Members of the 14-3-3 protein family regulate lipid biosynthesis through interactions with transcription factors such as WRINKLED1 (WRI1); however, the roles of the Eg14-3-3 isoforms in oil palm remain poorly understood. In this study, we investigated whether Eg14-3-3 proteins play a role in oil biosynthesis and evaluated their interaction with EgWRI1. Subcellular localization predictions indicated that Eg14-3-3 proteins are present in both the cytoplasm and the nucleus. Protein-protein interaction analyses revealed that several Eg14-3-3 isoforms interact with EgWRI1, with the interaction of the Eg14-3-3-like protein D isoform X2 being the strongest. Functional analysis revealed that transient expression of the Eg14-3-3-like protein D isoform X2 significantly increased oil accumulation. Furthermore, high-resolution melting (HRM) markers targeting the Eg14-3-3-like protein D isoform X2 and EgWRI1 could effectively distinguish between low-and high-yielding cultivars. Overall, the integration of functional genomics and HRM-based marker development represents an effective strategy for accelerating oil palm breeding.
Members of the 14?3?3 protein family regulate lipid biosynthesis through interactions with transcription factors such as WRINKLED1 (WRI1); however, the roles of Eg14?3?3 isoforms in oil palm remain poorly understood. In this study, we investigated whether Eg14?3?3 proteins contribute to oil biosynthesis and evaluated their interaction with EgWRI1. Subcellular localization predictions indicated that Eg14?3?3 proteins are present in both the cytoplasm and the nucleus. Protein–protein interaction analyses revealed that several Eg14?3?3 isoforms interact with EgWRI1, with the Eg14?3?3?like protein D isoform X2 showing the strongest interaction. Functional analysis demonstrated that transient expression of the Eg14?3?3?like protein D isoform X2 significantly increased oil accumulation. Furthermore, high?resolution melting (HRM) markers targeting the Eg14?3?3?like protein D isoform X2 and EgWRI1 effectively distinguished between low? and high?yielding cultivars. Overall, the integration of functional genomics and HRM?based marker development represents an effective strategy for accelerating oil palm breeding.
Accurate classification of wheat genotypes for adaptability and stability across diverse environments remains challenging in multi-environment trials. This study integrated mixed models, genotype plus genotype & times; environment and supervised machine learning to evaluate wheat genotype performance and improve ER-based classification. Seventy wheat genotypes were tested Trials (2023-2024). Genotype and environment effects were highly significant (p < 0.01), while genotype & times; environment interaction was significant (p < 0.05), confirming differential genotype responses. GGE biplot analysis identified rameters classified genotypes into six stability classes. After SMOTE, PCA, and hyperparameter tuning, the optimized support vector machine achieved the best performance, with 94.4% accuracy and strong agreement with ER classes. This integrated framework supports the selection of stable, high-yielding wheat genotypes.
This study proposes a machine-learning approach to predict soybean maturity using climatic variables and unmanned aerial vehicle (UAV) data, focusing on Brazilian high-altitude tropical conditions. The objective was to evaluate and compare different models for predicting days to full maturity, first using Eight algorithms were initially tested, and the three best-performing models were re-evaluated with inclusion of the GLI. Model performance was assessed dom forest (RF), support vector machine (SVM), and k-nearest neighbors (KNN) showed the highest accuracy, with RF outperforming the others. The inclusion of the GLI improved predictions by capturing the decline in canopy greenness. These findings demonstrate that combining machine learning, climatic data, and vegetation indices is an efficient, low-cost approach for predicting soybean maturity, supporting high-throughput phenotyping in breeding programs.
Image-based phenotyping has become an innovative approach in plant breeding. This study quantified fiber color intensity in Gossypium hirsutum L. genotypes and evaluated the effect of boll position in the upper, middle, and lower plant canopy thirds. Five cultivars and twelve F2:3 lines were assessed by sampling five plants per plot, collecting three bolls per third, and photographing them with a standard color card. Images were processed using ImageJ and anacard calibration increased mean values without changing genotype groupings. The upper third showed 6.25% lower color intensity, with no significant genotype & times; third interaction. SI efficiently discriminated genotypes, while BI strongly cor= 0.77, 0.80, and 0.88, respectively), supporting its use in breeding programs.
