Protein digestion in insects relies on several groups of proteases, among which trypsin plays a prominent role. In the current study, larvae of Pieris brassicae L. were fed radish leaves treated with 1 mM concentrations of three specific inhibitors of trypsin: AEBSF.HCl [4-(2- -aminoethyl)-benzenesulfonyl fluoride, monohydrochloride], TLCK (N-a-tosyl-l-lysine chloromethyl ketone) and SBTI (Soybean Trypsin Inhibitor) to find their potential effects on gene expression of trypsin. Initially, RT-PCR analysis revealed a gene of 748 bp responsible for synthesizing the digestive trypsin in P. brassicae larvae. Also, qRT-PCR data indicated a statistically greater expression of trypsin gene in the larvae fed 1 mM concentrations of AEBSF.HCl, TLCK and SBTI than the control. Results of the current study indicated that synthetic inhibitors can not only negatively affect the gene expression of P. brassicae trypsin, but also the insect can activate a compensatory mechanism against interruption of protein digestion by inducing more expression of the gene and producing more trypsin into the midgut lumen.

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.

Banana blood disease (BBD), caused by Ralstonia syzygii subsp. celebesensis ( Rsc), is a major threat to banana production in Southeast Asia. This study aimed to assess the resistance of cultivated and wild banana accessions to Rsc and investigate the expression of pathogenesis- related (PR) protein genes, namely PR3 and PR10, in disease-resistant bananas. Bacterial isolates were isolated from infected bananas in Yala Province, Thailand, and their pathogenicity and phylotype were confirmed, along with Rsc-specific PCR. Rsc-resistance banana screening was conducted on 16 banana accessions, including cultivated and wild types, using representative Rsc isolates. ‘Khai Kasetsart 2’ exhibited resistance (R), followed by ‘Raksa’ with moderate resistance (MR). The expression of PR3 and PR10 genes was analyzed in the resistant ‘Khai Kasetsart 2’ and susceptible ‘Hin’ bananas, revealing distinct expression patterns. PR3 showed rapid upregulation on day 1 after inoculation (DAI), while PR10 exhibited sustained upregulation from 1 to 7 DAI in the resistant cultivar. These findings indicate the involvement of PR proteins in the defense response against Rsc and hold promise for future breeding and disease management strategies in bananas.

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.

Nitrogen (N) is an essential macronutrient for the growth and development of plants, but excessive use of nitrogen fertiliser in agriculture can result in environmental pollution. As a preferred nitrogen form, ammonium (NH ₄ ⁺) is absorbed from the soil by the plants through ammonium transporters (AMTs). Therefore, it is important to explore AMTs to improve the efficiency of plant N utilisation. Here, we performed a comprehensive genome-wide analysis to identify and characterise the AMT genes in barley ( HvAMTs), which is a very important cereal crop. A total of seven AMT genes were identified in barley and further divided into two subfamilies ( AMT1 and AMT2) based on phylogenetic analysis. All HvAMT genes were distributed on five chromosomes with only one tandem duplication. HvAMTs might play an important role in plant growth, development, and various stress responses, as indicated by cis- regulatory elements, miRNAs, and protein interaction analysis. Further, we analysed the expression pattern of HvAMTs in various developmental plant tissues, which indicated that AMT1 subfamily members might play a major role in the uptake of NH ₄ ⁺ from the soil through the roots in barley. Altogether, these findings might be helpful to improve the barley crop with improved nitrogen use efficiency, which is not only of great significance to the crop but also for land and water as it will reduce N fertiliser pollution in the surrounding ecosystem.

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.

It has been acknowledged that two important rock aggregate properties are the Los Angeles abrasion value (LAAV) and magnesium sulphate soundness (M _wl). However, the determination of these properties is relatively challenging due to special sampling requirements and tedious testing procedures. In this study, detailed laboratory studies were carried out to predict the LAAV and M _wl for 25 different rock types located in NW Turkey. For this purpose, mineralogical, physical, mechanical, and aggregate properties were determined for each rock type. Strong predictive models were established based on gene expression programming (GEP) and artificial neural network (ANN) methodologies. The performance of the proposed models was evaluated using several statistical indicators, and the statistical analysis results demonstrated that the ANN-based proposed models with the correlation of determination (R2) value greater than 0.98 outperformed the other predictive models established in this study. Hence, the ANN-based predictive models can reliably be used to predict the LAAV and M _wl for the investigated rock types. In addition, the suitability of the investigated rock types for use in bituminous paving mixtures was also evaluated based on the ASTM D692/D692M standard. Accordingly, most of the investigated rock types can be used in bituminous paving mixtures. In conclusion, it can be claimed that the proposed predictive models with their explicit mathematical formulations are believed to save time and provide practical knowledge for evaluating the suitability of the rock aggregates in pavement engineering design studies in NW Turkey.