Exploring the mechanism of intestinal injury induced by Bisphenol S in freshwater crayfish (Procambarus clarkii): Molecular and biochemical approaches.

Bisphenol S (BPS) is extensively utilized in various industries such as plastic manufacturing, food packaging, and electronics. The release of BPS into aquatic environments has been observed to have negative impacts on aquatic ecosystems. Research has shown that exposure to BPS can have adverse effects on the health of aquatic animals. This study aimed to explore the mechanism of oxidative stress and endoplasmic reticulum stress induced in freshwater crayfish (Procambarus clarkii) by exposure to BPS (0 µg/L, 1 µg/L, 10 µg/L, and 100 µg/L) for 14 days. The results showed that BPS exposure resulted in elevated levels of reactive oxygen species (ROS) and malondialdehyde (MDA) and severe intestinal histological damage. In addition, oxidative stress can occur in the body by inhibiting the activity of antioxidant enzymes and the expression of related genes. BPS exposure induced a significant increase in the relative mRNA expression levels of inflammatory cytokines (NF-κB and TNF-α) and key unfolded protein response (UPR) related genes (Bip, Ire1, and Xbp1). At the same time, BPS exposure also induced up-regulation of apoptosis genes (Cytc and Casp3), suggesting that UPR and Nrf2-Keap1 signaling pathways may play a protective role in the process of apoptosis and oxidative stress. In conclusion, Our findings present the initial evidence that exposure to environmentally relevant levels of BPS can lead to intestinal injury through various pathways, highlighting concerns about the potential harm at a population level from BPS and other bisphenol analogs.

Research on cooperative advertising strategies for dual channel supply chain of fresh agricultural products considering carbon reduction efficiency under retailer leadership.

With the development of low-carbon economy and the dominant position of retailers, through the establishment and comparison of three cooperative advertising models: model of supplier's independent advertising, model of supplier's independent advertising and model of retailer's and supplier's cooperative advertising, this paper studies the cooperative advertising decision-making of fresh agricultural products supply chain with two channels, and the demand of both channels is affected by the level of advertising investment, the proportion of advertising cost sharing and the efficiency of carbon emission reduction. The results show that when supplier and retailer adopt the two-way cooperative advertising mode, the demand and profit level of the two channels reach the optimal level. Numerical analysis shows that carbon emission reduction efficiency has an important impact on profits and market demand, which is closely related to cooperative advertising strategy.

A novel method for vegetable and fruit classification based on using diffusion maps and machine learning.

Vegetable and fruit classification can help all links of agricultural product circulation to better carry out inventory management, logistics planning and supply chain coordination, and improve the efficiency and response speed of the supply chain. However, the current classification of vegetables and fruits mainly relies on manual classification, which inevitably introduces the influence of human subjective factors, resulting in errors and misjudgments in the classification of vegetables and fruits. In response to this serious problem, this research proposes an efficient and reproducible novel model to classify multiple vegetables and fruits using handcrafted features. In the proposed model, preprocessing operations such as Gaussian filtering, grayscale and binarization are performed on the pictures of vegetables and fruits to improve the quality of the pictures; statistical texture features representing vegetable and fruit categories, wavelet transform features and shape features are extracted from the preprocessed images; the feature dimension reduction method of diffusion maps is used to reduce the redundant information of the combined features composed of statistical texture features, wavelet transform features and shape features; five effective machine learning methods were used to classify the types of vegetables and fruits. In this research, the proposed method was rigorously verified experimentally and the results show that the SVM classifier achieves 96.25% classification accuracy of vegetables and fruits, which proves that the proposed method is helpful to improve the quality and management level of vegetables and fruits, and provide strong support for agricultural production and supply chain.