Cell growth requires a high level of protein synthesis and oncogenic pathways stimulate cell proliferation and ribosome biogenesis. Less is known about how cells respond to dysfunctional mRNA translation and how this feeds back into growth regulatory pathways. The Epstein-Barr virus (EBV)-encoded EBNA1 causes mRNA translation stress in cis that activates PI3K delta. This leads to the stabilization of MDM2, induces MDM2's binding to the E2F1 mRNA and promotes E2F1 translation. The MDM2 serine 166 regulates the interaction with the E2F1 mRNA and deletion of MDM2 C-terminal RING domain results in a constitutive E2F1 mRNA binding. Phosphorylation on serine 395 following DNA damage instead regulates p53 mRNA binding to its RING domain and prevents the E2F1 mRNA interaction. The p14Arf tumour suppressor binds MDM2 and in addition to preventing degradation of the p53 protein it also prevents the E2F1 mRNA interaction. The data illustrate how two MDM2 domains selectively bind specific mRNAs in response to cellular conditions to promote, or suppress, cell growth and how p14Arf coordinates MDM2's activity towards p53 and E2F1. The data also show how EBV via EBNA1-induced mRNA translation stress targets the E2F1 and the MDM2 - p53 pathway.

The rise in business and electronic transactions using the web as a medium has driven the widespread use of recommendation systems across all domains. Recommendation systems generate suggestions for the travel and tourism industry, particularly hotels or services, centered on user interests and preferences. Relevant information collection, organization, processing vast quantities of string data, efficient data retrieval, and computational complexity in handling information overload are key issues to be addressed in a recommendation system. This work proposes a novel approach, using encoded bit vectors, to process information on hotel amenities and features extracted from Tripadvisor. Hotel amenities at different locations are organized and stored, based on the priority of the location, using the most significant bit and least significant bit. The amenities are prioritized dynamically thereafter, based on the location, and unique amenities accorded greater preference than the ones routinely offered at most hotels. Encoded bit vectors are utilized for information retrieval and precise hotel recommendations made, based on user interest rather than the string processing method that is ineffective where scalable data are concerned. Finally, the recommended hotels are ranked with user-centric personalized recommendations. These high-speed, efficient, and scalable algorithms are ideal for incrementally updating data. They also facilitate fast, flawless IR and query processing, along with appropriate hotel recommendations. The theoretical evaluation shows that the proposed method aids quick query processing and retrieval. The experimental assessment shows that hotels recommended by the proposed bit vector recommendation system have a higher accuracy on par with user and expert recommendations.