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.

The new research paper, published on Dec 10 2025 in the journal Cyborg and Bionic Systems, investigated the anti-tumor effects of mono-MMW therapy (35 GHz, 10 mW/cm², close-contact irradiation), both alone and in combination with the immune checkpoint inhibitor α-PD-L1 in 4T1 and CT26 “cold tumors”.

Understanding how cells decide their fate is a central challenge in biology, complicated by the fact that single-cell RNA sequencing captures only static snapshots of highly complex and dynamic changes in cell state. In a recent study, researchers from Japan developed a computational method, called ddHodge, that can accurately reconstruct cell state dynamics, shedding light on how genes drive cell fate decisions. Their technique could be useful in developmental biology, regenerative medicine, and cancer research.

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.