Search

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Kumar, Chanda, Adkins and Kousik.)

In the current era, satisfying the appetite of data hungry models is becoming an increasingly challenging task. This challenge is particularly magnified in research areas characterised by sensitivity, where the quest for genuine data proves to be elusive. The study of violence serves as a poignant example, entailing ethical considerations and compounded by the scarcity of authentic, real-world data that is predominantly accessible only to law enforcement agencies. Existing datasets in this field often resort to using content from movies or open-source video platforms like YouTube, further emphasising the scarcity of authentic data. To address this, our dataset aims to pioneer a new approach by creating the first synthetic virtual dataset for violence detection, named the Weapon Violence Dataset (WVD). The dataset is generated by creating virtual violence scenarios inside the photo-realistic video game namely: Grand Theft Auto-V (GTA-V). This dataset includes carefully selected video clips of person-to-person fights captured from a frontal view, featuring various weapons-both hot and cold across different times of the day. Specifically, WVD contains three categories: Hot violence and Cold violence (representing the violence category) as well as No violence (constituting the control class). The dataset is designed and created in a way that will enable the research community to train deep models on such synthetic data with the ability to increase the data corpus if the needs arise. The dataset is publicly available on Kaggle and comprises normal RGB and optic flow videos., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)