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IntroductionDrought is the largest abiotic factor impacting agriculture. Plants are challenged by both natural and artificial stressors because they are immobile. To produce drought-resistant plants, we need to know how plants react to drought. A largescale proteome study of leaf and root tissue found drought-responsive proteins. Tomato as a vegetable is grown worldwide. Agricultural biotechnology focuses on creating drought-resistant cultivars. This is important because tomato drought is so widespread. Breeders have worked to improve tomato quality, production, and stress resistance. Conventional breeding approaches have only increased drought tolerance because of drought’s complexity. Many studies have examined how tomatoes handle drought. With genomics, transcriptomics, proteomics, metabolomics, and modern sequencing technologies, it’s easier to find drought-responsive genes.MethodBiotechnology and in silico studies has helped demonstrate the function of drought-sensitive genes and generate drought-resistant plant types. The latest tomato genome editing technology is another. WRKY genes are plant transcription factors. They help plants grow and respond to both natural and artificial stimuli. To make plants that can handle stress, we need to know how WRKY-proteins, which are transcription factors, work with other proteins and ligands in plant cells by molecular docking and modeling study.ResultAbscisic acid, a plant hormone generated in stressed roots, was used here to make plants drought-resistant. Abscisic acid binds WRKY with binding affinity -7.4kcal/mol and inhibitory concentration (Ki) 0.12 microM.DiscussionThis study aims to modulate the transcription factor so plants can handle drought and stress better. Therefore, polyphenols found to make Solanum lycopersicum more drought-tolerant.

Candida spp. is the most common microbial pathogen in fungal infections. There has been a tremendous increase in cases of candidiasis, especially among critically ill non-neutropenic patients. Candida albicans’ isolates were procured from the Prince Sultan Military Hospital, Riyadh, KSA. The isolates were characterized for their identification using CHROMagar, carbohydrate metabolism, germ tube formation, and RAPD-PCR techniques. The essential oil of Thymus vulgaris was obtained by hydro-distillation and characterized to decipher the major bioactive phytoconstituents. The antifungal activity of the thyme essential oil (TEO) was evaluated against fluconazole-resistant C. albicans isolates. The major phytocomponents identified by GC/MS were thymol (68.1%) followed by γ-terpinene (8.9%), cymol (7.7%), caryophyllene (1.1%), linalool (1.4%). The TEO successfully reduced the growth of C. albicans isolates. At very low doses, the TEO proved to be fungi static and fungicidal. TEO also effectively inhibited the germ tube formation and budging of fungal pathogens. The time kill assays have shown that TEO was more effective against drug resistant clinical isolates than fluconazole. This study provides an array of experimental evidence regarding the therapeutic efficacy of TEO against the drug-resistant clinical isolates of C. albicans. The findings may be used in the development of a new antifungal agent accordingly.

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To prevent the spread of illnesses and guarantee the steady and healthy growth of the apple sector, the proper diagnosis of apple leaf diseases is of utmost importance. The subtle interclass variations and enormous intraclass variances among apple leaf disease features, together with the uniformity of disease spots and the complicated background environment, make apple leaf disease diagnosis extremely challenging. A unique dual-branch apple leaf disease diagnosis system (DBNet) was put out to address the aforementioned issues. An attention branch with many dimensions and a multiscale joint branch (MS) make up the dual-branch network topology of the DBNet (DA). In this study, the MS branch and the DA branch are combined to create a DBNet, which successfully improves recognition accuracy while mitigating the negative impacts of complicated backdrop environments and lesion similarities. The accuracy of the DBNet network increases by 0.02843, 0.02412, 0.0144, and 0.0125, respectively, when compared to previous leaf disease detection models. This makes it evident that the suggested DBNet model has certain benefits over others in terms of identifying apple leaf disease.