InGaAs/GaAs metamorphic buffer for laser power converter applications.

This paper investigated the optimization of InGaAs/GaAs metamorphic buffer and its application to LPCs (laser power converters). Firstly, InGaAs (18%) metamorphic buffer with step (S), step + overshoot (SO), step + reverse (SR), and step + reverse + overshoot (SRO) structures were investigated by X-ray, AFM and TEM, respectively. Moreover, an InGaAs (24%) SRO buffer structure with a relaxation of 94.

Dermal fibroblast-derived extracellular matrix (ECM) synergizes with keratinocytes in promoting re-epithelization and scarless healing of skin wounds: Towards optimized skin tissue engineering.

Skin serves as the first-order protective barrier against the environment and any significant disruptions in skin integrity must be promptly restored. Despite significant advances in therapeutic strategies, effective management of large chronic skin wounds remains a clinical challenge. Dermal fibroblasts are the primary cell type responsible for remodeling the extracellular matrix (ECM) in wound healing.

Identifying Candidate Genes Related to Soybean () Seed Coat Color via RNA-Seq and Coexpression Network Analysis.

Background: The quality of soybeans is reflected in the seed coat color, which indicates soybean quality and commercial value. Researchers have identified genes related to seed coat color in various plants. However, research on the regulation of genes related to seed coat color in soybeans is rare.

Detection of heavy metals in soil using Au@SiO nanoparticles and surface microstructure combined with laser-induced breakdown spectroscopy.

The detection of heavy metals in soil is of great scientific significance for food security and human health. However, traditional detection methods are complicated, time-consuming, and labor-intensive. Herein, we developed a novel method using Au@SiO nanoparticles (NPs) and surface microstructure combined with laser-induced breakdown spectroscopy (Au@SiO NPs-SMS-LIBS) for the rapid detection of lead (Pb), chromium (Cr), and copper (Cu) in soil samples.

A prospective and comparative study on improving the diagnostic accuracy of early gastric cancer based on deep convolutional neural network real-time diagnosis system (with video).

Background: Endoscopic diagnosis of early gastric cancer (EGC) is a challenge. It is not clear whether deep convolutional neural network (DCNN) model could improve the endoscopists' diagnostic performance.

Methods: We established a DCNN-assisted system and found that accuracy of diagnosis is higher than endoscopists.