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Watermelon (Citrullus lanatus) is the third largest fruit crop in the world in term of production. However, it is susceptible to several viruses. Watermelon vine decline (WVD), caused by whitefly-transmitted squash vein yellowing virus (SqVYV), is a disease that has caused over $60 million in losses in the US and continues to occur regularly in southeastern states. Understanding the molecular mechanisms underlying resistance to SqVYV is important for effective disease management. A time-course transcriptomic analysis was conducted on resistant (392291-VDR) and susceptible (Crimson Sweet) watermelon genotypes inoculated with SqVYV. Significantly higher levels of SqVYV were observed over time in the susceptible compared to the resistant genotype. The plasmodesmata callose binding protein (PDCB) gene, which is responsible for increased callose deposition in the plasmodesmata, was more highly expressed in the resistant genotype than in the susceptible genotype before and after inoculation, suggesting the inhibition of cell-to-cell movement of SqVYV. The potential role of the RNA interference (RNAi) pathway was observed in the resistant genotype based on differential expression of eukaryotic initiation factor (eIF), translin, DICER, ribosome inactivating proteins, RNA-dependent RNA polymerase (RDR), and Argonaute (AGO) genes after inoculation. The significant differential expression of hormone-related genes, including those involved in the ethylene, jasmonic acid, auxin, cytokinin, gibberellin, and salicylic acid signaling pathways, was observed, emphasizing their regulatory roles in the defense response. Genes regulating pectin metabolism, cellulose synthesis, cell growth and development, xenobiotic metabolism, and lignin biosynthesis were overexpressed in the susceptible genotype, suggesting that alterations in cell wall integrity and growth processes result in disease symptom development. These findings will be helpful for further functional studies and the development of SqVYV-resistant watermelon cultivars.

During the last decade, the sorghum aphid (Melanaphis sorghi), previously identified as sugarcane aphid (Melanaphis sacchari), became a serious pest of sorghum, spreading to all sorghum‐producing regions in the United States, Mexico, and South America, where crop losses of 50%–100% have been reported. Developing sorghum cultivars with resistance to this insect is the most sustainable strategy for long‐term pest management. To design cultivars with aphid resistance, comprehensively understanding the mechanisms underlying aphid survival, host plant resistance, and aphid–sorghum interactions is critical. In this review, we summarize the comprehensive efforts to characterize the aphid populations as well as their interaction with sorghum plants via hormonal pathways that trigger various genes including leucine rich repeats, WRKY transcription factors, lipoxygenases, calmodulins, and others. We discuss efforts made during the last decade to identify specific genomic regions and candidate genes that confer aphid resistance, as well as describe recent successes and potential challenges in breeding for aphid resistance. Furthermore, we discuss the use of disruptive technologies like high‐throughput phenotyping, artificial intelligence, or machine learning for developing aphid resistant sorghum cultivars. Integration of these new technologies has the potential to accelerate the development and design of novel traits that confer durable aphid resistance in new sorghum cultivars to defend sorghum against new aphid genotype development. Core Ideas: Understanding aphid–sorghum interactions, mechanisms underlying aphid survival, and plant tolerance is crucial for sorghum breeding.Efforts are made during the last decade to address sorghum–aphid problems to identify key genes for enhancing resistance to sorghum aphids.High‐throughput technologies coupled with genome‐based breeding, genome editing, and artificial intelligence enable accelerated development of aphid‐resistant varieties. [ABSTRACT FROM AUTHOR]

Watermelon (Citrullus lanatus) is the third largest fruit crop in the world in term of production. However, it is susceptible to several viruses. Watermelon vine decline (WVD), caused by whitefly-transmitted squash vein yellowing virus (SqVYV), is a disease that has caused over $60 million in losses in the US and continues to occur regularly in southeastern states. Understanding the molecular mechanisms underlying resistance to SqVYV is important for effective disease management. A time-course transcriptomic analysis was conducted on resistant (392291-VDR) and susceptible (Crimson Sweet) watermelon genotypes inoculated with SqVYV. Significantly higher levels of SqVYV were observed over time in the susceptible compared to the resistant genotype. The plasmodesmata callose binding protein (PDCB) gene, which is responsible for increased callose deposition in the plasmodesmata, was more highly expressed in the resistant genotype than in the susceptible genotype before and after inoculation, suggesting the inhibition of cell-to-cell movement of SqVYV. The potential role of the RNA interference (RNAi) pathway was observed in the resistant genotype based on differential expression of eukaryotic initiation factor (eIF), translin, DICER, ribosome inactivating proteins, RNA-dependent RNA polymerase (RDR), and Argonaute (AGO) genes after inoculation. The significant differential expression of hormone-related genes, including those involved in the ethylene, jasmonic acid, auxin, cytokinin, gibberellin, and salicylic acid signaling pathways, was observed, emphasizing their regulatory roles in the defense response. Genes regulating pectin metabolism, cellulose synthesis, cell growth and development, xenobiotic metabolism, and lignin biosynthesis were overexpressed in the susceptible genotype, suggesting that alterations in cell wall integrity and growth processes result in disease symptom development. These findings will be helpful for further functional studies and the development of SqVYV-resistant watermelon cultivars. [ABSTRACT FROM AUTHOR]

Schiff bases have advanced physical, chemical, and medicinal properties, making them strong biological agents. Comparable FTIR, UV-VIS, 1HNMR, 13CNMR spectra confirmed the successful formation of N-N-(1,2-Phenylene)bis(1-(4-bromophenyl)methanimine) (CS3) compound. Geometry was optimized using DFT with B3LYP/cc-pVDZ basis set. Obtained energy gap of the compound was 2.07 eV. Carbon and oxygen atoms were electrophilic, according to molecular electrostatic potential analysis. Mulliken charge analysis found that C atoms have + ve and -ve charges, H atoms have only positive charges, while Br, O, and N atoms have only -ve charges. The ELF analyzed the molecule and found that the atoms of C, N, and Br all had delocalized electron density. The wave function LOL identified localized orbital positions in the hydrogen atoms. RDG maps provided various non-covalent interactions. Synthesized compound was docked using PDB: 2ZM5 protein, with the lowest binding attractions obtained, are −5.44 (7BYE) kcal/mol. [ABSTRACT FROM AUTHOR]

The compound was synthesized and characterized with Infrared, UV, and NMR studies. The present work is theoretically investigated using B3LYP/cc-pVDZ basis set. The LED has been performed using PBE0-D3/deft-TZVP basis set and DLPNO-CCSD(T). The DFT was used to define the structure and geometry of the compound. To identify the primary binding sites and weak interactions, the Multiwfn-3.8 was subjected to a topological analyses, including ELF, LOL, ALIE, and RDG. The UV-Visible spectrum was theoretically analyzed using the TD-SCF method. The B3LYP/cc-pVDZ was used to measure the HOMO-LUMO, MEP, NBO, and NLO. The NBO calculations investigate the intermolecular and intramolecular movement of charges, as well as the molecule stability. To determine whether the CS1 molecule has the potential to become a drug, a pharmacological investigation is performed using a Swiss-ADME. The docking study done against the 1NXU protein is performed using the Auto-dock. [ABSTRACT FROM AUTHOR]

Artificial intelligence, machine learning and deep learning algorithms have been widely used for Maximum Power Point Tracking (MPPT) in solar systems. In the traditional MPPT strategies, following of worldwide Global Maximum Power Point (GMPP) under incomplete concealing conditions stay overwhelming assignment and tracks different nearby greatest power focuses under halfway concealing conditions. The advent of artificial intelligence in MPPT has guaranteed of accurate following of GMPP while expanding the significant performance and efficiency of MPPT under Partial Shading Conditions (PSC). Still the selection of an efficient learning based MPPT is complex because each model has its advantages and drawbacks. Recently, Meta-heuristic algorithm based Learning techniques have provided better tracking efficiency but still exhibit dull performances under PSC. This work represents an excellent optimization based on Spotted Hyena Enabled Reliable BAT (SHERB) learning models, SHERB-MPPT integrated with powerful extreme learning machines to identify the GMPP with fast convergence, low steady-state oscillations, and good tracking efficiency. Extensive testing using MATLAB-SIMULINK, with 50000 data combinations gathered under partial shade and normal settings. As a result of simulations, the proposed approach offers 99.7% tracking efficiency with a slower convergence speed. To demonstrate the predominance of the proposed system, we have compared the performance of the system with other hybrid MPPT learning models. Results proved that the proposed cross breed MPPT model had beaten different techniques in recognizing GMPP viably under fractional concealing conditions. [ABSTRACT FROM AUTHOR]