Even well-known proteins can surprise us; some go beyond their recognised roles, stepping in to awaken latent viruses and trigger oncogenic pathways. A newly established team of researchers at Åbo Akademi University, led by Academy Research Fellow Silvia Gramolelli (Faculty of Science and Engineering), has discovered an additional function for Heat Shock Factor 2 (HSF2), a human transcription factor not previously associated with viral processes.

To provide the right treatment for MS, it is important to know when the disease changes from relapsing-remitting to secondary progressive, a transition that is currently recognised on average three years too late. Researchers at Uppsala University have now developed an AI model that can determine with 90 per cent certainty which variant the patient has.

Metal oxyhydroxides are nanoparticles with wide industrial applications, but determining their exact structure is often challenging. Recently, a research team from the Japan Advanced Institute of Science and Technology has developed an advanced imaging method called “lattice correlation analysis” to reveal the detailed 3D atomic structure of titanium oxyhydroxide nanoparticles. By leveraging data-driven insights, this method unlocks the crystal attributes without causing any damage, thus marking a milestone in the study of sensitive nanomaterials.

A new study, seen in Nature Genetics and led by researchers at King Abdullah University of Science and Technology (KAUST; Saudi Arabia) and Wageningen University & Research (the Netherlands), provides new insights on rice evolution, showing how the DNA of this valuable crop has changed across species. The findings are expected to not only help with improving rice yields but also with the introduction of rice into regions where rice production is currently untenable. 

Scientists at Columbia Engineering have created a machine learning algorithm that can observe the pattern produced by nanocrystals to infer the material’s atomic structure, as described in a new study published in Nature Materials.

Researchers from AIMR developed an AI-driven approach to model carbon growth on metal surfaces with high accuracy. By integrating molecular dynamics, time-stamped force-biased Monte Carlo simulations, and machine learning, their method replicates key processes like carbon diffusion and graphene nucleation, enabling scalable, efficient carbon nanomaterial production for electronics and energy storage.

The power conversion efficiency of all-organic solar cells was doubled through the development of novel organic electrodes that can be fabricated using a moderate process.