The emergence of the formalism school in the early years of the twentieth century has had a profound impact on the way in which literary and artistic texts are analyzed. Hence, the study of literacy in a cinematic work is not limited to vocabularies and also includes linguistic, visual and content aspects of the work. As in literature, the use of figures of speech (semantics eloquence rhetoric) in words and speech makes the text eloquent in the cinema these figures of speech with the help of images, montage, mise en scene, lighting , … create eloquent meaning differently. The present paper, based on formalists and neo-formalist views, seeks to study and analyze the film “mother” by Ali hatami from the point of view of literary and explaining its artistic and literary aspects. For this purpose in addition to analyzing the text of the script rhetoric has been analyzed in its images. According to the analysis  it should be said that the use of many literary arrays in the dialogue between the characters of the story and the use of methods and techniques for the transfer of concepts by the image-these methods have a long history in the literature- made the work of hatami prominent in literary style and gave it an independent identity that  with every time exposed by the informed audience to this work  the literary capacity and influence of the lexical and concept  terms of classical Persian literature are more than ever evident.

Aims: The production of biofuels has been one of the promising efforts in biotechnology in the past decades. Unicellular cyanobacteria are widespread phototrophic microorganisms that can be suitable chassis for production of valuable organic materials like biofuels. The aim of this study was the reconstruction and modeling of integrated metabolic network of a cyanobacterium to increase the production of biofuels.
Materials and Methods: In the present computational study, a software for integrating reconstructed metabolic networks was developed to optimize and increase their efficiency and was named as iMet. First, iMet was used to integrate the 3 pre-reconstructed metabolic networks of Synechocystis PCC6803. In the next step, the reconstructed network was modeled to produce 4 types of biofuels, including ethanol, propanol, butanol, and isobutanol.
Findings: The new merged model had 808 reactions and 560 metabolites. The amount of flux or flow in the integrated model was calculated to be 0.0295 hours per hour. This showed a remarkable increase compared to the previous three models. The cells were divided once every 24 hours. The amount of flux of 4 types of alcohol and their maximum theoretical efficiency increased in the integrated model compared to the previous 3 models. The flux of ethanol production was greater in all models than flux of 3 other alcohols, and the ethanol production reactions were closer to the flow or the central flux of carbon.
Conclusion: The analyses of flow equilibrium in the metabolic network coverage show an increase in the production of biofuels and a decrease in the number of blocked reactions in the new model, thereby the efficiency of the developed iMet software is proved.

Aims: Information of the protein structure is essential to understand the protein functions. Flexibility is one of the most important characteristics related to protein functions. Knowledge about flexibility of the protein structures can be helpful to improve protein structure prediction and comprehend their function. This study was conducted with the aim of investigating the flexibility prediction of protein structures, using support vector machine.
Materials and Methods: In this study, a balanced dataset containing 95 proteins was used. The features used in the present study for modeling amino acids formed a 33-dimensional vector. Some of them were obtained by crawling a window with the length of 17 focusing on the target amino acid on the protein chain, and some were only related to the target amino acid. To define the flexibility factor, the characteristics based on the information derived from the two-dimensional angular variations was used. The information was calculated for each amino acid by considering the position of each amino acid alone and for the adjacent amino acid pairs in a seventeenth window, and the support vector machine method was used for prediction.
Findings: The accuracy was 73.1%, F-measure was 71%, precision was 73%, and sensitivity was 73.2%. Acceptable superiority of the proposed method was confirmed in comparison with the current methods. The angular representation of each protein was able to accurately demonstrate the 3D characteristics and properties of the protein structure.
Conclusion: The accuracy is 73.1%, F-measure is 71%, precision is 73%, and sensitivity is 73.2% and angular aspect is the best descriptor for flexibility prediction. Angular representation of each protein can accurately reflect the 3D characteristics and properties of the protein structure.
 

​Genome editing using targetable nucleases is an emerging technology for precise genome modification in many organisms with hight ability and capability. All targeted genome engineering relies on the introduction of a site-specific double-strand break (DSB) in a pre-determined genomic locus by a rare-cutting DNA endonuclease. Subsequent repair of this DSB by non-homologous end joining (NHEJ) or homology-directed repair (HDR) generates the desired genetic modifications such as gene disruption, gene insertion, gene correction, etc. Three types of endonucleases, namely ZFNs (zinc finger nucleases), TALENs (transcription activator-like effector nucleases), and the CRISPR (clustered regularly interspersed short palindromic regions) associated (Cas9) system have been predominantly utilized for gene editing. Targeted genome engineering or editing enables researchers to modify genomic loci of interest in a precise manner, which has a turning point in medicine, biological research, and biotechnology. Treatment of human immunodeficiency virus (HIV) infection with ZFN-mediated CCR5 gene disruption is one of the indicator examples of the ability of ZFNs in genome editing. The emergence of TALENs in 2010 has enabled the genome modification of non- model organisms, while the emergence of the CRISPR/Cas9 system in 2013 as a revolutionary genome-editing tool has allowed us to anticipate the forthcoming new era of genome editing research. Soon, it is likely that tgenome editing also will provide the possibility of treating genetic diseases. Genome editing is also hoped to be available for use in the generation of crops and livestock with useful traits. An example would be the production of edible fungi resistant to browning by inactivation of the genes encoding polyphenol oxidase in 2016 under the non-GMO genetically edited crop plants and production of herbicide-resistant rice and rapeseed using CRISPR/Cas9 systems. In this article, we review essential genome editing tools, summarize their applications in crop improvement, as well as, next-generation crop breeding and their computational resources will be discussed.

Hordeum vulgare is a one-year-old herb of the Poaceae family. It is an important cereal used by humans which has been applied in many cases instead of wheat. The limitation of experimental methods is one of the important problems for identifying protein-protein interactions. So, in recent years, computational methods have played an important role in predicting and identifying protein-protein interactions. In this study, for constructing protein-protein interaction (PPI) network, the experimental PPI information of six model organisms includes Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Oryza sativa, and Arabidopsis thalian were extracted from the Intact database. Inparanoid was used for identifying barley orthologous proteins with model organisms. The Interolog method which was used in this study can predict protein-protein interactions by mapping protein interactions of the model organisms on orthologous proteins. After removing repetitive interactions, the final predicted barley PPI network contained 235966 interactions between 7350 proteins. This study is the first report presented on protein-protein interaction prediction in barley.