Enhanced Efficiency of In Vitro Rootstock Micro-propagation Using Silica-Based Nanoparticles and Plant Growth Regulators in Myrobalan 29C (Prunus cerasifera L.)

Different experiments were conducted to establish and optimize an efficient in vitro micropropagation protocol for Myrobalan 29C rootstocks. Disinfection of initial explants with AgNPs (2.5%) reduced the needed amount of NaClO (5.0%) by half. The highest rates of induced active buds were obtained in the DKW (90.63%), MS (86.67%), modified MS (82.22%), and WPM (78.15%) culture media supplemented with BAP (2.22 μmol L^?1)?+?GA₃ (2.88 μmol L^?1)?+?IBA (0.05 μmol L^?1)?+?Fe-EDDHA (228.72 μmol L^?1). The highest quality of the proliferated shoots (5.0) was also achieved using DKW medium. Inclusion of GA₃ (5.76 μmol L^?1), Fe-EDDHA (114.36–228.72 μmol L^?1), or BAP (2.22 μmol L^?1) were also able to enhance the rate of shoot multiplication. Compared to the agar-solidified culture system, the established shoots proliferated more efficiently when immersed by bioreactor in the liquid DKW culture medium on a regular basis. Exogenous application of silica-based nanoparticles (NPs) including the chemically synthesized silica NPs (TSiO₂ NPs, 1.0 ppm), rice husk derived biogenic silica NPs (RSiO₂ NPs, 10.0 ppm), or amine modified silica NPs (ASiO₂ NPs, 10.0 ppm) to the multiplication medium increased the number of regenerated lateral shoots by 520%, 360%, and 349%, respectively. Proliferated shoots with well-developed root system were obtained from the rooting medium supplemented with 19.68 μmol L^?1 IBA. Our results indicated that the rootstocks of Myrobalan 29C could be efficiently propagated under in vitro condition providing proper culture medium and optimal concentrations of additives and plant growth regulators were adopted.

     

On problem solving with Hopfield neural networks

Hopfield and Tank have shown that neural networks can be used to solve certain computationally hard problems, in particular they studied the Traveling Salesman Problem (TSP). Based on network simulation results they conclude that analog VLSI neural nets can be promising in solving these problems. Recently, Wilson and Pawley presented the results of their simulations which contradict the original results and cast doubts on the usefulness of neural nets. In this paper we give the results of our simulations that clarify some of the discrepancies. We also investigate the scaling of TSP solutions found by neural nets as the size of the problem increases. Further, we consider the neural net solution of the Clustering Problem, also a computationally hard problem, and discuss the types of problems that appear to be well suited for a neural net approach.