RNA barcoding approach reveals previously unknown virus-host relationships
Peer-Reviewed Publication
Updates every hour. Last Updated: 29-Oct-2026 16:16 ET (29-Oct-2026 20:16 GMT/UTC)
An interdisciplinary team of Rice University researchers has uncovered previously unknown relationships between bacteriophages and their bacterial hosts, offering a powerful new tool for next-generation microbiome engineering. Published in Nature Communications, the study utilizes a Rice-developed RNA-based barcoding system that allows scientists to identify which bacteria receive genetic material from bacteriophages in complex microbial environments.
With artificial intelligence tools available on every phone, laptop and tablet, higher education has struggled to implement consistent recommendations for how and when AI can be used. A new national guide seeks to change that.
China’s power grid is set to undergo major transformation by 2030 as wind and solar energy expand rapidly. A new article in Engineering identifies five typical grid development scenarios and key technical challenges in power supply, grid stability, and equipment performance. It also proposes targeted research directions to support secure, low-carbon operation of the future power system, offering clear guidance for technology innovation and grid upgrading.
Based on the underlying paper, the key innovation is not simply better toxicity detection, but an adaptive cascade of AI models. Instead of sending every post through a large, computationally expensive detector, the system starts with faster, lightweight classifiers. If a piece of content is clearly benign or clearly toxic, a decision is made immediately. Only ambiguous cases are passed to increasingly powerful models. A reinforcement-learning algorithm decides which model to consult next, balancing speed, accuracy and computing cost. This approach dramatically increases throughput while slightly improving detection accuracy. (ScienceDirect)
For a 50–60 word EurekAlert summary:
Concordia researchers have developed an AI system that detects toxic online content faster and more accurately by combining multiple detection models in an adaptive sequence. Simple cases are screened by lightweight classifiers, while only difficult content is sent to more powerful AI tools. The approach improves accuracy while processing content up to nine times faster than conventional methods.
Three students from Lehigh University's Department of Materials Science and Engineering have received National Science Foundation Graduate Research Fellowships, one of the nation's most competitive honors for emerging STEM researchers. Their success highlights the impact of a course designed to strengthen scientific communication, proposal writing, and fellowship preparation.