven E. sibiricus genotypes that have seed shattering variation. Then, nine EST-SSR primers were used for hybrid identification based on DNA fingerprinting, and genetic diversity analysis of hybrid populations and their respective parents. A total of 15 hybrids were identified. An analysis of amplified polymorphic bands among genuine hybrids and their respective parents revealed mainly two types of markers: 1) hybrids shared bands exclusively amplified in both parents; 2)hybrids shared bands exclusively amplified in male parents. For each hybrid population, the total number of amplified bands ranged from 37 to 57, the percentage of polymorphism varied from 65.12% to 75.68%, with an average of 70.51%. Novel bands found in each hybrid population varied from 0 to 9 bands, suggesting an occurrence of rearrangements in the hybrid population. The structure analysis revealed that all hybrid populations and parents were assigned to eight groups. The principal coordinate analysis (PCoA) showed similar results.

Conclusions

In general, this study proved EST-SSR markers are efficient for hybrid identification, and suggested more genetic variation could be captured in hybrid populations by crossing breeding.