Conventional handheld photoacoustic and ultrasound Imaging (PAUS), while offering flexibility, offers only a narrow view of the target region, providing limited information on its structure. Alternative methods require external sensors and bulky equipment, which can also make measurements inaccurate. In a new study, researchers developed a MoGLo-Net, a deep-learning model for 3D reconstruction of 2D PAUS images. This method does not require any external sensors and can make treatments more accessible, safer, and effective.

Researchers in Japan have developed a needle-type multi-enzyme biosensor that enables real-time monitoring of sucrose levels inside living plants. The sensor revealed daily patterns of sugar transport in strawberry guava and demonstrated that Japanese cedar can absorb sucrose through light-regulated stomata. The device, with its high sensitivity and stability, opens new avenues for studying plant physiology and optimizing agricultural practices through continuous, in vivo tracking of sugar dynamics under natural and controlled conditions.