Drought stress as one of the most devastating abiotic stresses affects agricultural and horticultural productivity in many parts of the world. The application of melatonin can be considered as a ...promising approach for alleviating the negative impact of drought stress. Modeling of morphological responses to drought stress can be helpful to predict the optimal condition for improving plant productivity. The objective of the current study is modeling and predicting morphological responses (leaf length, number of leaves/plants, crown diameter, plant height, and internode length) of citrus to drought stress, based on four input variables including melatonin concentrations, days after applying treatments, citrus species, and level of drought stress, using different Artificial Neural Networks (ANNs) including Generalized Regression Neural Network (GRNN), Radial basis function (RBF), and Multilayer Perceptron (MLP). The results indicated a higher accuracy of GRNN as compared to RBF and MLP. The great accordance between the experimental and predicted data of morphological responses for both training and testing processes support the excellent efficiency of developed GRNN models. Also, GRNN was connected to Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize input variables for obtaining the best morphological responses. Generally, the validation experiment showed that ANN-NSGA-II can be considered as a promising and reliable computational tool for studying and predicting plant morphological and physiological responses to drought stress.
Callogenesis is one of the most powerful biotechnological approaches for in vitro secondary metabolite production and indirect organogenesis in Passiflora caerulea. Comprehensive knowledge of ...callogenesis and optimized protocol can be obtained by the application of a combination of machine learning (ML) and optimization algorithms. In the present investigation, the callogenesis responses (i.e., callogenesis rate and callus fresh weight) of P. caerulea were predicted based on different types and concentrations of plant growth regulators (PGRs) (i.e., 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), 1-naphthaleneacetic acid (NAA), and indole-3-Butyric Acid (IBA)) as well as explant types (i.e., leaf, node, and internode) using multilayer perceptron (MLP). Moreover, the developed models were integrated into the genetic algorithm (GA) to optimize the concentration of PGRs and explant types for maximizing callogenesis responses. Furthermore, sensitivity analysis was conducted to assess the importance of each input variable on the callogenesis responses. The results showed that MLP had high predictive accuracy (R2 > 0.81) in both training and testing sets for modeling all studied parameters. Based on the results of the optimization process, the highest callogenesis rate (100%) would be obtained from the leaf explant cultured in the medium supplemented with 0.52 mg/L IBA plus 0.43 mg/L NAA plus 1.4 mg/L 2,4-D plus 0.2 mg/L BAP. The results of the sensitivity analysis showed the explant-dependent impact of the exogenous application of PGRs on callogenesis. Generally, the results showed that a combination of MLP and GA can display a forward-thinking aid to optimize and predict in vitro culture systems and consequentially cope with several challenges faced currently in Passiflora tissue culture.
Optimization of indirect shoot regeneration protocols is one of the key prerequisites for the development of Agrobacterium-mediated genetic transformation and/or genome editing in Passiflora ...caerulea. Comprehensive knowledge of indirect shoot regeneration and optimized protocol can be obtained by the application of a combination of machine learning (ML) and optimization algorithms.
In the present investigation, the indirect shoot regeneration responses (i.e., de novo shoot regeneration rate, the number of de novo shoots, and length of de novo shoots) of P. caerulea were predicted based on different types and concentrations of PGRs (i.e., TDZ, BAP, PUT, KIN, and IBA) as well as callus types (i.e., callus derived from different explants including leaf, node, and internode) using generalized regression neural network (GRNN) and random forest (RF). Moreover, the developed models were integrated into the genetic algorithm (GA) to optimize the concentration of PGRs and callus types for maximizing indirect shoot regeneration responses. Moreover, sensitivity analysis was conducted to assess the importance of each input variable on the studied parameters.
The results showed that both algorithms (RF and GRNN) had high predictive accuracy (R
> 0.86) in both training and testing sets for modeling all studied parameters. Based on the results of optimization process, the highest de novo shoot regeneration rate (100%) would be obtained from callus derived from nodal segments cultured in the medium supplemented with 0.77 mg/L BAP plus 2.41 mg/L PUT plus 0.06 mg/L IBA. The results of the sensitivity analysis showed the explant-dependent impact of exogenous application of PGRs on indirect de novo shoot regeneration.
A combination of ML (GRNN and RF) and GA can display a forward-thinking aid to optimize and predict in vitro culture systems and consequentially cope with several challenges faced currently in Passiflora tissue culture.
In vitro rooting as one of the most critical steps of micropropagation is affected by various extrinsic (e.g., medium composition, auxins) and intrinsic factors (e.g., species, explant). In ...Passiflora species, in vitro adventitious rooting is a difficult, complex, and non-linear process. Since in vitro rooting is a multivariable complex biological process, efficient and reliable computational approaches such as machine learning (ML) are required to model, predict, and optimize this non-linear biological process. Therefore, in the current study, a hybrid of generalized regression neural network (GRNN) and genetic algorithm (GA) was employed to predict in vitro rooting responses (rooting percentage, number of roots, and root length) of Passiflora caerulea based on the optimization of the level of auxins (indole-3-acetic acid (IAA), indolebutyric acid (IBA), and 1-naphthaleneacetic acid (NAA)) and the type of explant (microshoots derived from leaf, node, and internode). Based on the results, the GRNN model was accurate in predicting all in vitro rooting responses of P. caerulea (R2 > 0.92) in either training or testing sets. The result of the validation experiment also showed that there was a negligible difference between the predicted-optimized values and the validated results demonstrating the reliability of the developed GRNN-GA model. Generally, the results of the current study showed that GRNN-GA is a reliable and accurate model to predict and optimize in vitro rooting of P. caerulea.
Lime is an important commercial product in tropical and subtropical regions, where drought stress is becoming one of the most severe environmental challenges in the agricultural sector. Melatonin is ...an antioxidant molecule that helps plants regulate their development and respond to a variety of stresses. In this research, the effects of exogenous melatonin treatments were evaluated at different concentrations (0, 50, 100, and 150 μM) on biochemical aspects and gene expression in two species of lime plants (“Mexican lime” and “Persian lime”) under normal (100% field capacity (FC)) and drought stress conditions (75% and 40% FC). The experiments were factorial and based on a completely randomized design (CRD) with four replicates. Drought stress caused electrolyte leakage (EL) as well as accumulations of hydrogen peroxide (H2O2) and malondialdehyde (MDA), indicating the occurrence of damage to cellular membranes. In contrast, the melatonin pretreatment at various concentrations reduced the levels of EL, H2O2 and MDA while mitigating the negative effects of drought stress on the two lime species. The application of melatonin (100-μM) significantly increased the level of proline content and activity of antioxidant enzymes in plants under drought stress compared to control plants. According to real-time PCR analysis, drought stress and melatonin treatment enhanced the expression of genes involved in ROS scavenging, proline biosynthesis, and cell redox regulation in both species, as compared to their respective controls. According to these findings, melatonin is able to detoxify ROS and regulate antioxidant systems, thereby protecting lime plants from drought stress-induced damages.
As a novel antioxidant, sodium nitroprusside (SNP) can be used to reduce the adverse effects of various abiotic stresses, especially drought stress. Drought stress is a major problem in the ...vegetative and reproductive stages of “Mexican lime” (
Citrus aurantifolia
(Christ.) Swingle), which is known as a main horticultural plant. The aim of the current investigation was to study the impact of SNP on biochemical, morphological, and physiological characteristics of “Mexican lime” under drought stress in vitro condition. This study was performed as a factorial experiment based on a completely randomized design with four replications. Drought stress was induced by using polyethylene glycol (PEG 6000) in four levels (0, 1, 2, and 3%) in Murashige and Skoog (MS) medium. To evaluate the effect of SNP in ameliorating drought stress, various concentrations (0, 25, 50, and 100 μM) of SNP were supplemented to MS medium. The results showed that drought stress led to a reduction in shoot number, shoot length, leaf number, and fresh and dry weight. The application of 25 μM SNP resulted in a favorable impact on these parameters. Also, the highest level of total proline content, electrolyte leakage, and antioxidant enzyme activities were observed under severe drought stress induced by 3% PEG (− 0.3 MPa water potentials).
A feeding trial was carried out to evaluate the effects of inclusion of 3 (GL3), 6 (GL6), 9 (GL9), and 12 (GL12) % red seaweed,
Gracilaria pygmaea
, in rainbow trout,
Oncorhynchus mykiss
, feeds. A ...feed without seaweed was used as the control. All feeds were formulated to be iso-nitrogenous (48% protein), iso-lipidic (16%), and iso-energetic (20 kJ g
−1
), and they were fed to triplicate groups of 30 rainbow trout (initial average body weight 0.26 g) for 7 weeks. At the end of the trial, the final weight (FW) was significantly higher in fish fed the GL6 feed (3.49 ± 0.03 g) than that in fish fed the control (3.23 ± 0.03 g) and GL12 (2.85 ± 0.02 g) feeds (
P
< 0.05), but did not differ significantly from fish given the GL9 feed (3.30 ± 0.05 g). Moreover, specific growth rate (SGR) was significantly lower in fish fed the GL12 diet than that in other groups (
P
< 0.05). Feed intake (FI) showed a progressive increase with increasing
Gracilaria
levels. Feed conversion ratio (FCR) decreased up to a
Gracilaria
inclusion level of 6% (1.08 to 0.88) and then increased (1.12 in GL12). Supplementation of the experimental diets with
G. pygmaea
did not affect whole body composition and hematological parameters of juvenile rainbow trout (
P
> 0.05). In conclusion, the findings suggest that a dietary supplement of circa 6%
G. pygmaea
may be useful to promote the growth of juvenile rainbow trout.
In vitro rooting as one of the most critical steps of micropropagation is affected by various extrinsic (e.g., medium composition, auxins) and intrinsic factors (e.g., species, explant). In ...Passiflora species, in vitro adventitious rooting is a difficult, complex, and non-linear process. Since in vitro rooting is a multivariable complex biological process, efficient and reliable computational approaches such as machine learning (ML) are required to model, predict, and optimize this non-linear biological process. Therefore, in the current study, a hybrid of generalized regression neural network (GRNN) and genetic algorithm (GA) was employed to predict in vitro rooting responses (rooting percentage, number of roots, and root length) of Passiflora caerulea based on the optimization of the level of auxins (indole-3-acetic acid (IAA), indolebutyric acid (IBA), and 1-naphthaleneacetic acid (NAA)) and the type of explant (microshoots derived from leaf, node, and internode). Based on the results, the GRNN model was accurate in predicting all in vitro rooting responses of P. caerulea (R2 > 0.92) in either training or testing sets. The result of the validation experiment also showed that there was a negligible difference between the predicted-optimized values and the validated results demonstrating the reliability of the developed GRNN-GA model. Generally, the results of the current study showed that GRNN-GA is a reliable and accurate model to predict and optimize in vitro rooting of P. caerulea.
PDF
