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In vitro sterilization is a primary step of plant tissue culture which the ultimate results of in vitro culture are directly depended on the efficiency of the sterilization. Artificial intelligence models in a combination of optimization algorithms could be beneficial computational approaches for modeling and optimizing in vitro culture. The aim of this study was modeling and optimizing in vitro sterilization of chrysanthemum, as a case study, through Multilayer Perceptron- Non-dominated Sorting Genetic Algorithm-II (MLP-NSGAII). MLP was used for modeling two outputs including contamination frequency (CF), and explant viability (EV) based on seven variables including HgCl2, Ca(ClO)2, Nano-silver, H2O2, NaOCl, AgNO3, and immersion times. Subsequently, models were linked to NSGAII for optimizing the process, and the importance of each input was evaluated by sensitivity analysis. Results showed all of the R2 of training and testing data were over 94%. According to MLP-NSGAII, optimal CF (0%), and EV (99.98%) can be obtained from 1.62% NaOCl at 13.96 min immersion time. The results of sensitivity analysis showed that CF and EV were more sensitive to immersion time and less sensitive to AgNO3. Subsequently, the performance of predicted and optimized sterilants × immersion times combination were tested, and results indicated that the differences between the MLP predicted and validation data were negligible. Generally, MLP-NSGAII as a powerful methodology may pave the way for establishing new computational strategies in plant tissue culture.

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A hybrid artificial intelligence model and optimization algorithm could be a powerful approach for modeling and optimizing plant tissue culture procedures. The aim of this study was introducing an Adaptive Neuro-Fuzzy Inference System- Non-dominated Sorting Genetic Algorithm-II (ANFIS-NSGAII) as a powerful computational methodology for somatic embryogenesis of chrysanthemum, as a case study. ANFIS was used for modeling three outputs including callogenesis frequency (CF), embryogenesis frequency (EF), and the number of somatic embryo (NSE) based on different variables including 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), sucrose, glucose, fructose, and light quality. Subsequently, models were linked to NSGAII for optimizing the process, and the importance of each input was evaluated by sensitivity analysis. Results showed that all of the R 2 of training and testing sets were over 92%, indicating the efficiency and accuracy of ANFIS on the modeling of the embryogenesis. Also, according to ANFIS-NSGAII, optimal EF (99.1%), and NSE (13.1) can be obtained from a medium containing 1.53 mg/L 2,4-D, 1.67 mg/L BAP, 13.74 g/L sucrose, 57.20 g/L glucose, and 0.39 g/L fructose under red light. The results of the sensitivity analysis showed that embryogenesis was more sensitive to 2,4-D, and less sensitive to fructose. Generally, the hybrid ANFIS-NSGAII can be recognized as a powerful computational tool for modeling and optimizing in plant tissue culture.

Cotton (Gossypium hirsutum L., var. Coker 312) hypocotyl explants were transformed with three strains of Agrobacterium tumefaciens, LBA4404, EHA101 and C58, each harboring the recombinant binary vector pBI121 containing the chi gene insert and neomycin phosphotransferase (nptII) gene, as selectable marker. Inoculated tissue sections were placed onto cotton co-cultivation medium. Transformed calli were selected on MS medium containing 50 mg l )1 kanamycin and 200 mg l )1 cepotaxime. Putative calli were subsequently regenerated into cotton plantlets expressing both the kanamycin resistance gene and b-glucuronidase (gus) as a reporter gene. Polymerase chain reaction was used to confirm the integration of chi and nptII transgenes in the T 1 plants genome. Integration of chi gene into the genome of putative transgenic was further confirmed by Southern blot analysis. 'Western' immunoblot analysis of leaves isolated from T 0 transformants and progeny plants (T 1 ) revealed the presence of an immunoreactive band with MW of approximately 31 kDa in transgenic cotton lines using anti-chitinase-I polyclonal anti-serum. Untransformed control and one transgenic line did not show such an immunoreactive band. Chitinase specific activity in leaf tissues of transgenic lines was several folds greater than that of untransformed cotton. Crude leaf extracts from transgenic lines showed in vitro inhibitory activity against Verticillium dahliae. Transgenic plants currently growing in a greenhouse and will be bioassayed for improved resistance against V. dahlia the causal against of verticilliosis in cotton.

The aim of the current study was modeling and optimizing medium compositions for shoot proliferation of chrysanthemum, as a case study, through radial basis function- non-dominated sorting genetic algorithm-II (RBF-NSGAII). RBF as one of the artificial neural networks (ANNs) was used for modeling four outputs including proliferation rate (PR), shoot number (SN), shoot length (SL), and basal callus weight (BCW) based on four variables including 6-benzylaminopurine (BAP), indole-3-butyric acid (IBA), phloroglucinol (PG), and sucrose. Afterward, models were linked to the optimization algorithm. Also, sensitivity analysis was applied for evaluating the importance of each input. The R2 correlation values of 0.88, 0.91, 0.97, and 0.76 between observed and predicted data were obtained for PR, SN, SL, and BCW, respectively. According to RBF-NSGAII, optimal PR (98.85%), SN (13.32), SL (4.83 cm), and BCW (0.08 g) can be obtained from a medium containing 2.16 µM BAP, 0.14 µM IBA, 0.29 mM PG, and 87.63 mM sucrose. The results of sensitivity analysis indicated that PR, SN, and SL were more sensitive to BAP, followed by sucrose, PG, and IBA. Finally, the performance of predicted and optimized medium compositions were tested, and results showed that the difference between the validation data and RBF-NSGAII predicted and optimized data were negligible. Generally, RBF-NSGAII can be considered as an efficient computational strategy for modeling and optimizing in vitro organogenesis.

Background: Optimizing the somatic embryogenesis protocol can be considered as the first and foremost step in successful gene transformation studies. However, it is usually difficult to achieve an optimized embryogenesis protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach, such as machine learning algorithms for this aim. In the present study, two machine learning algorithms, including Multilayer Perceptron (MLP) as an artificial neural network (ANN) and support vector regression (SVR), were employed to model somatic embryogenesis of chrysanthemum, as a case study, and compare their prediction accuracy. Results: The results showed that SVR (R 2 > 0.92) had better performance accuracy than MLP (R 2 > 0.82). Moreover, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was also applied for the optimization of the somatic embryogenesis and the results showed that the highest embryogenesis rate (99.09%) and the maximum number of somatic embryos per explant (56.24) can be obtained from a medium containing 9.10 μM 2,4-dichlorophenoxyacetic acid (2,4-D), 4.70 μM kinetin (KIN), and 18.73 μM sodium nitroprusside (SNP). According to our results, SVR-NSGA-II was able to optimize the chrysanthemum's somatic embryogenesis accurately. Conclusions: SVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies.

Salinity imposes significant stresses in various living organisms including microalgae. High extracellular concentration of Na+ directly influences ionic balance inside the cell and subsequently the cellular activities. In the present study, the effect of such stress on growth and intracellular ions concentration (IIC) of Dunaliella salina and Chlorella Spp. was investigated. IIC was analyzed using Ion chromatography technique. D. salina showed the highest degree of resistance to increase in salinity as little changes occurred both in IIC and in growth parameters. D. salina could maintain the balance of K+ inside the cell and eject the excess Na+ even at NaCl concentrations above 1M. Moreover, D. salina accumulated β-carotene in order to protect its photosynthetic apparatus. Among Chlorella species, C. vulgaris showed signs of adaptation to high content of salinity, though it is a fresh water species by nature. Moreover, the response shown by C. vulgaris to rise in salinity was even stronger than that of C. salina, which is presumably a salt-water resistant species. In fact, C. vulgaris could maintain intracellular K+ better than C. salina in response to increasing salinity, and as a result, it could survive at NaCl concentrations as high as 0.75 M. Marine strains such as D. salina well cope with the fluctuations in salinity through the existing adaptation mechanisms i.e. maintaining the K+/N+ balance inside the cell, K+ accumulation and Na+ ejection, accumulation of photosynthetic pigments like β-carotene.