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.

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Seminal proteins can be considered as factors that control fertilization. Clusterin is one such protein that has been implicated in many activities, including apoptosis inhibition, cell cycle control, DNA repair, and sperm maturation. In this study, the relationship between human secretory clusterin (sCLU) in seminal plasma with sperm parameters, protamine deficiency, and DNA fragmentation was investigated. Semen samples were collected from 63 Iranian men, and semen analysis was performed according to World Health Organization criteria and computer aided semen analysis (CASA). The concentration of sCLU in seminal plasma was measured by enzyme-linked immunosorbant assay (ELISA), protamine deficiency was determined by chromomycin A3 staining (CMA3 ), and sperm DNA fragmentation was checked by sperm chromatin dispersion (SCD) assay. The level of sCLU in seminal fluid of fertile patients was 48.3 ± 38.6 ng/ml and in infertile patients was 15.5 ± 9.7 ng/ml; this difference was significant (P < 0.001). sCLU correlated negatively with protamine deficiency, sperm DNA fragmentation, and abnormal morphology. In conclusion, seminal clusterin can be considered as a marker for the quick assessment of semen quality in male infertility studies.