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To evaluate phytohormones effects on stomatal conductance, chlorophyll fluorescence, membrane stability, relative water content and chlorophyll content under salinity, a factorial experiment with 4 replicates was conducted. Treatments were salinity (0, 3.5 and 7 dS/m), phytohormones (control, gibberellic acid and abscisic acid) and wheat cultivars (Gascogen, Zagros, and Kuhdasht). Results showed that a high level of salinity increased chlorophyll fluorescence and relative water content, while membrane stability, chlorophyll content, and stomatal conductance were decreased. Abscisic acid treatment had more effective role in membrane stability. Although membrane stability was much more under gibberellic acid treatment, restoration of membrane stability was considerable under abscisic acid treatment for Gascogen and Kuhdasht cultivars. Spraying of gibberellic acid induced the highest chlorophyll content in the three salinity levels and all of the cultivars. The maximum amount of stomatal conductance was achieved under gibberellic acid treatment. Abscisic acid caused less chlorophyll fluorescence in comparison to gibberellic acid. About relative water content, abscisic acid was effective in high salinity levels so that it caused stomatal closure, which reduced water loss and maintained turgor in plants.

Sugar beet is recalcitrant to in vitro tissue culture. Usually, proliferation of in vitro cultured rosette explants is a prerequisite for micropropagation. Although hormonal treatments can induce proliferation in sugar beet rosette explants, they may also result in some side effects. In vitro culture of sugar beet explants and some hormonal treatments make them more prone to hyperhydricity. Effects of media with different concentrations of 6-benzylaminopurine (BAP) and kinetin (Kin) on the proliferation and hyperhydricity of haploid sugar beet explants were investigated. It was observed that 0.2 mg L -1 Kin, with a reasonable amount of proliferation and minimum rate of hyperhydricity, performed better than BAP in different concentrations and combinations. The effect sizes of the treatments on the dependent variables were large. The correlation between proliferation and hyperhydricity of the treated explants was statistically negative and the association was large. However, the hormonal treatments without BAP or with the lowest amount of it produced the highest proliferation rate with the least hyperhydricity. The coefficient of determination was R 2 quadratic = 0.885. The results suggest that, in comparison with BAP, Kin is a potent plant growth regulator for the proliferation of sugar beet haploid explants that causes the least hyperhydricity. Although explants proliferated better in the presence of 0.01 mg L -1 BAP in combination with Kin than under Kin alone, the hyperhydricity of the proliferated explants decreased their suitability for in vitro propagation.

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Predicting the therapeutic result of repetitive transcranial magnetic stimulation (rTMS) treatment could save time and costs as ineffective treatment can be avoided. To this end, we presented a machine-learning-based strategy for classifying patients with major depression disorder (MDD) into responders (R) and nonresponders (NR) to rTMS treatment. Resting state EEG data were recorded using 32 electrodes from 88 MDD patients before treatment. Then, patients underwent 7 weeks of rTMS, and 46 of them responded to treatment. By applying Independent Component Analysis (ICA) on EEG, we identified the relevant brain sources as possible indicators of neural activity in the dorsolateral prefrontal cortex (DLPFC). This was served through estimating the generators of activity in the sensor domain. Subsequently, we added physiological information and placed certain terms and conditions to offer a far more realistic estimation than the classic EEG. Ultimately, those components mapped in accordance with the region of the DLPFC in the sensor domain were chosen. Features extracted from the relevant ICs time series included permutation entropy (PE), fractal dimension (FD), Lempel-Ziv Complexity (LZC), power spectral density, correlation dimension (CD), features based on bispectrum, frontal and prefrontal cordance, and a combination of them. The most relevant features were selected by a Genetic Algorithm (GA). For classifying two groups of R and NR, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) were applied to predict rTMS treatment response. To evaluate the performance of classifiers, a 10-fold cross-validation method was employed. A statistical test was used to assess the capability of features in differentiating R and NR for further research. EEG characteristics that can predict rTMS treatment response were discovered. The strongest discriminative indicators were EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands, and CD. The Combined feature vector classified R and NR with a high performance of 94.31% accuracy, 92.85% specificity, 95.65% sensitivity, and 92.85% precision using SVM. This result indicates that our proposed method with power and nonlinear and bispectral features from relevant ICs time-series can predict the treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording. The obtained results show that the proposed method outperforms previous methods.