In ancient times, Withania coagulans Dunal was used as a therapeutic plant for the treatment of several diseases. This report aims to examine the effect of Agrobacterium tumefactions-mediated transformation of W. coagulans with the rolA gene to enhance secondary metabolite production, antioxidant activity, and anticancer activity of transformed tissues. Before transgenic plant production, the authors designed an efficient methodology for in vitro transformation. In this study, leaf explants were cultured on Murashage and Skoog (MS) media containing different amounts of naphthalene acetic acid (NAA) and benzyl adenine (BA). The best performance for inducing embryogenic callus was in MS medium containing 4 µM NAA and 6.0 µM BA, while the best results for shooting (100%) were obtained at 8 µM benzyl adenine. On the other hand, direct shooting was attained by subculturing leaves on MS medium supplemented with 8 µM benzyl adenine. Prolonged shoots showed excellent in vitro rooting results (80%) with 12 µM indole-3-butyric acid (IBA). The samples were precultivated for 3 days and were followed by 48 h infection with A. tumefaciens strain GV3101 having pCV002. Then, a vector expressed the rol A gene of strain Agrobacterium rhizogenes. Furthermore, three independent transgenic shoot lines and one callus line (T2) were produced and exhibited stable integration of transgene rol A genes, as revealed by PCR analysis. Transgenic strains showed a significant increase in antioxidant potential as compared to untransformed plants. Additionally, LC-MS analysis showed that the transformed strains have a higher withanolide content as compared to untransformed ones. Moreover, the reduced proliferation of prostate cancer cells was observed after treatment with extracts of transgenic plants. Furthermore, these transformed plants exhibited superior antioxidant capability and higher withanolide content than untransformed ones. In conclusion, the reported data can be used to select withanolide-rich germplasm from transformed cell cultures.
This study investigates the application of the Gaussian Radial Basis Function Neural Network (GRNN), Gaussian Process Regression (GPR), and Multilayer Perceptron Optimized by Particle Swarm Optimization (MLP-PSO) models in analyzing the relationship between rainfall and runoff and in predicting runoff discharge. These models utilize autoregressive input vectors based on daily-observed TRMM rainfall and TMR inflow data. The performance evaluation of each model is conducted using statistical measures to compare their effectiveness in capturing the complex relationships between input and output variables. The results consistently demonstrate that the MLP-PSO model outperforms the GRNN and GPR models, achieving the lowest root mean square error (RMSE) across multiple input combinations. Furthermore, the study explores the application of the Empirical Mode Decomposition-Hilbert-Huang Transform (EMD-HHT) in conjunction with the GPR and MLP-PSO models. This combination yields promising results in streamflow prediction, with the MLP-PSO-EMD model exhibiting superior accuracy compared to the GPR-EMD model. The incorporation of different components into the MLP-PSO-EMD model significantly improves its accuracy. Among the presented scenarios, Model M4, which incorporates the simplest components, emerges as the most favorable choice due to its lowest RMSE values. Comparisons with other models reported in the literature further underscore the effectiveness of the MLP-PSO-EMD model in streamflow prediction. This study offers valuable insights into the selection and performance of different models for rainfall-runoff analysis and prediction.
