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This month, we’re focusing on artificial intelligence (AI), a topic that continues to capture attention everywhere. Here, you’ll find the latest research news, insights, and discoveries shaping how AI is being developed and used across the world.
Updates every hour. Last Updated: 21-Nov-2025 10:11 ET (21-Nov-2025 15:11 GMT/UTC)
Without Antibodies and Without Amplification: Ultra-fast Identification of Whole Proteins Using a Technology Developed at the Technion
Researchers at the Technion – Israel Institute of Technology have developed a groundbreaking technology for the ultra-fast identification of whole proteins, enabling rapid and precise protein diagnostics without the need for antibodies or molecular amplification. The innovation, led by Prof. Amit Meller and Dr. Neeraj Soni from the Faculty of Biomedical Engineering, represents a major step toward real-time proteome analysis and next-generation medical diagnostics.
Published in Nature Nanotechnology, the study introduces a nanopore-based platform that identifies proteins by reading their unique electrical “fingerprints” as they move through synthetic nanometer-scale pores. The system employs a “stick–slip” mechanism to control protein motion and uses machine learning algorithms to decode the resulting electrical signals, achieving identification speeds several orders of magnitude faster than existing methods.
The researchers demonstrated the approach using the amino acid cysteine, which is found in approximately 97% of human proteins—making the method broadly applicable across the human proteome. The technology, developed in collaboration with the University of Illinois and Rice University, holds promise for diverse clinical applications, including early cancer detection and personalized medicine through rapid blood-based protein analysis.
Supported by a European Research Council (ERC) Advanced Grant under the Horizon 2020 program, this breakthrough provides a new foundation for developing point-of-care systems capable of near-instant protein diagnostics—advancing both biomedical research and patient care.
The University of Texas at Austin has launched a new research consortium to help inform industry partners on options for more sustainable growth of this new industry. The consortium – called Collaborative Optimization & Management of Power Allocation, Surface & Subsurface strategies (COMPASS) – was announced last week at a data center workshop for industry leaders and policy makers led by the UT Bureau of Economic Geology, which is part of the Jackson School of Geosciences.
Researchers at Seoul National University and Kyung Hee University report a framework to control collective motions, such as ring, clumps, mill, flock, by training a physics-informed AI to learn the local rules that govern interactions among individuals. The approach specifies when an ordered state should appear from random initial conditions and tunes geometric features (average radius, cluster size, flock size). Furthermore, trained on published GPS trajectories of real pigeons (Nagy et al., 2010), the model uncovers interaction mechanisms observed in real flocks.
For over 100 years, teddy bears have been a hallmark of childhood nurseries, ubiquitously embedded in our early memories and rarely the object of deep scrutiny. However, according a recent article in BioScience by Dr. Nicolas Mouquet (CRNS) and colleagues, the humble teddy bear is much more than a mere plaything. Instead, the authors suggest that the beloved plushes play a pivotal role in our early conception of nature, potentially shaping the ways we interact with the natural world throughout our lives.
A team of researchers from the University of Science and Technology of China and the Zhongguancun Institute of Artificial Intelligence has developed SciGuard, an agent-based safeguard designed to control the misuse risks of AI in chemical science. By combining large language models with principles and guidelines, external knowledge databases, relevant laws and regulations, and scientific tools and models, SciGuard ensures that AI systems remain both powerful and safe, achieving state-of-the-art defense against malicious use without compromising scientific utility. This study not only highlights the dual-use potential of AI in high-stakes science, but also provides a scalable framework for keeping advanced technologies aligned with human values.