Field-Scale Precision: Predicting Grain Yield of Diverse Wheat Breeding Lines Using High-Throughput UAV Multispectral Imaging

This study explored how to use UAV-based multispectral imaging, a plot detection model, and machine learning (ML) algorithms to predict wheat grain yield at the field scale. Multispectral data were collected over several weeks using the MicaSense RedEdge-P camera. Ground truth data on vegetation indices were collected utilizing portable phenotyping instruments, and agronomic data were collected manually. The YOLOv8 detection model was utilized for field-scale wheat plot detection. Four ML algorithms—decision tree (DT), random forest (RF), gradient boosting (GB), and extreme GB (XGBoost were used to evaluate wheat grain yield prediction using normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green NDVI (G-NDVI) data. The results demonstrated the RF algorithm's predicting ability across all growth stages, with a root-mean-square error (RMSE) of 43 grams per plot (g/p) and a coefficient of determination ($R^{2}$) value of 0.90 for NDVI data. For NDRE data, DT outperformed other models, with an RMSE of 43 g/p and an $R^{2}$ of 0.88. GB exhibited the highest predictive accuracy for G-NDVI data, with an RMSE of 42 g/p and an $R^{2}$ value of 0.89. The study integrated isogenic bread wheat sister lines and checked cultivars differing in grain yield, grain protein, and other agronomic traits to facilitate the identification of high-yield performers. The results show the potential use of UAV-based multispectral imaging combined with a detection model and ML in various precision agriculture applications, including wheat breeding, agronomy research, and broader agricultural practices

Association of Stromal Cell-Derived Factor-1-3 ' A Polymorphism to Higher Mobilization of Hematopoietic Stem Cells CD34+in Tunisian Population

We explored the influence of polymorphisms in genes encoding the chemokine stromal cellderived factor-1 (SDF-1)/CXCL12 in a cohort of Tunisian patients with malignant hematologic diseases multiple myeloma [MM], non-Hodgkin's lymphoma [NHL], Hodgkin's disease, and acute myeloid leukemia [AML], who underwent stem cell mobilization for autologous transplantation versus a group of healthy donors for allogeneic transplantation. Polymerase chain reactionrestriction fragment length polymorphism (PCR-RFLp) analysis was used for rapid identification of genotypes. Significant associations for SDF1-3\ue2\u80\ub2A polymorphism were observed exclusively in patients with MM and NHL. While there was a lack of all association of SDF-1 polymorphism with AML patients. However, considering that the ability of mobilization varies among subjects, we have observed that the SDF1-3\ue2\u80\ub2A allele was associated with good mobilization capacity. Interestingly, the association was mainly observed among healthy allogeneic transplant donors where the analysis was not biased by background disease or chemotherapy (P = .010; odds ratio = 2.603; confidence interval [95%] = 1.2395.466)