MicroRNAs (miRNAs) are short non-protein coding and single-stranded small RNA molecules with a critical role in the regulation of gene expression. These molecules are crucial regulatory elements in diverse biological processes such as apoptosis, development, and progression. miRNA genes have been associated with various human diseases, particularly cancer, and considered as a new biomarker. After the discovery of miRNAs, many researches have focused on identifying and characterizing miRNA genes in cancer. The various expression levels of miRNAs between cancer cells and normal cells are very crucial to diagnosis, prognosis, and treatment of many cancers. Many computational and experimental tools have been employed to characterize miRNAs. However, there exist some challenges in identifying miRNA using both computational and experimental tools due to miRNA features. The present review briefly introduced miRNA biology and certain computational and experimental tools for identifying and profiling miRNAs in cancer. Furthermore, we presented the advantages and challenges of these tools.

Hydraulic performance of emitters plays a major role in the trickle (drip) irrigation system uniformity. But, manufacturing proper emitters is highly expensive and time consuming. On the other hand, the study of flow behavior of the water passing through emitters' channels is difficult because of their small size and complexity. Therefore, it seems that Computational Fluid Dynamics (CFD) can significantly contribute to the study of flow hydraulic characteristics in emitter channels. In the present study, the three emitter codes A, B and C were selected and their channels size determined using the Scanning Electronic Microscope (SEM) for taking the pictures of channels. The flow in emitters channels was simulated with the two software FLOW3D and FLUENT. In this study laminar and turbulence models were applied for the simulation of flow in emitters’ channels, the discharge for each emitter was calculated. Verification of the CFD simulation results with those obtained in laboratory showed that Relative error value (Er) for laminar model of FLUENT, turbulent model of FLUENT, laminar model of FLOW3D, turbulent model of FLOW3D was 4.0, 4.4, 5.3, 12.0 per cent respectively. Laminar model estimated emitters discharge less than turbulent model. Also, it was shown that in both models FLOW3D and FLUENT the error calculated for turbulent model is greater than laminar model and that FLUENT software is more efficient than FLOW3D in the simulation of flow within emitters’ channels. But, generally, it can be said that the two software tools can simulate flow in emitters' channels with a good accuracy.

Introduction: Mutations in the BRCA1 gene are major risk factors for breast and ovarian cancers. However, the relationship between some BRCA1 mutations and cancer risk remains largely unknown. Cancer risk predictions could be improved by evaluation of the impairment degree in the BRCA1 functions due to a specific mutation. This study aimed to assess the functional effect of a novel variant (Glu1661Gly) in BRCA1 gene by a combination of in silico tools, structural analysis, and also experimental functional assay based on yeast transcription activation. Methods: Computational tools including PROVEAN, PolyPhen2, Align-GVGD, Mutation Taster, and also structural analysis were used for prediction of the impact of Glu1661Gly on protein function. To perform the yeast functional assay, the BRCA1 C-terminal (BRCT domain) was cloned into pLexA plasmid in-frame with the DNA-binding domain of LexA to generate a functional transcription activator. The resulted construct was transformed into EGY48/ pRB1840 yeast and positive colonies were assayed for β-galactosidase activity. Wild-type BRCA1 and Ser1613Gly were used as positive controls and Met1775Arg as negative control. Results: The Glu1661Gly variant was predicted to be neutral by PROVEAN, diseasecausing by Mutation Taster, probably damaging by Polyphen2, and intermediate effect by Align-GVGD. The yeast functional assay revealed that Glu1661Gly activity was comparable to wild-type BRCA1. Conclusions: Observed discrepancies between in silico tools make it difficult to interpret the results. Based on structural analysis, the Glu1661Gly on α 1 helix of the C-terminal domain does not seem to impair function due to α 1 helix is far from the BRCT-BRCT interface and phosphopeptide-binding site. This variant was also classified as neutral; using yeast functional assay.

Mutations in the BRCA1 gene are known to be a major cause of hereditary breast cancer. However, characterizing the point mutations associated with cancer in BRCA1 is challenging because the functional impact of most of them is still unknown. Nowadays, a variety of methods are employed to identify cancer-associated mutations in BRCA1. This study is aimed to assess the functional effects of two mutations, Asp1733Gly and Val1714Gly, using a combination of in silico tools and yeast functional transcription activator assay. Our computational analysis showed that theVal1714Gly mutation was deleterious, while the other one, Asp1733Gly, predicted as neutral. Also using yeast functional transcription activator assay, we found that the Asp1733Gly mutation displayed similar ability with positive controls. In contrast, the Val1714Gly mutation completely abrogated transcriptional activity in the yeast. These results suggested that Val1714Gly and Asp1733Gly can be classified as pathogenic and benign mutations for the BRCA1, respectively.

In this study, using the approach of computational social science, the impact of fertility preferences as a key factor on the fertility behavior is examined. Understanding the relationship between preferences and fertility behavior can play a significant role in formulating effective policies to increase fertility rates. The main objective of the research is to analyze and predict the relationship between changes in women's desired number of children and their fertility behavior in Tehran Province. In this regard, the agent-based modeling- ABM method is employed as a key technique in data analysis within the field of computational social science, allowing data from a ten-year period (period (2020–2029) in Tehran province to be analyzed and forecasted. Different scenarios were designed using the AnyLogic software, and their effects on the Total Fertility Rate (TFR) were systematically evaluated. In the first scenario, reducing women's fertility preferences from 2.5 children to 2 children, the TFR is projected to decrease from 1.3 in 2021 to 1.04 in 2028. In the second scenario, increasing the desired number of children to 4 leads to a slight rise in the TFR, reaching approximately 1.08 children by 2028.  The simulation results indicate a significant impact of changes in fertility preferences on the overall fertility trend. In computational social science, the purpose of simulating human and behavioral systems is to understand and extract the trends in behavioral-social patterns, rather than providing precise numerical predictions. This approach can be effective in forecasting demographic changes and evaluating the impact of population policies.

Recent advances in DNA sequencing techniques have led to an increase in the identification of single nucleotide polymorphisms (SNPs) in BRCA1 and BRCA2 genes, but no further information regarding the deleterious probability of many of them is available (Variants of Unknown Significance/VUS). As a result, in the current study, different sequence-and structure-based computational tools including SIFT, PolyPhen2, PANTHER, SNPs&GO, FATHMM, SNAP, PhD-SNP, Align-GVGD, and I-Mutant were utilized for determining how resulted BRCA protein is affected by corresponding missense mutations. FoldX was used to estimate mutational effects on the structural stability of BRCA proteins. Variants were considered extremely deleterious only when all tools predicted them to be deleterious. A total of 10 VUSs in BRCA1 (Cys39Ser, Cys64Gly, Phe861Cys, Arg1699Pro, Trp1718Cys, Phe1761Ser, Gly1788Asp, Val1804Gly, Trp1837Gly, and Trp1837Cys) and 12 in BRCA2 (Leu2510Pro, Asp2611Gly, Tyr2660Asp, Leu2686Pro, Leu2688Pro, Tyr2726Cys, Leu2792Pro, Gly2812Glu, Gly2813Glu, Arg2842Cys, Asp3073Gly, and Gly3076Val) were considered as extremely deleterious. Results suggested that deleterious variants were mostly enriched in the N-and Cterminal domain of the BRCA1 and BRCA2 C-terminus. Utilizing evolutionary conservation analysis, we demonstrated that the majority of deleterious SNPs ensue in highly conserved regions of BRCA genes. Furthermore, utilizing FoldX, we demonstrated that alterations in the function of proteins are not always together with stability alterations.