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Updates every hour. Last Updated: 16-Jun-2026 14:16 ET (16-Jun-2026 18:16 GMT/UTC)
Advances in structural biology have allowed scientists to determine molecular structures with atomic‑level detail, sometimes yielding static snapshots that do not reflect the dynamism of proteins. However, these motions are often crucial for biological function. Researchers from the Institute of Science and Technology Austria (ISTA) together with international collaborators have now combined several methods to shed light on how proteins ‘breathe’ and how some experimental techniques freeze their motion. The findings—which could boost protein design approaches and improve AI-based structural prediction tools—were published in Nature Chemistry.
A CUHK-led team has developed an integrated all-optical signal processor (OSP) that corrects signal distortion directly in light form, reducing latency and energy use compared with conventional digital signal processors (DSP). Built on a silicon photonic chip, it can perform real-time signal equalization in the optical domain and achieve 1.6 Tb/s throughput with under 60 picoseconds latency and extremely low power consumption, while flexibly compensating various transmission impairments, including fiber dispersion, system bandwidth limitation, and nonlinearities. Published in Science, the breakthrough offers a high-bandwidth, low-latency, and energy-efficient solution for high-speed AI data centre interconnects and marks progress toward using light for both data transmission and processing.
Some cancer cells evade medicines by switching to a sleep-like state with the help of stress hormone receptors. Researchers have developed a method that allows them to degrade these receptors and therefore bypass the cells’ protective mechanism. To this end, they built a switch that can be turned on and off with light in the vicinity of the tumour and that tags the receptors for disposal. This system is effective against lung cancer in the lab and could also have future applications in breast and prostate cancer.
A method to interpret artificial intelligence (AI) models used in materials discovery by analyzing their learned features has been developed by researchers from Japan. The method extracts key features from an AI model trained on atomic structural data and optical absorption spectra, and then groups materials with similar structural and spectral characteristics. This approach can be extended to reveal how atomic arrangements influence other material properties, paving the way for more efficient materials design.
In a study published in Nature Astronomy, an Israeli-US team led by researchers from the Weizmann Institute of Science has now defined a new kind of life’s signature. It could offer a relatively simple way to address the age-old question: Are we alone? The new approach relies less on complicated chemistry and more on statistical patterns. The central idea is to examine molecular diversity, with the understanding that life reorganizes chemistry according to function. Sometimes that means expanding diversity and sometimes narrowing it. Instead of focusing on individual molecules, the researchers looked at statistical patterns in groups of molecules – their spread and relative abundances.