Predicting and optimizing reactive oxygen species metabolism in Punica granatum L. through machine learning: role of exogenous GABA on antioxidant enzyme activity under drought and salinity stress.

BACKGROUND: Drought and salinity stress have been proposed as the main environmental factors threatening food security, as they adversely affect crops' agricultural productivity. As a potential solution, the application of plant growth regulators to enhance drought and salinity tolerance has gained considerable attention. γ-aminobutyric acid (GABA) is a four-carbon non-protein amino acid that accumulates in plants as a response to stressful conditions. This study focused on a comparative assessment of several machine learning (ML) regression models, including radial basis function, generalized regression neural network (GRNN), random forest (RF), and support vector regression (SVR) to develop predictive models for assessing the effect of different concentrations of GABA (0, 10, 20, and 40 mM) on various physio-biochemical traits during periods of drought, salinity, and combined stress conditions. The physio-biochemical traits included antioxidant enzyme activities (superoxide dismutase, SOD; peroxidase, POD; catalase, CAT; and ascorbate peroxidase, APX), protein content, malondialdehyde (MDA) levels, and hydrogen peroxide (H2O2) levels. The non­dominated sorting genetic algorithm­II (NSGA­II) was employed for optimizing the superior prediction model. RESULTS: The GRNN model outperformed the other ML algorithms and was therefore selected for optimization by NSGA-II. The GRNN-NSGA-II model revealed that treatment with GABA at concentrations of 20.90 mM and 20.54 mM, under combined drought and salinity stress conditions at 20.86 and 20.72 days post-treatment, respectively, could result in the maximum values for protein content (by 0.80 and 0.69), APX activity (by 50.63 and 51.51), SOD activity (by 0.54 and 0.53), POD activity (by 1.53 and 1.72), CAT activity (by 4.42 and 5.66), as well as lower MDA levels (by 0.12 and 0.15) and H2O2 levels (by 0.44 and 0.55), respectively, in the 'Atabaki' and 'Rabab' cultivars. CONCLUSIONS: This study demonstrates that the GRNN-NSGA-II model, as an advanced ML algorithm with a strong predictive ability for outcomes in combined stressful environmental conditions, provides valuable insights into the significant factors influencing such multifactorial processes.

Vitamin B12 biosynthesis over waste frying sunflower oil as a cost effective and renewable substrate.

Statistical experimental designs were used to develop a medium based on waste frying sunflower oil (WFO) and other nutrient sources for production of vitamin B12 (VB12) by Propionibacterium freudenreichii subsp. freudenreichii PTCC 1674. The production of acetic acid and propionic acid were also evaluated using the same microorganism. The amount of WFO in the media was initially optimized. The amount of 4 % w/v of oil found to be an appropriate amount for production of VB12. A Plackett Burman design was then employed to identify nutrients that have significant effect on the production of VB12 in the WFO media. Dimethylbenzimidazolyl (DMB), cobalt chloride, ferrous sulfate, and calcium chloride were the most important compounds. The level optimization of nutrients as the significant factors was finally performed using response surface methodology based on a central composite design. The model predicted that a medium containing 35.56 mg/L DMB, 14.69 mg/L CoCl2.6H2O, 5.82 mg/L FeSO4.7H2O, and 11.41 mg/L CaCl2.2H2O gives the maximum VB12 production of 2.60 mg/L. The optimized medium provides a final concentration of vitamin 170 % higher than that by the original medium. This study offers valuable insights on a cost-effective carbon source for industrial production of food-grade VB12.