Shahid Chamran University of Ahvaz

About: Shahid Chamran University of Ahvaz is a education organization based out in Ahvāz, Iran. It is known for research contribution in the topics: Population & Catalysis. The organization has 5006 authors who have published 7248 publications receiving 75600 citations.

HOTAIR : an oncogenic long non-coding RNA in different cancers

Abstract: Long non-coding RNAs (lncRNAs) refer to a group of RNAs that are usually more than 200 nucleotides and are not involved in protein generation. Instead, lncRNAs are involved in different regulatory processes, such as regulation of gene expression. Different lncRNAs exist throughout the genome. LncRNAs are also known for their roles in different human diseases such as cancer. HOTAIR is an lncRNA that plays a role as an oncogenic molecule in different cancer cells, such as breast, gastric, colorectal, and cervical cancer cells. Therefore, HOTAIR expression level is a potential biomarker for diagnostic and therapeutic purposes in several cancers. This RNA takes part in epigenetic regulation of genes and plays an important role in different cellular pathways by interacting with Polycomb Repressive Complex 2 (PRC2). In this review, we describe the molecular function and regulation of HOTAIR and its role in different types of cancers.

A review on self-healing coatings based on micro/nanocapsules

Abstract: Polymer coating systems are classically applied on a metal surface to provide a dense barrier against the corrosive species. Coatings are susceptible to damage in the form of cracks, which form deep within the structure where detection is difficult and repair is almost impossible. Major advances for automatic repairing of defects have been made in the present decade within the field of self-healing polymeric materials. One of the most significant types of smart coatings is self-healing coating, which has the ability to release encapsulated active agents in a controlled way. They can be employed to develop a new family of smart multifunctional coatings. Incorporating micro/nanocapsules in coating matrix provides release of repairing agent rapidly after triggering due to crack propagation in coatings and gifts the self-healing to the coatings. This review covers the effective parameters in synthesis of micro/nanocapsules, several approaches to fabricate self-healing coatings based on these capsules and disadvantages of embedding them in coatings matrix. Current comprehensive review also provides all the knowledge of self-healing coatings based on micro/nanocapsules to whom that are concerned with coatings and corrosion prevention.

Stomatal conductance as a screen for osmotic stress tolerance in durum wheat growing in saline soil

Abstract: The change in stomatal conductance measured soon after durum wheat (Triticum turgidum ssp. durum Desf.) was exposed to salinity was verified as an indicator of osmotic stress tolerance. It was a reliable and useful screening technique for identifying genotypic variation. The minimum NaCl treatment needed to obtain a significant stomatal response was 50 mM, but 150 mM was needed to obtain significant differences between genotypes. The response to the NaCl was osmotic rather than Na+-specific. Stomatal conductance responded similarly to iso-osmotic concentrations of KCl and NaCl, both in the speed and extent of closure, and in the difference between genotypes. The new reduced rate of stomatal conductance in response to addition of 50 mM NaCl or KCl occurred within 45 min, and was independent of the concentration of Na+ in leaves. The difference between genotypes was long-lasting, translating into differences in shoot biomass and tiller number after a month. These results indicate that the relative size of the change in stomatal conductance when the salinity is introduced could be a means of screening for osmotic stress tolerance in wheat and other cereals.

The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

Abstract: The main objective of present study is to predict daily global solar radiation (GSR) on a horizontal surface, based on meteorological variables, using different artificial neural network (ANN) techniques. Daily mean air temperature, relative humidity, sunshine hours, evaporation, and wind speed values between 2002 and 2006 for Dezful city in Iran (32°16′N, 48°25′E), are used in this study. In order to consider the effect of each meteorological variable on daily GSR prediction, six following combinations of input variables are considered: (I) Day of the year, daily mean air temperature and relative humidity as inputs and daily GSR as output. (II) Day of the year, daily mean air temperature and sunshine hours as inputs and daily GSR as output. (III) Day of the year, daily mean air temperature, relative humidity and sunshine hours as inputs and daily GSR as output. (IV) Day of the year, daily mean air temperature, relative humidity, sunshine hours and evaporation as inputs and daily GSR as output. (V) Day of the year, daily mean air temperature, relative humidity, sunshine hours and wind speed as inputs and daily GSR as output. (VI) Day of the year, daily mean air temperature, relative humidity, sunshine hours, evaporation and wind speed as inputs and daily GSR as output. Multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are applied for daily GSR modeling based on six proposed combinations. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data. The comparison of obtained results from ANNs and different conventional GSR prediction (CGSRP) models shows very good improvements (i.e. the predicted values of best ANN model (MLP-V) has a mean absolute percentage error (MAPE) about 5.21% versus 10.02% for best CGSRP model (CGSRP 5)).