The Society for Laboratory Automation and Screening (SLAS) is accepting applications until November 9 for the 2027 SLAS Graduate Education Fellowship Grant, continuing a more than decade-long commitment to supporting the next generation of scientists through innovative graduate research funding. The fellowship provides up to $100,000 over two years to support graduate students pursuing research in life sciences, including quantitative biosciences, drug discovery and laboratory technology. 

Scientists have found a way to use defects in crystals to engineer stronger materials, according to a new study. 

A new Caltech-led paper demonstrates that an AI program—specifically a type of neural network called a recurrent neural operator (RNO)—can be used to predict when tipping points occur in simple models, including models of a climate-change system and simulations of turbulent airflow over a plane's wing. 

 

In Philadelphia, and many urban areas across the country with sewer systems dating back to the 1800s, more frequent flooding is posing a threat not only to people and property, but also to surrounding ecosystems. In these areas stormwater and sewage drain into the same pipes and when flooding occurs they are designed to spill into nearby waterways, rather than inundating streets. Researchers at Drexel University, in collaboration with the Philadelphia Water Department, are looking at the long-term water quality impact of these events — called combined sewer overflows — to understand what steps could be taken to mitigate their negative effects.

Researchers present RFdiffusion3 (RFD3), a new generative AI system that designs proteins and nucleic acids atom by atom, which could enable the ability to engineer novel biomolecules with precise, complex functions. Recent advances in generative deep learning have made it increasingly possible to design proteins from scratch with specific functions. However, most existing approaches work at the amino acid level, which provides enough detail for designing protein structures and interactions but not for precisely engineering enzyme active sites or other functional regions at the atomic scale. Newer methods have introduced atom-level modeling, but they still struggle to design detailed interactions between proteins and nonprotein molecules such as small-molecule ligands, nucleic acids, or catalytic groups. As a result, current approaches remain limited in their ability to create proteins, particularly functional RNA and DNA molecules, with precisely engineered active sites. Jasper Butcher and colleagues developed RFD3, a generative AI system that designs proteins and nucleic acids by modeling individual atoms, enabling precise control over the molecular interactions that give biomolecules their functions. Trained on a broad collection of biological structures, RFD3 can construct complex functional sites, design RNA molecules, and create enzymes that coordinate multiple catalytic components. In computational tests, Butcher et al. show that the model outperforms previous approaches in designing protein structures and organizing catalytic sites. Moreover, laboratory experiments demonstrated its ability to produce functional enzymes from scratch, including enzymes that break down paraoxon, a toxic organophosphate, and a protease engineered to cleave a peptide associated with transthyretin amyloidosis.