Size variations of mesoporous silica nanoparticle control uptake efficiency and delivery of AC2-derived dsRNA for protection against tomato leaf curl New Delhi virus.

KEY MESSAGE: We report the size dependent uptake of dsRNA loaded MSNPs into the leaves and roots of Nicotiana benthamiana plants and accessed for their relative reduction in Tomato leaf curl New Delhi viral load. A non-GMO method of RNA interference (RNAi) has been recently in practice through direct delivery of double stranded RNA into the plant cells. Tomato leaf curl New Delhi virus (ToLCNDV), a bipartitie begomovirus, is a significant viral pathogen of many crops in the Indian subcontinent. Conventional RNAi cargo delivery strategies for instance uses viral vectors and Agrobacterium-facilitated delivery, exhibiting specific host responses from the plant system. In the present study, we synthesized three different sizes of amine-functionalized mesoporous silica nanoparticles (amino-MSNPs) to mediate the delivery of dsRNA derived from the AC2 (dsAC2) gene of ToLCNDV and showed that these dsRNA loaded nanoparticles enabled effective reduction in viral load. Furthermore, we demonstrate that amino-MSNPs protected the dsRNA molecules from nuclease degradation, while the complex was efficiently taken up by the leaves and roots of Nicotiana benthamiana. The real time gene expression evaluation showed that plants treated with nanoparticles of different sizes ~ 10 nm (MSNPDEA), ~ 32 nm (MSNPTEA) and ~ 66 nm (MSNPNH3) showed five-, eleven- and threefold reduction of ToLCNDV in N. benthamiana, respectively compared to the plants treated with naked dsRNA. This work clearly demonstrates the size dependent internalization of amino-MSNPs and relative efficacy in transporting dsRNA into the plant system, which will be useful in convenient topical treatment to protect plants against their pathogens including viruses. Mesoporous silica nanoparticles loaded with FITC, checked for its uptake into Nicotiana benthamiana.

The Use of Computational Geometry Techniques to Resolve the Issues of Coverage and Connectivity in Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) enhance the ability to sense and control the physical environment in various applications. The functionality of WSNs depends on various aspects like the localization of nodes, the strategies of node deployment, and a lifetime of nodes and routing techniques, etc. Coverage is an essential part of WSNs wherein the targeted area is covered by at least one node. Computational Geometry (CG) -based techniques significantly improve the coverage and connectivity of WSNs. This paper is a step towards employing some of the popular techniques in WSNs in a productive manner. Furthermore, this paper attempts to survey the existing research conducted using Computational Geometry-based methods in WSNs. In order to address coverage and connectivity issues in WSNs, the use of the Voronoi Diagram, Delaunay Triangulation, Voronoi Tessellation, and the Convex Hull have played a prominent role. Finally, the paper concludes by discussing various research challenges and proposed solutions using Computational Geometry-based techniques.