A Computational Approach to Understand \u3cem\u3eArabidopsis thaliana\u3c/em\u3e and Soybean Resistance to \u3cem\u3eFusarium solani\u3c/em\u3e (Fsg)

In this study, we reported the analysis of Arabidopsis thaliana microarray gene expression profile of root tissues after the plant was challenged with fungal pathogen Fusarium solani f. sp. glycines (Fsg). Our microarray analysis showed that the infection caused 130 transcript abundances (TAs) to increase by more than 2 fold and 32 out of 130 TAs were increased by more than 3 fold in the root tissues. However, only nineteen ESTs were observed with a decrease in TAs by more than 2 fold and 13 of them went down more than 3 fold due to the pathogen infection. In addition, the number of the up-regulated genes was nearly seven times more than that of downregulated genes. The coordinate regulation of adjacent genes was detected and the distance distribution of the nearest neighbor genes was less likely to be randomly scattered in genome. The results of this study enabled us to decipher the resistance mechanism to Fsg through an integrated computational approach

A Computational Approach to Understand Arabidopsis Thaliana and Soybean Resistance to Fusarium Solani (Fsg)

In this study, we reported the analysis of Arabidopsis thaliana microarray gene expression profile of root tissues after the plant was challenged with fungal pathogen Fusarium solani f. sp. glycines (Fsg). Our microarray analysis showed that the infection caused 130 transcript abundances (TAs) to increase by more than 2 fold and 32 out of 130 TAs were increased by more than 3 fold in the root tissues. However, only nineteen ESTs were observed with a decrease in TAs by more than 2 fold and 13 of them went down more than 3 fold due to the pathogen infection. In addition, the number of the up-regulated genes was nearly seven times more than that of down-regulated genes. The coordinate regulation of adjacent genes was detected and the distance distribution of the nearest neighbor genes was less likely to be randomly scattered in genome. The results of this study enabled us to decipher the resistance mechanism to Fsg through an integrated computational approach

Effects of Irrigation Rate and Planting Density on Maize Yield and Water Use Efficiency in the Temperate Climate of Serbia

Scarce water resources severely limit maize (Zea mays L.) cultivation in the temperate regions of northern Serbia. A two-year field experiment was conducted to investigate the effects of irrigation and planting density on yield and water use efficiency in temperate climate under sprinkler irrigation. The experiment included five irrigation treatments (full irrigated treatment – FIT; 80% FIT, 60% FIT, 40% FIT, and rainfed) and three planting densities (PD1: 54,900 plants ha–1 ; PD2: 64,900 plants ha–1; PD3: 75,200 plants ha–1). There was increase in yield with the irrigation (1.05–80.00%) as compared to the rainfed crop. Results showed that decreasing irrigation rates resulted in a decrease in yield, crop water use efficiency (WUE), and irrigation water use efficiency (IWUE). Planting density had significant effects on yield, WUE, and IWUE which differed in both years. Increasing planting density gradually increased yield, WUE, and IWUE. For the pooled data, irrigation rate, planting density and their interaction was significant (P < 0.05). The highest two-year average yield, WUE, and IWUE were found for FIT-PD3 (14,612 kg ha–1), rainfed-PD2 (2.764 kg m–3), and 60% FITPD3 (2.356 kg m–3), respectively. The results revealed that irrigation is necessary for maize cultivation because rainfall is insufficient to meet the crop water needs. In addition, if water becomes a limiting factor, 80% FIT-PD3 with average yield loss of 15% would be the best agronomic practices for growing maize with a sprinkler irrigation system in a temperate climate of Serbia