KASP marker technology has been used in molecular marker-assisted breeding because of its high efficiency and flexibility, and an intelligent evaluation model of KASP marker primer typing results is essential to improve the efficiency of marker development on a large scale. To this end, this paper proposes a gene population delineation method based on NTC identification module and data distribution judgment module to improve the accuracy of K-Means clustering, and introduces a decision tree to construct the KASP-IEva primer typing evaluation model. The model firstly designs the NTC identification module and data distribution judgment module to extract four types of data, grouping and categorizing to achieve the improvement of the distinguishability of amplification product signals; secondly, the K-Means algorithm is used to aggregate and classify the data, to visualize the five aggregated clusters and to obtain the morphology location eigenvalues; lastly, the evaluation criteria for the typing effect level are constructed, and the logical decision tree is used to make conditional discrimination on the eigenvalues in order to realize the score prediction. The performance of the model was tested by the KASP marker typing test results of 2519 groups of cotton varieties, and the following conclusions were obtained: the model is able to visualize the aggregation and classification effects of the amplification products of NTC, pure genotypes, heterozygous genotypes, and untyped genotypes, enabling rapid and accurate KASP marker typing evaluation. Comparing and analyzing the model evaluation results with the expert evaluation results, the average accuracy rate of the four grades evaluated by the model was 87%, and the overall evaluation results showed an uneven distribution of the grades with significant differential characteristics. When evaluating 2519 KASP fractal maps, the expert evaluation consumes 15 hours, and the model evaluation only uses 8min27.45s, which makes the model intelligent evaluation significantly better than the expert evaluation from the perspective of time. The establishment of the model will further enhance the application of KASP markers in molecular marker-assisted breeding and provide technical support for the large-scale screening and identification of excellent genotypes.

Rehmannia glutinosa, a member of the Scrophulariaceae family, has been widely used in traditional Chinese medicine since ancient times. The main bioactive component of R. glutinosa is catalpol. However, the biogenesis of catalpol, especially its downstream pathway, remains unclear. To identify candidate genes involved in the biosynthesis of catalpol, transcriptomes were constructed from R. glutinosa using the young leaves of three cultivars, Beijing No. 3, Huaifeng, and Jin No. 9, as well as the tuberous roots and adventitious roots of the Jin No. 9 cultivar. As a result, 71,142 unigenes with functional annotations were generated. A comparative analysis of the R. glutinosa transcriptomes identified over 200 unigenes of 13 enzymes potentially involved in the downstream steps of catalpol formation, including 9 genes encoding UGTs, 13 for aldehyde dehydrogenases, 70 for oxidoreductases, 44 for CYP450s, 22 for dehydratases, 30 for decarboxylases, 19 for hydroxylases, and 10 for epoxidases. Moreover, two novel genes encoding geraniol synthase (RgGES), which is the first committed enzyme in catalpol production, were cloned from R. glutinosa. The purified recombinant proteins of RgGESs effectively converted GPP to geraniol. This study is the first to discover putative genes coding the tailoring enzymes mentioned above in catalpol biosynthesis, and functionally characterize the enzyme-coding gene in this pathway in R. glutinosa. The results enrich genetic resources for engineering the biosynthetic pathway of catalpol and iridoids.

Rehmannia glutinosa is an important medicinal plant that has long been used in Chinese traditional medicine. Acteoside, one of the bioactive components from R. glutinosa, possessed various pharmacological activities for human health; however, the molecular mechanism of acteoside formation is not fully understood. In the current study, a novel tyrosine decarboxylase (designated as RgTyDC2) was identified from the R. glutinosa transcriptome. Biochemical analysis of RgTyDC2 showed RgTyDC2 uses tyrosine and dopa as the substrate to produce tyramine and dopamine, respectively, and it displays higher catalytic efficiency toward tyrosine than dopa. Moreover, the transcript level of RgTyDC2 was consistent with the accumulation pattern of acteoside in R. glutinosa, supporting its possible role in the biosynthesis of acteoside in vivo.

Cotton verticillium wilt significantly impacts both cotton quality and yield. Selecting disease-resistant varieties and using their resistance genes in breeding is an effective and economical control measure. Accurate severity estimation of this disease is crucial for breeding resistant cotton varieties. However, current methods fall short, slowing the breeding process. To address these challenges, this paper introduces CVW-Etr, a high-precision method for estimating the severity of cotton verticillium wilt. CVW-Etr classifies severity into six levels (L0 to L5) based on the proportion of segmented diseased leaves to lesions. Upon integrating YOLOv8-Seg with MobileSAM, CVW-Etr demonstrates excellent performance and efficiency with limited samples in complex field conditions. It incorporates the RFCBAMConv, C2f-RFCBAMConv, AWDownSample-Lite, and GSegment modules to handle blurry transitions between healthy and diseased regions and variations in angle and distance during image collection, and to optimize the model’s parameter size and computational complexity. Our experimental results show that CVW-Etr effectively segments diseased leaves and lesions, achieving a mean average precision (mAP) of 92.90% and an average severity estimation accuracy of 92.92% with only 2.6M parameters and 10.1G FLOPS. Through experiments, CVW-Etr proves robust in estimating cotton verticillium wilt severity, offering valuable insights for disease-resistant cotton breeding applications.

Cottonseeds, rich in high-quality protein and fatty acids, represent a vital plant-derived feedstuff and edible oil resource. To systematically investigate genetic variation patterns in nutritional quality and screen superior germplasm, this study analyzed 26 nutritional quality traits and 8 fiber traits across 259 upland cotton (Gossypium hirsutum L.) accessions using multivariate statistical approaches. Results revealed significant genetic diversity in cottonseed nutritional profiles, with coefficients of variation ranging from 3.42% to 26.37%. Moreover, with advancements in breeding periods, the contents of protein, amino acids, and the proportion of unsaturated fatty acids (UFAs) increased, while oil content and C16:0 levels decreased. Correlation analyses identified significant positive associations (p < 0.05) between proteins, amino acids, UFAs, and most fiber traits, except for seed index (SI), fiber micronaire (FM), and fiber elongation (FE). Through a principal component analysis–fuzzy membership function (PCA-FMF) model, 13 elite accessions (F > 0.75) with high protein content, high UFA proportion, and excellent fiber quality were identified. These findings provide both data-driven foundations and practical germplasm resources for value-added utilization of cottonseed and coordinated breeding for dual-quality traits of nutrition and fiber.

Intelligent and accurate evaluation of KASP primer typing effect is crucial for large-scale screening of excellent markers in molecular marker-assisted breeding. However, the efficiency of both manual discrimination methods and existing algorithms is limited and cannot match the development speed of molecular markers. To address the above problems, we proposed a typing evaluation method for KASP primers by integrating deep learning and traditional machine learning algorithms, called TAL-SRX. First, three algorithms are used to optimize the performance of each model in the Stacking framework respectively, and five-fold cross-validation is used to enhance stability. Then, a hybrid neural network is constructed by combining ANN and LSTM to capture nonlinear relationships and extract complex features, while the Transformer algorithm is introduced to capture global dependencies in high-dimensional feature space. Finally, the two machine learning algorithms are fused through a soft voting integration strategy to output the KASP marker typing effect scores. In this paper, the performance of the model was tested using the KASP test results of 3399 groups of cotton variety resource materials, with an accuracy of 92.83% and an AUC value of 0.9905, indicating that the method has high accuracy, consistency and stability, and the overall performance is better than that of a single model. The performance of the TAL-SRX method is the best when compared with the different integrated combinations of methods. In summary, the TAL-SRX model has good evaluation performance and is very suitable for providing technical support for molecular marker-assisted breeding and other work.