In an article featured in Science China Earth Sciences, researchers from Tianjin University elucidate the coupling relationship between soil fungi and reactive minerals in ecosystems by utilizing global fungal carbon stocks, mineral-associated carbon stocks, and high-resolution nanoscale secondary ion mass spectrometry. They also reveal the mechanisms of fungal-nanoparticle interactions belowground. These findings provide new insights into the critical role of microorganisms in soil carbon stability and storage.

In a study published today in Biofunctional Materials, Prof. Dr. Haidar, Founder and CEO of BioMAT’X I+D+I LABs in Santiago, Chile, explores a new era in healthcare diagnostics driven by groundbreaking advancements in point-of-care (PoC) testing, artificial intelligence (AI), and research and development (R&D). These technologies are revolutionizing the way diseases are diagnosed and treated, particularly in dentistry, medicine, and surgery.

Thin-disk single-cavity dual-comb lasers (TDSCs) offer a promising alternative to traditional dual-comb systems, simplifying the setup and enabling power scaling. Their shared resonator components ensure high coherence between the two mode-locked pulses, a key advantage over traditional approaches. While current dual-comb systems predominantly operate in the visible, near-infrared, and mid-infrared ranges, exploration of the ultraviolet (UV) and deep ultraviolet (DUV) regimes has been lacking.  Recently, the research group led by Prof. Hongwen Xuan at the GBA branch of the Aerospace Information Research Institute, collaborating with Prof. Jinwei Zhang’s group in Huazhong University of Science and Technology, achieved the first DUV dual-comb using a high-power TDSC with frequency conversion. This work, demonstrating the application of a TDSC Yb:YAG laser for precise ranging, highlights the potential of this platform for future development of extreme ultraviolet (XUV) and terahertz dual-comb systems.

POSTECH and The Jackson Laboratory, Developing an In Vitro Platform to Evaluate and Predict Drug Responses in Gastric Cancer Patients.

Osaka Metropolitan University researchers have developed a highly accurate method for predicting rents by applying computer vision and machine learning models to street view images.

Researchers in Korea have successfully developed a new material that significantly enhances the efficiency of green hydrogen production while reducing costs.