In vitro embryogenic callus is a critical factor for genetic transformation of rice, especially for indica varieties. In this study, we investigated the relationship between polyamines, including putrescine (Put), spermidine (Spd) and spermine (Spm), and callus browning, and we studied the effect of exogenous Put on callus regeneration and on the content of endogenous polyamines. In addition, the expression levels of arginine decarboxylase gene (Adcl) and S-adenosylmethionine decarboxylase gene (Samdc) in embryogenic callus were studied by quantitative Real-time PCR analysis. The results showed that the contents of endogenous Put and Spd in the browning callus were significantly lower than those in normal callus. Exogenous Put could effectively improve the growing state of callus of indica rice and enhance the development of embryogenic callus. The content of endogenous polyamines in embryogenic callus, especially Spd and Spm, was increased after addition of exogenous Put. Additionally, exogenous Put also had an obvious impact on the expression levels of Adcl but partial effect on the expression levels of Samdc gene. This study could increase the knowledge of both embryogenic callus induction and polyamine catabolism in callus in indica rice.
Arbuscular mycorrhizal fungi are the most widespread root fungal symbionts, forming associations with the vast majority of plant species. Ectomycorrhizal development alters gene expression in plant symbionts. In this work we examined the effect of arbuscular mycorrhizal fungi spores on the growth and development of Brassica and on the expression of BnMT2 in winter rape. In a pot experiment, rape seedlings growing on different types of sterile and nonsterile soils were inoculated simultaneously with mycorrhizal fungi spores of Acaulospora longula,Glomus geosporum, Glomus mosseae and Scutellospora calospora. As compared with control plants growing in the absence of spores, ten-week-old seedlings of Brassica napus L. in sterile soil inoculated with arbuscular spores had longer shoots and higher fresh biomass of above-ground plant parts. In other types of substrates enriched with mycorrhizal fungi spores, the plants were smaller than non-inoculated plants. The presence of AMF spores stimulated the elongation growth of hypocotyls in both analyzed substrates. BnMT2 expression was highest in plants growing on the sterile substrate. Generally, the presence of mycorrhizal fungi spores appeared to have an adverse effect on the growth of rape plants.
This paper presents methods for optimal test frequencies search with the use of heuristic approaches. It includes a short summary of the analogue circuits fault diagnosis and brief introductions to the soft computing techniques like evolutionary computation and the fuzzy set theory. The reduction of both, test time and signal complexity are the main goals of developed methods. At the before test stage, a heuristic engine is applied for the principal frequency search. The methods produce a frequency set which can be used in the SBT diagnosis procedure. At the after test stage, only a few frequencies can be assembled instead of full amplitude response characteristic. There are ambiguity sets provided to avoid a fault tolerance masking effect.
In this paper, an attempt was made to find out two empirical relationships incorporating linear multivariate regression (LMR) and gene expression programming (GEP) for predicting the blast-induced ground vibration (BIGV) at the Sarcheshmeh copper mine in south of Iran. For this purpose, five types of effective parameters in the blasting operation including the distance from the blasting block, the burden, the spacing, the specific charge, and the charge per delay were considered as the input data while the output parameter was the BIGV. The correlation coefficient and root mean squared error for the LMR were 0.70 and 3.18 respectively, while the values for the GEP were 0.91 and 2.67 respectively. Also, for evaluating the validation of these two methods, a feed-forward artificial neural network (ANN) with a 5-20-1 structure has been used for predicting the BIGV. Comparisons of these parameters revealed that both methods successfully suggested two empirical relationships for predicting the BIGV in the case study. However, the GEP was found to be more reliable and more reasonable.
Blasting cost prediction and optimization is of great importance and significance to achieve optimal fragmentation through controlling the adverse consequences of the blasting process. By gathering explosive data from six limestone mines in Iran, the present study aimed to develop a model to predict blasting cost, by gene expression programming method. The model presented a higher correlation coefficient (0.933) and a lower root mean square error (1088) comparing to the linear and nonlinear multivariate regression models. Based on the sensitivity analysis, spacing and ANFO value had the most and least impact on blasting cost, respectively. In addition to achieving blasting cost equation, the constraints such as fragmentation, fly rock, and back break were considered and analyzed by the gene expression programming method for blasting cost optimization. The results showed that the ANFO value was 9634 kg, hole diameter 76 mm, hole number 398, hole length 8.8 m, burden 2.8 m, spacing 3.4 m, hardness 3 Mhos, and uniaxial compressive strength 530 kg/cm2 as the blast design parameters, and blasting cost was obtained as 6072 Rials/ton, by taking into account all the constraints. Compared to the lowest blasting cost among the 146-research data (7157 Rials/ton), this cost led to a 15.2% reduction in the blasting cost and optimal control of the adverse consequences of the blasting process.