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Background: Aging is a physiological phenomenon in the process of life, and skin aging has a significant impact on human appearance. Therefore, the search for methods to delay skin aging is of great significance for improving the quality of human life. Materials and Methods: This study investigated the anti‐photoaging effect of Tricholoma matsutake (T) extract composition combined with bakuchiol (B) and ergothioneine (E), and explored its potential mechanism through transcriptome, metabolomics, and network pharmacology. Results: 57 main chemical components are identified from the ethanol extract of T. matsutake (T), including D‐carnitine (24.55%), α,α‐trehalose (15.56%), DL malic acid (8.99%), D‐(−)‐quinic acid (7.46%), erucamide (7.04%) and so on. After TBE treatment, inflammation of the mice dorsal skin is significantly minimized. Hematoxylin and eosin (H&E) staining and toluidine blue staining reveal that TBE has an anti‐inflammatory effect on the back skin tissue of mice. Masson staining shows that TBE has a repair effect on mice dorsal skin tissue. In addition, the inflammatory factors (IL‐1β, IL‐6, TNF‐α) in the mice dorsal skin tissues are significantly reduced but collagen (COL‐1) is significantly increased. By cellular immunofluorescence assay, TBE is shown to promote PPAR‐α expression in cells. Transcriptomics, metabolomics, and network pharmacology have revealed that TBE can regulate exogenous stimuli and cancer‐related signaling pathways to prevent skin aging. Conclusion: The results suggest that TBE can be a beneficial supplement to natural anti‐aging. [ABSTRACT FROM AUTHOR]

Phase angle and vitamin D can reflect the state of the body cells, and the two may interact with each other. Therefore, this study was conducted to find out the relationship between PA and vitamin D. Taking women in early pregnancy as our study subjects, we found that PA had a positive effect on vitamin D levels. Body composition phase angle, as a noninvasive, easy‐to‐operate, and easy‐to‐monitor indicator, can be used an early screening index for vitamin D nutrition levels in early pregnancy, and the cutoff value was 4.95°. [ABSTRACT FROM AUTHOR]

Under the circumstance of simultaneously scanning multiple regions in an environment which can change as time goes by, the authors study an optimal algorithm to solve antenna deployment problem for MIMO radar systems here. It is solved by multi-objective particle swarm optimisation algorithm (MOPSO) combining an autoregressive (AR) prediction model (MOPSO-AR). In a time period and dynamic environment, the MOPSO-AR method uses the previous optimal information to calculate the current deployment schemes before PSO optimisation starts up. It greatly reduces computational load and the error of the solutions. First, by discretising the time period into several time intervals, the problem in each time interval can be seen as a static problem. However, there may be relationship between these time intervals. Second, use the previous information and an AR model to predict temporally optimal solutions. Then, to get the exact optimal solutions, the predicted solutions and PSO method was applied to compute. Simulations show that the prediction strategy improves algorithm performance. [ABSTRACT FROM AUTHOR]

Recently, a deep learning technique has been introduced to saliency detection and has achieved promising results, but most of them are based on superpixel algorithms. Consequently, their performances and efficiencies depend largely on the results of a segmentation algorithm. Instead of classifying superpixels, we treat salient object detection as a dense prediction task. Fully convolutional networks show strong potential in dense prediction tasks, but the resolution and the quality of output maps need improving due to the loss of location information. In order to achieve high‐quality saliency maps, we propose a very efficient method using two Fully Convolutional Networks (FCNs) to extract global and local information respectively via different receptive fields. The global model produces accurate but coarse saliency maps, while the refined model produces full‐sized, fine results. We evaluate our method on eight public datasets and find that our method outperforms the other state‐of‐the‐art methods. Besides, our method runs much faster than the existing deep learning methods. [ABSTRACT FROM AUTHOR]