Early intervention of cyanobacterial risks starting from the genome?
Peer-Reviewed Publication
Updates every hour. Last Updated: 24-Jan-2026 06:11 ET (24-Jan-2026 11:11 GMT/UTC)
Recently, a research group led by Professor Jinren Ni published a research paper titled “Genomic blueprint enables early intervention in cyanobacterial risk management” in Science Bulletin. By decoding the genetic secrets behind cyanobacterial toxicity based on cyanobacterial genomes from the world’s largest phosphorus-limited water diversion system, this study proposed a novel early-warning approach: using genome size as an indicator for early prediction of cyanobacterial risks.
Osaka Metropolitan University researchers have developed a polymerization technology that enables the synthesis of degradable polymer capsules in aqueous solvents without any initiators or catalysts by irradiating light-reactive monomers derived from natural products.
TU Graz is launching the COMET project AutoForst for digitalisation and automation of the forestry value chain. The research project has a budget of 6 million euros and is being implemented in collaboration with three other universities and more than 20 industrial partners.
This study presents a transfer learning–based method for predicting train-induced environmental vibration. The method applies data fusion to combine physics-based numerical simulations and limited measurement data within a neural network. It reduces the heavy reliance of conventional machine learning–based models on scarce and costly field measurements while achieving improved prediction accuracy.