Poly(A)polymerase-alpha (PAPOLA) has been the most extensively investigated mammalian polyadenylating enzyme, mainly in regard to its multifaceted post-translational regulation. The possibility of translational regulation of this enzyme was addressed. The transcription start site was mapped and two uORFs, highly conserved among several species, were identified in the 211-bp long, GC-rich, 5' UTR of the PAPOLA mRNA. Mutation of the 5' proximal AUG resulted in increased translational efficiency of the adjacent coding sequence, whereas no significant effect was observed after mutation of the second AUG. These observations imply that translational regulation is among the conserved mechanisms regulating PAPOLA expression.
The identification of defect causes plays a key role in smart manufacturing as it can reduce production risks, minimize the effects of unexpected downtimes, and optimize the production process. This paper implements a literature review protocol and reports the latest advances in Root Cause Analysis (RCA) toward Zero-Defect Manufacturing (ZDM). The most recent works are reported to demonstrate the use of machine learning methodologies for root cause analysis in the manufacturing domain. The popularity of these technologies is then summarized and presented in the form of visualizing graphs. This enables us to identify the most popular and prominent methods used in modern industry. Although artificial intelligence gains more and more attraction in smart manufacturing, machine learning methods for root cause analysis seem to be under-explored. The literature survey revealed that only limited reviews are available in the field of RCA towards zero-defect manufacturing using AI and machine learning; thus, it attempts to fill this gap. This work also presents a set of open challenges to determine future developments.
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