SummaryThe GT‐1 cis‐element widely exists in many plant gene promoters. However, the molecular mechanism that underlies the response of the GT‐1 cis‐element to abiotic and biotic stresses remains elusive in rice. We previously isolated a rice short‐chain peptide‐encoding gene, Os2H16, and demonstrated that it plays important roles in both disease resistance and drought tolerance. Here, we conducted a promoter assay of Os2H16 and identified GT‐1 as an important cis‐element that mediates Os2H16 expression in response to pathogen attack and osmotic stress. Using the repeated GT‐1 as bait, we characterized an abscisic acid, stress and ripening 2 (ASR2) protein from yeast‐one hybridization screening. Sequence alignments showed that the carboxy‐terminal domain of OsASR2 containing residues 80–138 was the DNA‐binding domain. Furthermore, we identified that OsASR2 was specifically bound to GT‐1 and activated the expression of the target gene Os2H16, as well as GFP driven by the chimeric promoter of 2 × GT‐1‐35S mini construct. Additionally, the expression of OsASR2 was elevated by pathogens and osmotic stress challenges. Overexpression of OsASR2 enhanced the resistance against Xanthomonas oryzae pv. oryzae and Rhizoctonia solani, and tolerance to drought in rice. These results suggest that the interaction between OsASR2 and GT‐1 plays an important role in the crosstalk of the response of rice to biotic and abiotic stresses.
Plants are continuously exposed to myriad pathogen stresses. However, the molecular mechanisms by which these stress signals are perceived and transduced are poorly understood. In this study, the maize gene GRMZM2G315431 was identified to be highly inducible by Rhizoctonia solani infection, suggesting that the promoter of GRMZM2G315431 (pGRMZM2G315431) might contain a specific cis-acting element responsive to R. solani attack. To identify the R. solani-responsive element in pGRMZM2G315431, a series of binary plant transformation vectors were constructed by fusing pGRMZM2G315431 or its deletion-derivatives with the reporter genes. In the transient gene expression system of Nicotiana benthamiana leaves inoculated with R. solani, GUS quantification suggested that the DNA fragment contains the unknown pathogen-inducible cis-elements in the −1323 to −1212 region. Furthermore, detailed quantitative assays showed that two novel cis-elements, GTTGA in the −1243 to −1239 region and TATTT in the −1232 to −1228 region, were responsible for the R. solani-inducible activity. These two cis-elements were also identified to have R. solani-specific-inducible activity in stable transgenic rice plants, suggesting the existence of a novel regulation mechanism involved in the interaction between R. solani and Zea mays.
Neural Networks may be made much faster and more efficient by reducing the amount of memory and computation used. In this paper, a new type of neural network called an Adaptive Neural Network is introduced. The proposed neural network is comprised of five unique pairings of events. Each pairing is a module and the modules are connected within a single neural network. The pairings are a simulation of respondent conditioning. The simulations do not necessarily represent conditioning in actual organisms. In the theory presented here, the pairings in respondent conditioning become aggregated together to form a basis for operant conditioning. The specific pairings are as follows. The first pairing is between the reinforcer and the neural stimulus that elicits the behavior. This pairing strengthens and makes salient that eliciting neural stimulus. The second pairing is that of the now salient neural stimulus with the external environmental stimulus that precedes the operant behavior. The third is the pairing of the environmental stimulus event with the reinforcing stimulus. The fourth is the pairing of the stimulus elicited by the drive with the reinforcement event, changing the strength of the reinforcer. The fifth pairing is that after repeated exposure the external environmental stimulus is paired with the drive stimulus. This drive stimulus is generated by an intensifying drive. Within each module, a “0” means no occurrence of a pairing A of Stimuli A and a “1” means an occurrence of a pairing A of Stimuli A. Similarly, a “0” means no occurrence of a pairing Band a “1” means an occurrence of a pairing B, and so on for all 5 pairings. To obtain an output one multiplies the values of pairings through E. In one trial or instance, all 5 pairings will occur. The results of the multiplications are then accumulated and divided by the number of instances. The use of these simple respondent pairings as a basis for neural networks reduces errors. Examples of problems that may be addressable by such networks are included.
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