18 results on '"Maliheh Eftekhari"'
Search Results
2. Predictive modeling of Persian walnut (Juglans regia L.) in vitro proliferation media using machine learning approaches: a comparative study of ANN, KNN and GEP models
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Mohammad Sadat-Hosseini, Mohammad M. Arab, Mohammad Soltani, Maliheh Eftekhari, Amanollah Soleimani, and Kourosh Vahdati
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Walnut in vitro propagation ,Artificial neural network ,Gene expression programming ,k-nearest neighbors ,Prediction model ,Plant culture ,SB1-1110 ,Biology (General) ,QH301-705.5 - Abstract
Abstract Background Optimizing plant tissue culture media is a complicated process, which is easily influenced by genotype, mineral nutrients, plant growth regulators (PGRs), vitamins and other factors, leading to undesirable and inefficient medium composition. Facing incidence of different physiological disorders such as callusing, shoot tip necrosis (STN) and vitrification (Vit) in walnut proliferation, it is necessary to develop prediction models for identifying the impact of different factors involving in this process. In the present study, three machine learning (ML) approaches including multi-layer perceptron neural network (MLPNN), k-nearest neighbors (KNN) and gene expression programming (GEP) were implemented and compared to multiple linear regression (MLR) to develop models for prediction of in vitro proliferation of Persian walnut (Juglans regia L.). The accuracy of developed models was evaluated using coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). With the aim of optimizing the selected prediction models, multi-objective evolutionary optimization algorithm using particle swarm optimization (PSO) technique was applied. Results Our results indicated that all three ML techniques had higher accuracy of prediction than MLR, for example, calculated R2 of MLPNN, KNN and GEP vs. MLR was 0.695, 0.672 and 0.802 vs. 0.412 in Chandler and 0.358, 0.377 and 0.428 vs. 0.178 in Rayen, respectively. The GEP models were further selected to be optimized using PSO. The comparison of modeling procedures provides a new insight into in vitro culture medium composition prediction models. Based on the results, hybrid GEP-PSO technique displays good performance for modeling walnut tissue culture media, while MLPNN and KNN have also shown strong estimation capability. Conclusion Here, besides MLPNN and GEP, KNN also is introduced, for the first time, as a simple technique with high accuracy to be used for developing prediction models in optimizing plant tissue culture media composition studies. Therefore, selection of the modeling technique to study depends on the researcher’s desire regarding the simplicity of the procedure, obtaining clear results as entire formula and/or less time to analyze.
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- 2022
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3. Combining gene expression programming and genetic algorithm as a powerful hybrid modeling approach for pear rootstocks tissue culture media formulation
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Saeid Jamshidi, Abbas Yadollahi, Mohammad Mehdi Arab, Mohammad Soltani, Maliheh Eftekhari, Hamed Sabzalipoor, Abdollatif Sheikhi, and Jalal Shiri
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Gene expression programming ,Radial basis function neural network ,Genetic algorithm ,Multiple linear regression ,Pyrodwarf ,OHF ,Plant culture ,SB1-1110 ,Biology (General) ,QH301-705.5 - Abstract
Abstract Background Predicting impact of plant tissue culture media components on explant proliferation is important especially in commercial scale for optimizing efficient culture media. Previous studies have focused on predicting the impact of media components on explant growth via conventional multi-layer perceptron neural networks (MLPNN) and Multiple Linear Regression (MLR) methods. So, there is an opportunity to find more efficient algorithms such as Radial Basis Function Neural Network (RBFNN) and Gene Expression Programming (GEP). Here, a novel algorithm, i.e. GEP which has not been previously applied in plant tissue culture researches was compared to RBFNN and MLR for the first time. Pear rootstocks (Pyrodwarf and OHF) were used as case studies on predicting the effect of minerals and some hormones in the culture medium on proliferation indices. Results Generally, RBFNN and GEP showed extremely higher performance accuracy than the MLR. Moreover, GEP models as the most accurate models were optimized using genetic algorithm (GA). The improvement was mainly due to the RBFNN and GEP strong estimation capability and their superior tolerance to experimental noises or improbability. Conclusions GEP as the most robust and accurate prospecting procedure to achieve the highest proliferation quality and quantity has also the benefit of being easy to use.
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- 2019
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4. High throughput mathematical modeling and multi-objective evolutionary algorithms for plant tissue culture media formulation: Case study of pear rootstocks.
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Saeid Jamshidi, Abbas Yadollahi, Mohammad Mehdi Arab, Mohammad Soltani, Maliheh Eftekhari, and Jalal Shiri
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Medicine ,Science - Abstract
Simplified prediction of the interactions of plant tissue culture media components is of critical importance to efficient development and optimization of new media. We applied two algorithms, gene expression programming (GEP) and M5' model tree, to predict the effects of media components on in vitro proliferation rate (PR), shoot length (SL), shoot tip necrosis (STN), vitrification (Vitri) and quality index (QI) in pear rootstocks (Pyrodwarf and OHF 69). In order to optimize the selected prediction models, as well as achieving a precise multi-optimization method, multi-objective evolutionary optimization algorithms using genetic algorithm (GA) and particle swarm optimization (PSO) techniques were compared to the mono-objective GA optimization technique. A Gamma test (GT) was used to find the most important determinant input for optimizing each output factor. GEP had a higher prediction accuracy than M5' model tree. GT results showed that BA (Γ = 4.0178), Mesos (Γ = 0.5482), Mesos (Γ = 184.0100), Micros (Γ = 136.6100) and Mesos (Γ = 1.1146), for PR, SL, STN, Vitri and QI respectively, were the most important factors in culturing OHF 69, while for Pyrodwarf culture, BA (Γ = 10.2920), Micros (Γ = 0.7874), NH4NO3 (Γ = 166.410), KNO3 (Γ = 168.4400), and Mesos (Γ = 1.4860) were the most important influences on PR, SL, STN, Vitri and QI respectively. The PSO optimized GEP models produced the best outputs for both rootstocks.
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- 2020
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5. Development of an Artificial Neural Network as a Tool for Predicting the Targeted Phenolic Profile of Grapevine (Vitis vinifera) Foliar Wastes
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Maliheh Eftekhari, Abbas Yadollahi, Hamed Ahmadi, Abdolali Shojaeiyan, and Mahdi Ayyari
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bioactive compounds ,grapevine waste ,neural network ,prediction ,regression ,Plant culture ,SB1-1110 - Abstract
High performance liquid chromatography data related to the concentrations of 12 phenolic compounds in vegetative parts, measured at four sampling times were processed for developing prediction models, based on the cultivar, grapevine organ, growth stage, total flavonoid content (TFC), total reducing capacity (TRC), and total antioxidant activity (TAA). 12 Artificial neural network (ANN) models were developed with 79 input variables and different number of neurons in the hidden layer, for the prediction of 12 phenolics. The results confirmed that the developed ANN-models (R2 = 0.90 – 0.97) outperform the stepwise regression models (R2 = 0.05 – 0.78). Moreover, the sensitivity of the model outputs against each input variable was computed by using ANN and it was revealed that the key determinant of phenolic concentration was the source organ of the grapevine. The ANN prediction technique represents a promising approach to predict targeted phenolic levels in vegetative parts of the grapevine.
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- 2018
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6. Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA)
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Mohammad M. Arab, Abbas Yadollahi, Hamed Ahmadi, Maliheh Eftekhari, and Masoud Maleki
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artificial neural network (ANN) ,genetic algorithm (GA) ,G × N15 rootstock ,cytokinin–auxin combination ,proliferation ,Prunus micro-propagation ,Plant culture ,SB1-1110 - Abstract
The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin–auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs) on four growth parameters (outputs), i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW) and the quality index (QI) of plantlets. Calculation of statistical values such as R2 (coefficient of determination) related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3), KIN (9.64), and IBA (2.63) showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53) for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin–auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate), the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro propagation.
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- 2017
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7. Applicability of soft computing techniques for in vitro micropropagation media simulation and optimization: A comparative study on Salvia macrosiphon Boiss
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Mohammad Sadat-Hosseini, Mohammad M. Arab, Mohammad Soltani, Maliheh Eftekhari, and Amanollah Soleimani
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Agronomy and Crop Science - Published
- 2023
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8. An integrated framework (CTSR-BWG) for outsourcing decisions in a marine manufacturing firm
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Reza Rahbin, Maliheh Eftekhari, and Mohsen Cheshmberah
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Decision aiding ,business.industry ,Strategy and Management ,Business ,Management Science and Operations Research ,Statistics, Probability and Uncertainty ,Business and International Management ,Industrial organization ,Management Information Systems ,Outsourcing - Published
- 2019
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9. Chemodiversity evaluation of grape (Vitis vinifera) vegetative parts during summer and early fall
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Abdolali Shojaeiyan, Mahdi Ayyari, Christopher M. Ford, Maliheh Eftekhari, Abbas Yadollahi, and Hossein Hokmabadi
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0106 biological sciences ,Vine ,Chemistry ,DPPH ,food and beverages ,Growing season ,Catechin ,04 agricultural and veterinary sciences ,Berry ,040401 food science ,01 natural sciences ,chemistry.chemical_compound ,Horticulture ,0404 agricultural biotechnology ,Polyphenol ,Botany ,Cultivar ,Quercetin ,Agronomy and Crop Science ,010606 plant biology & botany - Abstract
The majority of studies on grape secondary metabolism are focusing on the berry or stem and leaves phenolic composition. Although the composition of phytochemicals is known to be highly variable in different cultivars, the changes of this composition during vine growth in vegetative parts remain poorly understood. The present study was performed to profile and compare several non-colored polyphenol compounds and the antioxidant potential of Vitis vinifera leaves and stems during a part of growing season and early fall (July, August, September and October) from seventy Iranian native cultivars (white and red) using HPLC-DAD method. Leaves showed significantly higher amounts of analyzed phenolics. Considering the average amounts, the most predominant detected phenolics in the leaves were catechin and o-coumaric acid while the most abundant compound found in the stems was quercetin. Naringenin was found as the lowest detected phenolic. The phenolic potential of the samples varied in each time point. Antioxidant activity was detected higher in stems than leaves with the highest in white cultivar “Atabaki” in both leaves and stems. Leaves of the red cultivars “Angourab” and “Yaghouti shiraz” showed the highest concentrations considering the summation of analyzed phenolics. In addition to the cultivar and plant organ, the phenolic potential of the samples varied in each time point. All samples exhibited antioxidant activity using the DPPH radical and Folin-Ciocalteo Reagent, with mostly higher amounts in stems than leaves showing a strong correlation between two methods In view of both methods, white cultivars were more often higher than red ones with the highest amounts detected in stems and leaves of white cultivar “Atabaki”. PCA results confirmed the variability of phenolic composition in grapevines and during four-month development and showed no obvious discrimination among red and white cultivars. As far as we know, this is the first time that a relationship between the content of polyphenolic compounds is evaluated in leaves and stems, as well as the targeted phenolic profiling of several cultivars foliage. Targeted phenolics profiling indicated the high potential of such wastes to be used in the pharmaceutical, cosmetic and food industries.
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- 2017
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10. Combining gene expression programming and genetic algorithm as a powerful hybrid modeling approach for pear rootstocks tissue culture media formulation
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Mohammad Dehghani Soltani, Mohammad Mehdi Arab, Abdollatif Sheikhi, Maliheh Eftekhari, Hamed Sabzalipoor, Abbas Yadollahi, Jalal Shiri, and Saeid Jamshidi
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0106 biological sciences ,0301 basic medicine ,OHF ,Radial basis function neural network ,Computer science ,Plant tissue culture ,Plant Science ,lcsh:Plant culture ,Machine learning ,computer.software_genre ,01 natural sciences ,03 medical and health sciences ,Tissue culture ,Linear regression ,Genetic algorithm ,Genetics ,Gene expression programming ,lcsh:SB1-1110 ,lcsh:QH301-705.5 ,Multiple linear regression ,Artificial neural network ,business.industry ,Research ,Perceptron ,030104 developmental biology ,lcsh:Biology (General) ,Pear rootstock ,Artificial intelligence ,business ,computer ,Pyrodwarf ,010606 plant biology & botany ,Biotechnology ,Explant culture - Abstract
BackgroundPredicting impact of plant tissue culture media components on explant proliferation is important especially in commercial scale for optimizing efficient culture media. Previous studies have focused on predicting the impact of media components on explant growth via conventional multi-layer perceptron neural networks (MLPNN) and Multiple Linear Regression (MLR) methods. So, there is an opportunity to find more efficient algorithms such as Radial Basis Function Neural Network (RBFNN) and Gene Expression Programming (GEP). Here, a novel algorithm, i.e. GEP which has not been previously applied in plant tissue culture researches was compared to RBFNN and MLR for the first time. Pear rootstocks (Pyrodwarf and OHF) were used as case studies on predicting the effect of minerals and some hormones in the culture medium on proliferation indices.ResultsGenerally, RBFNN and GEP showed extremely higher performance accuracy than the MLR. Moreover, GEP models as the most accurate models were optimized using genetic algorithm (GA). The improvement was mainly due to the RBFNN and GEP strong estimation capability and their superior tolerance to experimental noises or improbability.ConclusionsGEP as the most robust and accurate prospecting procedure to achieve the highest proliferation quality and quantity has also the benefit of being easy to use.
- Published
- 2019
11. Novel organic-based postharvest sanitizer formulation using Box Behnken design and mathematical modeling approach: A case study of fresh pistachio storage under modified atmosphere packaging
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Seyed Hossein Mirdehghan, Maliheh Eftekhari, Hamed Ahmadi, Shahin Gheysarbigi, Abdollatif Sheikhi, Saeid Jamshidi, and Mohammad Mehdi Arab
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Total Viable Count ,Horticulture ,Box–Behnken design ,chemistry.chemical_compound ,Ingredient ,Hand sanitizer ,chemistry ,Modified atmosphere ,Browning ,Postharvest ,Food science ,Citric acid ,Agronomy and Crop Science ,Food Science ,Mathematics - Abstract
In the present study Box Behnken Design (BBD) and Artificial Neural Network-Genetic Algorithm (ANN-GA) hybrid system were used for predicting and optimizing a new organic-based postharvest sanitizer for fresh pistachio nuts under modified atmosphere packaging, combining different concentrations of five generally recognized as safe (GRAS) ingredients including H2O2 (1, 3 and 5%), Na2CO3 (1, 3 and 5%), K2CO3 (1, 3 and 5%), citric acid (CA) and acetic acid (AA, 1000, 5500 and 10,000 mg L−1). The nuts were submerged in sanitizer solutions for two minutes then dried for five minutes in ambient condition and packaged in polyethylene bags injected with ambient atmospheric gas (21% O2, 0.03% CO2, and 87% N2). BBD as a computer-based design of experiment (DOE) tool reduced the cost, labor and time needed to perform the experiment by reducing the number of treatments from 243 to 40 while ensuring the well-sampled experiment design. NaOCl (100 mg L−1) and distilled water (DW) were used as controls for the validation experiment. The ANN-GA described relations between five input (H2O2, Na2CO3, K2CO3, CA, and AA) and four output (total viable count, enzymatic browning, overall acceptability, and taste) variables. Sensitivity analysis was used to find the most important ingredient as input variable affecting output variables. ANN-based models could effectively fit the supplied data on the total viable count and quality parameters of pistachios to various concentrations of the ingredients in the sanitizers. Based on the ANN-GA results, the input variables concentrations of 3.6% H2O2, 3.9% Na2CO3, 3.2% K2CO3, 8118.5 (mg L−1) CA, and 8202.8 mg L−1 AA could result in the lowest total viable count (0.07 log colony forming units (CFU g-1)). The lowest amount of enzymatic browning (1.48%) can be obtained by applying a mixture of 4.26% H2O2, 3.7% Na2CO3, 3.7% K2CO3, 7785.4 mg L−1 CA, and 7363.5 mg L−1 AA. The highest overall acceptability (1.38) can be achieved using a mixture of 3.0% H2O2, 3.0% Na2CO3, 3.4% K2CO3, 8190.1 mg L−1 CA, and 8316.7 mg L−1 AA. The best taste (1.21) was predicted to be attained using a combination of 3.3% H2O2, 4.1% Na2CO3, 3.9% K2CO3, 8114.5 mg L−1 CA, and 7574.0 mg L−1 AA. According to the sensitivity analysis, AA was the most important factor in reducing total viable count and enzymatic browning and enhancing overall acceptability. The validation test showed that the optimized sanitizer for total viable count was superior to the 100 mg L−1 NaOCl which is the most used commercial sanitizer for fresh crops.
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- 2020
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12. Modeling and Optimizing a New Culture Medium for In Vitro Rooting of G×N15 Prunus Rootstock using Artificial Neural Network-Genetic Algorithm
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Abbas Yadollahi, Mohammad Mehdi Arab, Maliheh Eftekhari, Mohammad Akbari, Saadat Sarikhani Khorami, and Hamed Ahmadi
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0106 biological sciences ,0301 basic medicine ,Multidisciplinary ,Chemistry ,lcsh:R ,lcsh:Medicine ,01 natural sciences ,In vitro ,Article ,Butyric acid ,03 medical and health sciences ,Prunus ,chemistry.chemical_compound ,Horticulture ,030104 developmental biology ,Dry weight ,Root length ,lcsh:Q ,Root number ,lcsh:Science ,Rootstock ,010606 plant biology & botany - Abstract
The main aim of the present investigation is modeling and optimization of a new culture medium for in vitro rooting of G×N15 rootstock using an artificial neural network-genetic algorithm (ANN-GA). Six experiments for assessing different media culture, various concentrations of Indole – 3- butyric acid, different concentrations of Thiamine and Fe-EDDHA were designed. The effects of five ionic macronutrients (NH4+, NO3−, Ca2+, K+ and Cl−) on five growth parameters [root number (RN), root length (RL), root percentage (R%), fresh (FW) and dry weight (DW)] were evaluated using the ANN-GA method. The R2 correlation values of 0.88, 0.88, 0.98, 0.94 and 0.87 between observed and predicted values were acquired for all five growth parameters, respectively. The ANN-GA results indicated that among the input variables, K+ (7.6) and NH4+ (4.4), K+ (7.7) and Ca2+ (2.8), K+ (36.7) and NH4+ (4.3), K+ (14.7) and NH4+ (4.4) and K+ (7.6) and NH4+ (4.3) had the highest values of variable sensitivity ratio (VSR) in the data set, for RN, RL, R%, FW and DW, respectively. ANN-GA optimized LS medium for G×N15 rooting contained optimized amounts of 1 mg L−1 IBA, 100, 150, or 200 mg L−1 Fe-EDDHA and 1.6 mg L−1 Thiamine. The efficiency of the optimized culture media was compared to other standard media for Prunus rooting and the results indicated that the optimized medium is more efficient than the others.
- Published
- 2018
13. Optimizing culture media for in vitro proliferation and rooting of Tetra (Prunus empyrean 3) rootstock
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Abbas Yadollahi, Maliheh Eftekhari, F. Sadeghi, and M. Jafarkhani Kermani
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BAP, 6-benzylamino purine ,Vegetative rootstock ,lcsh:QH426-470 ,lcsh:Biotechnology ,MS, Murashige and Skoog (1962) ,FW, fresh weight ,IBA, indol-3-butric acid ,DW, dry weight ,LS, Linsmaier and Skoog (1965) ,Prunus ,Murashige and Skoog medium ,WPM, woody plant medium ,lcsh:TP248.13-248.65 ,Botany ,General Materials Science ,ME, media created specifically (Cos et al., 2004) ,biology ,biology.organism_classification ,In vitro propagation ,lcsh:Genetics ,Micropropagation ,Tetra ,Shoot ,Plant Biotechnology ,Rooting ,Empyrean ,Rootstock ,Explant culture - Abstract
The enormous demand for new rootstock genotypes in Prunus spp. makes us to use micropropagation as an unavoidable propagation method. Therefore, the study on micropropagation of a new semi-dwarf vegetative rootstock namely Tetra (Prunus empyrean 3) was carried out to develop an optimized protocol. Culture establishment using nodal segments was enhanced using WPM (woody plant medium) medium lacking growth regulators. From various shoot multiplication treatments, the highest number of shoots per explant (30.4) was found on ME (Media created specifically) medium supplemented with 0.8 mg l−1 BAP and 0.05 mg l−1 IBA. 100% in vitro rooting was achieved on ½ strength MS medium with 0.5 mg l−1 IBA, 1.6 mg l−1 thiamine and 150 mg l−1 iron sequestrene.
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- 2015
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14. Development of an Artificial Neural Network as a Tool for Predicting the Targeted Phenolic Profile of Grapevine (
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Maliheh, Eftekhari, Abbas, Yadollahi, Hamed, Ahmadi, Abdolali, Shojaeiyan, and Mahdi, Ayyari
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bioactive compounds ,neural network ,Methods ,regression ,Plant Science ,grapevine waste ,prediction - Abstract
High performance liquid chromatography data related to the concentrations of 12 phenolic compounds in vegetative parts, measured at four sampling times were processed for developing prediction models, based on the cultivar, grapevine organ, growth stage, total flavonoid content (TFC), total reducing capacity (TRC), and total antioxidant activity (TAA). 12 Artificial neural network (ANN) models were developed with 79 input variables and different number of neurons in the hidden layer, for the prediction of 12 phenolics. The results confirmed that the developed ANN-models (R2 = 0.90 – 0.97) outperform the stepwise regression models (R2 = 0.05 – 0.78). Moreover, the sensitivity of the model outputs against each input variable was computed by using ANN and it was revealed that the key determinant of phenolic concentration was the source organ of the grapevine. The ANN prediction technique represents a promising approach to predict targeted phenolic levels in vegetative parts of the grapevine.
- Published
- 2017
15. Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA)
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Maliheh Eftekhari, Mohammad Mehdi Arab, Masoud Maleki, Abbas Yadollahi, and Hamed Ahmadi
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0106 biological sciences ,0301 basic medicine ,Coefficient of determination ,proliferation ,cytokinin–auxin combination ,Plant Science ,lcsh:Plant culture ,01 natural sciences ,03 medical and health sciences ,genetic algorithm (GA) ,Proliferation rate ,Genetic algorithm ,lcsh:SB1-1110 ,G × N15 rootstock ,Mathematics ,Artificial neural network ,business.industry ,artificial neural network (ANN) ,030104 developmental biology ,Callus ,In vitro proliferation ,Artificial intelligence ,Biological system ,Rootstock ,business ,Prunus micro-propagation ,010606 plant biology & botany ,Explant culture - Abstract
The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin–auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs) on four growth parameters (outputs), i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW) and the quality index (QI) of plantlets. Calculation of statistical values such as R2 (coefficient of determination) related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3), KIN (9.64), and IBA (2.63) showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53) for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin–auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate), the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro propagation.
- Published
- 2017
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16. Predicting In vitro Culture Medium Macro-Nutrients Composition for Pear Rootstocks Using Regression Analysis and Neural Network Models
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Abbas Yadollahi, Hamed Ahmadi, Saeid Jamshidi, Maliheh Eftekhari, and Mohammad Mehdi Arab
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0106 biological sciences ,0301 basic medicine ,in vitro culture medium ,macro nutrients ,Analytical chemistry ,Plant Science ,lcsh:Plant culture ,01 natural sciences ,regression analysis ,03 medical and health sciences ,Botany ,Methods ,Genetics ,lcsh:SB1-1110 ,PEAR ,Chlorosis ,Chemistry ,Regression analysis ,optimized medium ,In vitro ,030104 developmental biology ,Shoot ,Composition (visual arts) ,Rootstock ,neural network model ,010606 plant biology & botany ,Explant culture - Abstract
Two modeling techniques [artificial neural network-genetic algorithm (ANN-GA) and stepwise regression analysis] were used to predict the effect of medium macro-nutrients on in vitro performance of pear rootstocks (OHF and Pyrodwarf). The ANN-GA described associations between investigating eight macronutrients (NO[Formula: see text], NH[Formula: see text], Ca(2+), K(+), Mg(2+), PO[Formula: see text], SO[Formula: see text], and Cl(-)) and explant growth parameters [proliferation rate (PR), shoot length (SL), shoot tip necrosis (STN), chlorosis (Chl), and vitrification (Vitri)]. ANN-GA revealed a substantially higher accuracy of prediction than for regression models. According to the ANN-GA results, among the input variables concentrations (mM), NH[Formula: see text] (301.7), and NO[Formula: see text], NH[Formula: see text] (64), SO[Formula: see text] (54.1), K(+) (40.4), and NO[Formula: see text] (35.1) in OHF and Ca(2+) (23.7), NH[Formula: see text] (10.7), NO[Formula: see text] (9.1), NH[Formula: see text] (317.6), and NH[Formula: see text] (79.6) in Pyrodwarf had the highest values of VSR in data set, respectively, for PR, SL, STN, Chl, and Vitri. The ANN-GA showed that media containing (mM) 62.5 NO[Formula: see text], 5.7 NH[Formula: see text], 2.7 Ca(2+), 31.5 K(+), 3.3 Mg(2+), 2.6 PO[Formula: see text], 5.6 SO[Formula: see text], and 3.5 Cl(-) could lead to optimal PR for OHF and optimal PR for Pyrodwarf may be obtained with media containing 25.6 NO[Formula: see text], 13.1 NH[Formula: see text], 5.5 Ca(2+), 35.7 K(+), 1.5 Mg(2+), 2.1 PO[Formula: see text], 3.6 SO[Formula: see text], and 3 Cl(-).
- Published
- 2016
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17. Somaclonal variations and their applications in horticultural crops improvement
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Hare Krishna, Nitesh Chauhan, M. Alizadeh, Dhurendra Singh, Udayvir Singh, Maliheh Eftekhari, and Radha Kishan Sadh
- Subjects
0106 biological sciences ,0301 basic medicine ,Germplasm ,Epignetic variation ,Plant tissue culture ,Horticultural crops ,Review Article ,Crop improvement ,Environmental Science (miscellaneous) ,Gene mutation ,Biology ,01 natural sciences ,Somaclonal variation ,Crop ,03 medical and health sciences ,Genetic variability ,business.industry ,Somaclones ,Molecular markers ,food and beverages ,Micropropagation ,Agricultural and Biological Sciences (miscellaneous) ,Biotechnology ,030104 developmental biology ,Oxidative stress ,business ,010606 plant biology & botany - Abstract
The advancements made in tissue culture techniques has made it possible to regenerate various horticultural species in vitro as micropropagation protocols for commercial scale multiplication are available for a wide range of crops. Clonal propagation and preservation of elite genotypes, selected for their superior characteristics, require high degree of genetic uniformity amongst the regenerated plants. However, plant tissue culture may generate genetic variability, i.e., somaclonal variations as a result of gene mutation or changes in epigenetic marks. The occurrence of subtle somaclonal variation is a drawback for both in vitro cloning as well as germplasm preservation. Therefore, it is of immense significance to assure the genetic uniformity of in vitro raised plants at an early stage. Several strategies have been followed to ascertain the genetic fidelity of the in vitro raised progenies comprising morpho-physiological, biochemical, cytological and DNA-based molecular markers approaches. Somaclonal variation can pose a serious problem in any micropropagation program, where it is highly desirable to produce true-to-type plant material. On the other hand, somaclonal variation has provided a new and alternative tool to the breeders for obtaining genetic variability relatively rapidly and without sophisticated technology in horticultural crops, which are either difficult to breed or have narrow genetic base. In the present paper, sources of variations induced during tissue culture cycle and strategies to ascertain and confirm genetic fidelity in a variety of in vitro raised plantlets and potential application of variants in horticultural crop improvement are reviewed.
- Published
- 2016
- Full Text
- View/download PDF
18. Evaluation of the total phenolics and quercetin content of foliage in mycorrhizal grape (Vitis vinifera L.) varieties and effect of postharvest drying on quercetin yield
- Author
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P. Ebrahimi, Maliheh Eftekhari, and M. Alizadeh
- Subjects
chemistry.chemical_classification ,biology ,Inoculation ,Flavonoid ,biology.organism_classification ,Arbuscular mycorrhiza ,chemistry.chemical_compound ,Horticulture ,chemistry ,Plant defense against herbivory ,Postharvest ,Quercetin ,Agronomy and Crop Science ,Pruning ,Glomus - Abstract
Secondary phenolic metabolites play an important role in plant defense mechanisms and most of them are known to be valuable in human health. There are only few studies related to the significant role of various mycorrhizal fungi on changes taking place in their host secondary metabolites. Furthermore, grapevine pruning wastes were known as a potential source of high-value phytochemicals with respect to medicinal and antimicrobial properties. Total phenols and quercetin content of four Iranian grape (Vitis vinifera L.) varieties (Asgari, Khalili, Keshmeshi and Shahroodi) were investigated following inoculation with four arbuscular mycorrhizal fungi (AMF) strains (Glomus mosseae, Glomus fasciculatum and Glomus intraradices and a mixture of three species). Moreover, quercetin content of vegetative parts (leaf and stem tissues) was studied using a simple extraction and isocratic HPLC method. Additionally the effect of a common postharvest processing treatment (oven-drying) on the quercetin content of these grape varieties were also investigated. Higher levels of quercetin were consistently found in Keshmeshi (70.8 μg/g FW) and Shahroodi (100.6 μg/g FW) varieties following inoculation with G. mosseae. The non inoculated plants of the same varieties were found to produce only 35.7 and 16.3 μg/g FW quercetin, respectively. Oven-drying did not affect the leaf quercetin content in none of samples except in Keshmeshi variety in which drying resulted to higher quercetin yield in comparison with fresh leaf tissues (72.4 vs. 37.0 μg/g FW, respectively). The present study suggests that grapevine wastes are valuable sources for extraction of flavonoid quercetin which can also be further increased following inoculation with arbuscular mycorrhizal fungi.
- Published
- 2012
- Full Text
- View/download PDF
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