Search published articles


Showing 2 results for Fallahi
Purification and Partial Characterization of Polygalacturonase From Virulent Isolate Of Ascochyta rabiei (IK06): Causal Agent of Ascochyta Blight in Chickpea
H. Fallahi, M. Motallebi, M.r. Zamani,
Volume 10, Issue 4 (winter 2007)
Abstract
Ascochyta blight caused by Ascochyta rabiei is one of the major diseases of chickpea (Cicer arientinum) in Iran. Many phytopathogenic microorganisms, incuding A .rabiei, attack their host plant by secreting pectic enzymes including polygalacturonase (PG) which causes modification of cell-wall structure, increasing accessibility of cell-wall components for degradation by other enzymes. Polygalacturonase is the major factor in the initiation of Ascochyta blight disease, therefore in this study, the enzyme was purified from a virulent isolate of A .rabiei (IK06). Fungi were cultured in PZ medium culture media were harvested and after dialysis used for purification. Purification was achieved by Carboxy Methyl Sepharose Fast Flow ion exchange column equilibrated to pH= 5.5. Zero to one molar NaCl gradient was used for elution of the proteins from the column. Determination of protein content and enzyme activity of each fraction showed that PG was eluted from the column in 0.3 to 0.4 M salt. The purity of the protein and the MW of the enzyme were determined using SDS-PAGE technique. The MW was found to be around 27 KDa. The activity of the purified protein was also evaluated using polyacrylamide gel containing pectin as substrate (zymogram gel). Optimum pH for the purified enzyme was 7.5.
Improvement of Gene Expression Programming Model Performance using Wavelet Transform for the Estimation of Long-Term Rainfall in Rasht City
M. M. Fallahi, B. Yaghoubi, F. Yosevfand, S. Shabanlou,
Volume 24, Issue 3 (Fall 2020)
Abstract
Rainfall may be considered as the most important source of drinking water and watering land in different areas all over the world. Therefore, simulation and estimation of the hydrological phenomenon is of paramount importance. In this study, for the first time, the long-term rainfall in Rasht city was simulated using an optimum hybrid artificial intelligence (AI) model over a 62 year period from 1956 to 2017. The gene expression programming (GEP) and wavelet transform (WT) were combined to develop the hybrid AI model (WGEP). Firstly, the most effective lags of time series data were identified by means of the autocorrelation function (ACF); then eight various GEP and WGEP models were defined. Next, the GEP models were analyzed and the superior GEP model as well as the most influenced lags was detected. For instance, the variance accounting for (VAF), correlation coefficient (R) and scatter index (SI) for the superior GEP model was calculated to be 0.765, 0.508 and 0.709, respectively. Additionally, lags (t-1), (t-2), (t-3) and (t-12) were the most influenced. Then, the different mother wavelets were examined, indicating that the demy mother wavelet was the most optimal one. Moreover, analyzing the numerical simulations showed that the mother wavelet enhanced the performance of the GEP model significantly. For example, the VAF index for the superior WGEP model was increased almost three times after using the mother wavelet. Furthermore, the R and MARE statistical indices for the WGEP model were computed to be 0.935 and 0.862, respectively.

Page 1 from 1     

© 2024 CC BY-NC 4.0 | JWSS - Isfahan University of Technology

Designed & Developed by : Yektaweb