Codon usage bias analysis of genes linked with esophagus cancer

Esophageal cancer involves multiple genetic alternations. A systematic codon usage bias analysis was completed to investigate the bias among the esophageal cancer responsive genes. GC-rich genes were low (average effective number of codon value was 49.28). CAG and GTA are over-represented and under-represented codons, respectively. Correspondence analysis, neutrality plot, and parity rule 2 plot analysis confirmed the dominance over mutation pressure in modulating the codon usage pattern of genes linked with esophageal cancer.     

Indian publishing: enduring the boom

There has been a boom in the publication of Indian plant science research in recent years, defying national trends in other sciences and outperforming the international trends in plant science publications. This boom augurs well for India considering the importance of agriculture to her economy and the crucial need for science-based solutions to break the yield barriers. However, sustaining it requires tackling the problems of funding, infrastructure, manpower and other policy issues.     

Digital by design approach to develop a universal deep learning AI architecture for automatic chromatographic peak integration

Chromatographic data processing has garnered attention due to multiple Food and Drug Administration 483 citations and warning letters, highlighting the need for a robust technological solution. The healthcare industry has the potential to greatly benefit from the adoption of digital technologies, but the process of implementing these technologies can be slow and complex. This article presents a “Digital by Design” managerial approach, adapted from pharmaceutical quality by design principles, for designing and implementing an artificial intelligence (AI)-based solution for chromatography peak integration process in the healthcare industry. We report the use of a convolutional neural network model to predict analytical variability for integrating chromatography peaks and propose a potential GxP framework for using AI in the healthcare industry that includes elements on data management, model management, and human-in-the-loop processes. The component on analytical variability prediction has a great potential to enable Industry 4.0 objectives on real-time release testing, automated quality control, and continuous manufacturing.