STAG3 is the meiotic component of cohesin and a member of the Cancer Testis Antigen (CTA) family. This gene has been found to be overexpressed in many types of cancer, and recently, its variants have been implicated in other disorders and many human diseases. Therefore, this study aimed to analyze the major variants of STAG3. Western blot (WB) and immunoprecipitation (IP) assays were performed using two different anti-STAG3 antibodies that targeted the relevant protein in MCF-7, T-47D, MDA-MB-468, and MDA-MB-231 breast cancer cells with Jurkat and MCF-10A cells as positive and negative controls, respectively. In silico analyses were searched to study the major isoforms. WB and IP assays revealed two abundant polypeptides < 191 kDa and

In this paper, we will study non parametric model when the response variable have missing data (non response) in observations it under missing mechanisms MCAR, then we suggest Kernel-Based Non-Parametric Single-Imputation instead of missing value and compare it with Nearest Neighbor Imputation by using the simulation about some difference models and with difference cases as the sample size, variance and rate of missing data.      

The contemporary business environment is witnessing increasing calls for modifications to the traditional cost system, and a trend towards adopting cost management techniques to provide appropriate financial and non-financial information for senior and executive departments, including the Resource Consumption Accounting (RCA) technique in question, which classifies costs into fixed and variable to support the decision-making process. Moreover, (RCA) combines two approaches to cost estimation, the first based on activity-based cost accounting (ABC) and the second on the German cost accounting method (GPK). The research aims to provide a conceptual vision for resource consumption accounting, after Considering it as an accounting te

Short Multi-Walled Carbon Nanotubes functionalized with OH group (MWCNTs-OH) were used to synthesize flexible MWCNTs networks. The MWCNTs suspension was synthesized using Benzoquinone (BQ) and N, N Dimethylformamide alcohol (DMF) in specific values and then deposited on filter paper by filtration from suspension (FFS) method. Polypyrrole (PPy) conductive polymer doped with metallic nanoparticles (MNPs) prepared using in-situ chemical polymerization method. To improve the properties of the MWCNTs networks, a coating layer of (PPy) conductive polymer, PPy:Ag nanoparticles, and PPy: Cu nanoparticles were applied to the network. The fabricated networks were characterized using an X-ray diffractometer (XRD), UV-Vis. spectrometer, and Ato

Six isolates of A. pullulans were collected from many sources including Hibiscus sabdariffa (Roselle), old Roofs of houses and bathroom surface that referred as Ap ros1, Ap or2, 3, 4 and Ap bs5, 6 respectively, all these isolates were identified based on morphological characteristics and nutritional physiology profiles, all were able to utilize various carbon and nitrogen sources such as glucose, xylose, sucrose, maltose, ammonium sulfate, ammonium nitrate and ammonium chloride, also they showed positive test for starch and amylase, while α-cellulose, ethanol, and methanol were could not be ass

Deep Learning Techniques For Skull Stripping of Brain MR Images

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

Estimating the semantic similarity between short texts plays an increasingly prominent role in many fields related to text mining and natural language processing applications, especially with the large increase in the volume of textual data that is produced daily. Traditional approaches for calculating the degree of similarity between two texts, based on the words they share, do not perform well with short texts because two similar texts may be written in different terms by employing synonyms. As a result, short texts should be semantically compared. In this paper, a semantic similarity measurement method between texts is presented which combines knowledge-based and corpus-based semantic information to build a semantic network that repre