No abstract

Replication of DNA is an important process for the cell division cycle, gene expression regulation and other biological evolution processes. It also has a crucial role in a living organism’s physical growth and structure. Replication of DNA comprises of three stages known as initiation, elongation and termination, whereas the origin of replication sites (ORI) is the location of initiation of the DNA replication process. There exist various methodologies to identify ORIs in the genomic sequences, however, these methods have used either extensive computations for execution, or have limited optimization for the large datasets. Herein, a model called ORI-Deep is proposed to identify ORIs from the multiple cell type genomic sequence benchmark data. An efficient method is proposed using a deep neural network to identify ORIs for four different eukaryotic species. For better representation of data, a feature vector is constructed using statistical moments for the training and testing of data and is further fed to a long short-term memory (LSTM) network. To prove the effectiveness of the proposed model, we applied several validation techniques at different levels to obtain seven accuracy metrics, and the accuracy score for self-consistency, 10-fold cross-validation, jackknife and the independent set test is observed to be 0.977, 0.948, 0.976 and 0.977, respectively. Based on the results, it can be concluded that ORI-Deep can efficiently predict the sites of origin replication in DNA sequence with high accuracy. Webserver for ORI-Deep is available at (https://share.streamlit.io/waqarhusain/orideep/main/app.py), whereas source code is available at (https://github.com/WaqarHusain/OriDeep).

Smartphone applications are getting popular and have become a necessity. There numerous smartphone applications ranging from entertaining to gaming and from utility to mission-critical. Almost everything on the web is now in hands of Smartphone users, which makes this domain very important and its quality should not be compromised. Achieving the desired quality is not an easy task for the mobile platform as it has its limitations. To produce a quality app, developers and testers need to test and assess the app in numerous ways to ensure the best trait of the application. In this concern, some efficient and mature techniques are required to test smartphone applications. In this study, the techniques, approaches, and models to assess mobile apps covering major prospects and angels to test mobile apps are identified. Our focus is on assessing the existing techniques and to evaluate them on standard validation parameters.

This study analyzes the state of quality engineering practices being exercised in the software industry of Pakistan. Statistics have been collected and analyzed to access important aspects of quality engineering including quality policy, review mechanism, quality assurance activities and practices, quality standards and models, and quality management systems. For this purpose, an elaborated questionnaire was prepared to pertain to various aspects of quality management and more than 30 software houses and software development organizations were surveyed in Islamabad and Rawalpindi. The survey results and a description of the concluding remarks are reported in this paper.

No abstract