Background: Alopecia areata(AA) is a common autoimmune disease that causes hair loss without scarring. It occurs as a result of T-helper 1 (Th1) and Th17 cells attacking the anagen hair follicles. Genetic factors play a role in the occurrence of infection, which stimulates the production of pro and anti-inflammatory interleukins. Polymorphisms of IL-37 play a role in autoimmune diseases. However, IL37 single nucleotide polymorphisms(SNP) have not been identified in patients with AA. Therefore, this study aimed to reveal the IL37 gene SNP and its relationship to AA. Methods: Genotyping of IL-37 gene single nucleotide polymorphisms SNPs were detected using sequence-specific primer-polymerase chain reaction (SSP-PCR) method was done following

In this research want to make analysis for some indicators and it's classifications that related with the teaching process and the            scientific level for graduate studies in the university by using analysis of variance for ranked data for repeated measurements instead of the ordinary analysis of variance . We reach many conclusions  for the                         

important classifications for each indicator that has affected on   the teaching process.         &nb

تحليل الشراكات التجارية للعراق 2003-2013

Is the subject of environmental analysis of the important issues that should occupy the attention of the departments of banks in general and banks in particular, the research sample, regardless of their size and the nature of its activity, So if the current era and witnessing rapid developments and changes, making the banking environment is more complex and interconnected than ever, And it became not possible for banks to be aware and understand the external environment variables without an analysis of the various constituent elements and influential heavily in its financial and administrative,Therefore research aims to find out the relationship between the external environment and banking facilities analysis application on the Rafidain

Automatic Programming Assessment (APA) has been gaining lots of attention among researchers mainly to support automated grading and marking of students’ programming assignments or exercises systematically. APA is commonly identified as a method that can enhance accuracy, efficiency and consistency as well as providing instant feedback on students’ programming solutions. In achieving APA, test data generation process is very important so as to perform a dynamic testing on students’ assignment. In software testing field, many researches that focus on test data generation have demonstrated the successful of adoption of Meta-Heuristic Search Techniques (MHST) so as to enhance the procedure of deriving adequate test data for efficient t

Simulation experiments are a means of solving in many fields, and it is the process of designing a model of the real system in order to follow it and identify its behavior through certain models and formulas written according to a repeating software style with a number of iterations. The aim of this study is to build a model  that deals with the behavior suffering from the state of (heteroskedasticity) by studying the models (APGARCH & NAGARCH) using (Gaussian) and (Non-Gaussian) distributions for different sample sizes (500,1000,1500,2000) through the stage of time series analysis (identification , estimation, diagnostic checking and prediction). The data was generated using the estimations of the parameters resulting f

توزيعات كثافة البروتون (PDD)، خلافاتهم وتناثر الإلكترون مرنة عوامل الشكل، F (ف) من ارض الدولة لبعض نوى قذيفة، مثل ( ¹⁰⁴ المشتريات، ¹⁰⁶

The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA