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With the development of society and the improvement of people's living standards, there is an increasing demand for melanin-inhibiting products that prioritize health, safety, and efficacy. Therefore, the development of natural products that can safely and efficiently inhibit melanin synthesis is of great social significance and has significant market potential. In this paper, by reviewing the literature reported in recent years, we summarized the natural products with inhibition of melanin synthesis effects that have been put into or not yet put into the market, and classified them according to the chemical groups of their compounds or the extraction methods of the natural products. Through the summary analysis, we found that these compounds mainly include terpenoids, phenylpropanoids, flavonoids and so on, while the natural product extracts mainly include methanol extracts, ethanol extracts, and aqueous extracts. Their main inhibition of melanin synthesis mechanisms include: (1) direct inhibition of tyrosinase activity; (2) down-regulation of the α-MSH-MC1R, Wnt, NO, PI3K/Akt and MAPK pathways through the expression of MITF and its downstream genes TYR, TRP-1, and TRP-2; (3) antioxidant; (4) inhibition of melanocyte growth through cytotoxicity; (5) inhibition of melanosome production and transport. This paper provides an in-depth discussion on the research progress of whitening natural products and their market value. The aim is to offer guidance for future research and development of natural skin whitening products.

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Objective.Event-related potentials (ERPs) are cerebral responses to cognitive processes, also referred to as cognitive potentials. Accurately decoding ERPs can help to advance research on brain-computer interfaces (BCIs). The spatial pattern of ERP varies with time. In recent years, convolutional neural networks (CNNs) have shown promising results in electroencephalography (EEG) classification, specifically for ERP-based BCIs.Approach.This study proposes an auto-segmented multi-time window dual-scale neural network (AWDSNet). The combination of a multi-window design and a lightweight base network gives AWDSNet good performance at an acceptable cost of computing. For each individual, we create a time window set by calculating the correlation of signedR-squared values, which enables us to determine the length and number of windows automatically. The signal data are segmented based on the obtained window sets in sub-plus-global mode. Then, the multi-window data are fed into a dual-scale CNN model, where the sizes of the convolution kernels are determined by the window sizes. The use of dual-scale spatiotemporal convolution focuses on feature details while also having a large enough receptive length, and the grouping parallelism undermines the increase in the number of parameters that come with dual scaling.Main results.We evaluated the performance of AWDSNet on a public dataset and a self-collected dataset. A comparison was made with four popular methods including EEGNet, DeepConvNet, EEG-Inception, and PPNN. The experimental results show that AWDSNet has excellent classification performance with acceptable computational complexity.Significance.These results indicate that AWDSNet has great potential for applications in ERP decoding.

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Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. Deep learning methods have been increasingly adopted for ERP-based brain-computer interfaces (BCIs) due to their excellent feature representation abilities, which allow for deep analysis of oscillatory activity within the brain. Features with higher spatiotemporal frequencies usually represent detailed and localized information, while features with lower spatiotemporal frequencies usually represent global structures. Mining EEG features from multiple spatiotemporal frequencies is conducive to obtaining more discriminative information. A multiscale feature fusion octave convolution neural network (MOCNN) is proposed in this article. MOCNN divides the ERP signals into high-, medium- and low-frequency components corresponding to different resolutions and processes them in different branches. By adding mid- and low-frequency components, the feature information used by MOCNN can be enriched, and the required amount of calculations can be reduced. After successive feature mapping using temporal and spatial convolutions, MOCNN realizes interactive learning among different components through the exchange of feature information among branches. Classification is accomplished by feeding the fused deep spatiotemporal features from various components into a fully connected layer. The results, obtained on two public datasets and a self-collected ERP dataset, show that MOCNN can achieve state-of-the-art ERP classification performance. In this study, the generalized concept of octave convolution is introduced into the field of ERP-BCI research, which allows effective spatiotemporal features to be extracted from multiscale networks through branch width optimization and information interaction at various scales.

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