RESUMEN
A new bidentate Schiff base ligand (C16H16Cl2N4), condensation product of ethylene diamine and 4-chloro N-phenyl formamide, and its metal complexes [M(C16H16Cl2N4)2(OAc)2] (where M = Mn(II) and Zn(II)) were synthesized and characterized using various analytical and spectral techniques, including high-resolution mass spectrometry (HRMS), elemental analysis, ultraviolet-visible (UV-vis), Fourier-transform infrared (FTIR) spectroscopy, AAS, molar conductance, 1H NMR, and powder XRD. All the compounds were non-electrolytes and nanocrystalline. The synthesized compounds were assessed for antioxidant potential by DPPH radical scavenging and FRAP assay, with BHT serving as the positive control. Inhibitory concentration at 50% inhibition (IC50) values were calculated and used for comparative analysis. Furthermore, the prepared compounds were screened for antibacterial activity against two Gram-negative bacteria (Staphylococcus aureus and Bacillus subtilis) and two Gram-positive bacteria (Escherichia coli and Salmonella typhi) using disk-diffusion methods, with amikacin employed as the standard reference. The comparison of inhibition zones revealed that the complexes showed better antibacterial activity than the ligand. To gain insights into the molecular interactions underlying the antibacterial activity, the ligand and complexes were analyzed for their binding affinity with S. aureus tyrosyl-tRNA synthetase (PDB ID: 1JIL) and S. typhi cell membrane protein OmpF complex (PDB ID: 4KR4). These analyses revealed robust interactions, validating the observed antibacterial effects against the tested bacterial strains.
RESUMEN
In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop has been proposed. The images were captured from experimental fields of ICAR-IIMR, Ludhiana, India, targeted to three important diseases viz. Maydis Leaf Blight, Turcicum Leaf Blight and Banded Leaf and Sheath Blight in a non-destructive manner with varied backgrounds using digital cameras and smartphones. In order to solve the problem of class imbalance, artificial images were generated by rotation enhancement and brightness enhancement methods. In this study, three different architectures based on the framework of 'Inception-v3' network were trained with the collected diseased images of maize using baseline training approach. The best-performed model achieved an overall classification accuracy of 95.99% with average recall of 95.96% on the separate test dataset. Furthermore, we compared the performance of the best-performing model with some pre-trained state-of-the-art models and presented the comparative results in this manuscript. The results reported that best-performing model performed quite better than the pre-trained models. This demonstrates the applicability of baseline training approach of the proposed model for better feature extraction and learning. Overall performance analysis suggested that the best-performed model is efficient in recognizing diseases of maize from in-field images even with varied backgrounds.