Genetic engineering of sunflower (<Emphasis Type="Italic">Helianthus annuus</Emphasis> L.) for resistance to necrosis disease through deployment of the TSV coat protein gene

Nitrogen is a major driver of plant growth and the nitrogen source can be critical to good growth in vitro. A response surface methodology mixture-component design and a data mining algorithm were applied to nitrogen (N) nutrition for improving the micropropagation of Prunus armeniaca Lam. Data taken on shoot cultures included a subjective quality rating, shoot number, shoot length, leaf characteristics and physiological disorders. Data were analyzed using the Classification and Regression Tree data mining algorithm. The best overall shoot quality as well as leaf color were on medium with NO₃^??>?25 mM and NH₄⁺/Ca⁺ >?0.8. Improving shoot length to15 mm required 25?<?NO₃^? ≤?35 mM with NH₄⁺/Ca²⁺ ≤?2.33. The most shoots (11.6) were produced with NO₃^? >?25 mM and NH₄⁺/Ca²⁺ ≤ 0.8, but there were 5–10 shoots at other NO₃^? concentrations regardless of NH₄⁺/Ca²⁺ proportion. Leaves increased in size with higher NO₃^? concentrations (>?55 mM). Physiological disorders were also influenced by the nitrogen components. Shoot tip necrosis was rarely present with NO₃^? > 45 mM. Callus production decreased somewhat with NH₄⁺/Ca²⁺ >?2.33. Suggested concentrations for an improved medium considering all of these growth characteristics would be 25?<?NO₃^? ≤?35 mM and NH₄⁺/Ca⁺ ≤ 0.8. Validation experiments comparing WPM and three trial media showed improvements in several shoot growth parameters on medium with optimized mesos and optimized nitrogen components.