Investigating the Effect of Dam Construction on Land Use Change and Land Cover in the Period 1991 to 2020 (Case Study: Gotvand-Khuzestan Dam)

Humans are always trying to change land to use natural resources to meet their needs. One of the land use changes that take place in order to benefit from sustainable water resources is dam construction. Dam construction has many positive and negative consequences for the environment from the beginning to use. The objective of this study was to investigate the effect of Gotvand Dam on the problem of collision of water flow path with salt domes and large volume accumulation of salt behind the dam lake. Images of the Landsat 5 satellite TM sensor for 1991, Landsat 7 satellite ETM+ sensor for 2008, and Landsat 8 satellite OLI sensor for 2020 were used to classify images, and prepare land use maps of the studied basin. Reviewing and evaluating the land use maps of the study area showed that agricultural lands are being developed after the operation of the dam. Also, barren lands were decreasing as well as the area's water content was increasing during the study period. In the second period of study (2008-2020), the population of the regions with an increasing area has been increasing. Also, the rangeland and meadows had a decreasing trend during the first and second periods. The results of classification accuracy using the object-oriented method for three periods of 1991, 2008, and 2020 were obtained as 0.92, 0.97, and 0.93, respectively. In general, it can be stated that the construction of the dam has increased the area under cultivation of land and by increasing population and urbanization in the construction area of the dam, destruction and reduction of rangelands occurred

microRNAs involved in T-cell development, selection, activation, and hemostasis

MicroRNAs (miRNAs) characterized by small, noncoding RNAs have a fundamental role in the regulation of gene expression at the post-transcriptional level. Additionally, miRNAs have recently been identified as potential regulators of various genes involved in the pathogenesis of the autoimmune and inflammatory disease. So far, the interaction between miRNAs and T lymphocytes in the immune response as a new and significant topic has not been emphasized substantially. The role of miRNAs in different biological processes including apoptosis, immune checkpoints and the activation of immune cells is still unclear. Aberrant miRNA expression profile affects various aspects of T-cell function. Accordingly, in this literature review, we summarized the role of significant miRNAs in T-cell development processes. Consequently, we demonstrated precise mechanisms that candidate miRNAs interfere in Immune response mediated by different types of T cells. We believe that a good understanding of the interaction between miRNAs and immune response contributes to the new therapeutic strategies in relation to disease with an immunological origin. © 2020 Wiley Periodicals, Inc

A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles

The aim of this research is to develop three soft-computing techniques, including adaptive-neuro-fuzzy inference system (ANFIS), genetic-programming (GP) tree-based, and simulated annealing–GP or SA–GP for prediction of the ultimate-bearing capacity (Qult) of the pile. The collected database consists of 50 driven piles properties with pile length, pile cross-sectional area, hammer weight, pile set and drop height as model inputs and Qult as model output. Many GP and SA–GP models were constructed for estimating pile bearing capacity and the best models were selected using some performance indices. For comparison purposes, the ANFIS model was also applied to predict Qult of the pile. It was observed that the developed models are able to provide higher prediction performance in the design of Qult of the pile. Concerning the coefficient of correlation, and mean square error, the SA–GP model had the best values for both training and testing data sets, followed by the GP and ANFIS models, respectively. It implies that the neural-based predictive machine learning techniques like ANFIS are not as powerful as evolutionary predictive machine learning techniques like GP and SA–GP in estimating the ultimate-bearing capacity of the pile. Besides, GP and SA–GP can propose a formula for Qult prediction which is a privilege of these models over the ANFIS predictive model. The sensitivity analysis also showed that the Qult of pile looks to be more affected by pile cross-sectional area and pile set