Why does cancer sometimes recur after chemotherapy? Why do some bacteria survive antibiotic treatment? In many cases, the answer appears to lie not in genetic differences, but in biological noise — random fluctuations in molecular activity that occur even among genetically identical cells.

Biological systems are inherently noisy, as molecules inside living cells are produced, degraded, and interact through fundamentally random processes. Understanding how biological systems cope with such fluctuations — and how they might be controlled — has been a long-standing challenge in systems and synthetic biology.

Although modern biology can regulate the average behavior of a cell population, controlling the unpredictable fluctuations of individual cells has remained a major challenge. These rare “outlier” cells, driven by stochastic variation, can behave differently from the majority and influence system-level outcomes.

A research paper by scientists from Beijing Institute of Technology investigated the anti-tumor effect of millimeter waves (MMWs) alone and in combination with the anti-programmed cell death-ligand 1 (α-PD-L1) antibody in a 4T1 “cold tumor” model.

What if artificial intelligence could turn centuries of scientific literature—and just a few lab experiments—into a smarter, faster way to produce clean energy from waste? That’s exactly what Dr. Yeqing Li and Dr. Junting Pan have achieved with their innovative “knowledge-based machine learning loop framework” (KMLLF), a breakthrough now published in the open-access journal Carbon Research (Volume 4, Article 71, December 16, 2025). Their work redefines how scientists design biochar—the charcoal-like material increasingly used to turbocharge anaerobic digestion (AD), a key process for turning organic waste into renewable biogas.

The challenge of resource allocation for UAV swarms in dynamic and uncertain electromagnetic environments has been investigated for years. In a recent breakthrough published in the Chinese Journal of Aeronautics, a novel intelligent decision-making framework that addresses incomplete interference information has emerged. This innovative framework integrates fuzzy logic for uncertainty modeling, dynamic constrained multi-objective optimization, and transfer learning, enabling UAV swarms to achieve autonomous and efficient spectrum allocation under rapidly changing conditions while maintaining both communication performance and security.

Professor Keisuke Fujii, a leading researcher in quantum science at The University of Osaka, has been named among the Quantum 100, a major global initiative celebrating the centennial of the development of quantum mechanics in 2025, proclaimed by the United Nations and led by UNESCO.