saeideh taheri

Global forest area has decreased by nearly 180 million ha in three decades. So, many ecosystem services provided by forests have been threatened. Reforestation and afforestation are the most effective strategies for mitigating deforestation. Therefore, in a variety of countries worldwide, including Iran, several afforestation projects have been implemented; however, the quantity and quality of many afforested lands have decreased over time under the influence of catastrophic factors, environmental stress, and different management methods. Awareness and continuous monitoring of forest programs are some of the most fundamental forestry policies. Therefore, the monitoring of annual afforestation in the eastern Golestan province using Google Earth software was put on the agenda to prepare an afforestation map and identify the reasons for the success and failure of the implemented plans. According to the findings, 19,377.39 ha of afforestation have been planted in Golestan province. Of these, Maraveh Tappeh city has the highest amount of afforestation with more than 9,256 ha with C. arizonica L, C. sempervirens L. var. horizontalis (Mill.) Gord. and P. brutia Ten, mostly. The presence of suitable climatic and edaphic conditions in the Golidagh of Maraveh Tappeh City and the ArabDagh area of Kalaleh City played a prominent role in the success of artificial forests in these areas. It should be noted that wood smuggling, the presence of livestock, the occurrence of fires, the presence of natural and human obstacles, and continuous monitoring have been other factors affecting the success or failure of afforestation programs in the eastern Golestan province.

Nonlinear analysis methods for quantitative structure–activity relationship (QSAR) studies better describe molecular behaviors, than linear analysis. Artificial neural networks are mathematical models and algorithms which imitate the information process and learning of human brain. Some S-alkyl derivatives of thiosemicarbazone are shown to be beneficial in prevention and treatment of mycobacterial infections and this study seeks to find out the relationship between structural features and the anti-tuberculosis activity of these compounds.

Multiple linear regression and Bayesian regularized artificial neural network (BRANN) for 47 compounds of thiosemicarbazone derivatives were designed using QSAR approaches. Descriptors were selected from a pool of 343 descriptors by stepwise selection and backward elimination. A three layer Bayesian regularized back-propagation feed-forward network was designed, optimized, and evaluated using MATLAB version R2009a.

The best model with 6 descriptors was found using multiple linear regression analysis: Log MIC= 2.592 + (0.067 ± 0.018) PMIX – (0.066 ± 0.017) PMIZ – (1.706 ± 1.600) Qneg – (0.235 ± 0.039) RDF030p + (0.118 ± 0.026) RDF 140u – (0.064 ± 0.021) RDF060p. The best BRANN model was a three-layer network with three nodes in its hidden layer.

The BRANN model has a better predictive power than linear models and may better predict the anti-tuberculosis activity of new compounds with similar backbone of thiosemicarbazone moiety.