An optimized lightweight real-time detection network model for IoT embedded devices.

With the rapid development of Internet of Things (IoT) technology, embedded devices in various computer vision scenarios can realize real-time target detection and recognition tasks, such as intelligent manufacturing, automatic driving, smart home, and so on. YOLOv8, as an advanced deep learning model in the field of target detection, has attracted much attention for its excellent detection speed, high precision, and multi-task processing capability. However, since IoT embedded devices typically own limited computing resources, direct deployment of YOLOv8 is a big challenge, especially for real-time detection tasks.

Rapid and Sensitive Detection of Mutations in SARS-CoV-2 by Surface-Enhanced Raman Spectroscopy.

Since the outbreak of the novel coronavirus (SARS-CoV-2), the world has suffered significant losses. At present, the pneumonia disease caused by SARS-CoV-2 virus has not been eliminated, and SARS-CoV-2 has a high mutation rate, and its variant strains also have a high prevalence rate, which has always threatened the health of all mankind. This study aims to develop a rapid and sensitive method to complement existing SARS-CoV-2 diagnostic tools by utilizing surface-enhanced Raman spectroscopy (SERS) for the direct detection of the intrinsic SERS signal from the S proteins of SARS-CoV-2 and its variants (Omicron and Delta) within 5 min using a portable Raman spectrometer.

Revealing structure-performance relationship of biomimetic cyclodextrin metal-organic framework composite electrolytes for dendrite-free lithium metal batteries.

Cyclodextrin metal-organic frameworks (CD-MOFs) with infinitely extensible network structures show potential applications in lithium metal batteries. However, the disordered accumulation of CD-MOF particles leads to slow interparticle diffusion of ions, so the CD-MOF composite electrolytes are needed to be developed. In addition, the influences of CD-MOFs structure on the electrochemical performance of the composite electrolytes remains unclear.

Construction of a rodent neural network-skeletal muscle assembloid that simulate the postnatal development of spinal cord motor neuronal network.

Neuromuscular diseases usually manifest as abnormalities involving motor neurons, neuromuscular junctions, and skeletal muscle (SkM) in postnatal stage. Present in vitro models of neuromuscular interactions require a long time and lack neuroglia involvement. Our study aimed to construct rodent bioengineered spinal cord neural network-skeletal muscle (NN-SkM) assembloids to elucidate the interactions between spinal cord neural stem cells (SC-NSCs) and SkM cells and their biological effects on the development and maturation of postnatal spinal cord motor neural circuits.