In situ mass spectrometry imaging reveals pesticide residues and key metabolic pathways throughout the entire cowpea growth process.

Cowpea plants, renowned for their high edibility, pose a significant risk of pesticide residue contamination. Elucidating the behavior of pesticide residues and their key metabolic pathways is critical for ensuring cowpea safety and human health. This study investigated the migration of pesticide residues and their key metabolic pathways in pods throughout the growth process of cowpea plants via in situ mass spectrometry. To this end, four pesticides--including systemic (thiram), and nonsystemic (fluopyram, pyriproxyfen, and cyromazine) pesticides--were selected. The results indicate the direct upward and downward transmission of pesticides in cowpea stems and pods. Systemic pesticides gradually migrate to the core of cowpea plants, whereas nonsystemic pesticides remain on the surface of cowpea peels. The migration rate is influenced by the cowpea maturity, logarithmic octanol-water partition coefficient (log Kow) value, and molecular weight of the pesticide. Further, 20 types of key metabolites related to glycolysis, tricarboxylic acid cycle, and flavonoid synthesis were found in cowpea pods after pesticide treatment. These findings afford insights into improving cowpea quality and ensuring the safe use of pesticides.

Emerging Roles of Lysophosphatidic Acid in Macrophages and Inflammatory Diseases.

Lysophosphatidic acid (LPA) is a bioactive phospholipid that regulates physiological and pathological processes in numerous cell biological functions, including cell migration, apoptosis, and proliferation. Macrophages are found in most human tissues and have multiple physiological and pathological functions. There is growing evidence that LPA signaling plays a significant role in the physiological function of macrophages and accelerates the development of diseases caused by macrophage dysfunction and inflammation, such as inflammation-related diseases, cancer, atherosclerosis, and fibrosis. In this review, we summarize the roles of LPA in macrophages, analyze numerous macrophage- and inflammation-associated diseases triggered by LPA, and discuss LPA-targeting therapeutic strategies.

Distribution characteristics of microplastics and corresponding feeding habits of the dominant shrimps in the rivers of Chongming Island.

There is increasing global concern regarding microplastics. Rivers play a key role in the transportation and storage of microplastics on the Earth's surface. Here, we aimed to investigate the spatial-temporal variations in the distribution of microplastics in water as well as in the dominant species of the macrobenthic fauna Exopalaemon modestus and Macrobrachium nipponense in the river system of Chongming Island by setting up 16 fixed sampling sites. We found that the abundance of microplastics in the water of the rivers on the Chongming Island was 0.48 ± 0.10 n/L. There was no significant difference among different reaches. The abundance of microplastics in the major rivers was significantly higher in summer than in the other seasons. Detection rates of microplastics in Exopalaemon modestus and Macrobrachium nipponense were 50.12 % and 64.58 %, with mean abundances of 1.92 ± 0.52 n/g and 1.49 ± 0.30 n/g, respectively. The composition characteristics of the microplastics in shrimps were affected by the microplastics in the aquatic environment. The microplastic content in the shrimps and water were linearly correlated in terms of the same characteristics (shape, color, and polymer). Shrimps showed a stronger feeding preference {Target Group Index (TGI) > 1} for microplastics with fibrous shapes, transparent and green colors, rayon (RA) and polyethylene (PE) polymers, and relatively small sizes (<400 µm). These results indicate that shrimps prefer to consume microplastics that have similar appearance to their prey. Their benthic dwelling habits may limit their feeding space to the bottom of the water, which in turn leads to an increase in the feeding probability on microplastics of greater densities (e.g., RA). The catabolism of microplastics in shrimps may lead to an overestimation of their feeding preference for smaller sizes. Further controlled experiments should be carried out to obtain deeper insights into the preferences of shrimp for microplastics.

Improving text mining in plant health domain with GAN and/or pre-trained language model.

The Bidirectional Encoder Representations from Transformers (BERT) architecture offers a cutting-edge approach to Natural Language Processing. It involves two steps: 1) pre-training a language model to extract contextualized features and 2) fine-tuning for specific downstream tasks. Although pre-trained language models (PLMs) have been successful in various text-mining applications, challenges remain, particularly in areas with limited labeled data such as plant health hazard detection from individuals' observations. To address this challenge, we propose to combine GAN-BERT, a model that extends the fine-tuning process with unlabeled data through a Generative Adversarial Network (GAN), with ChouBERT, a domain-specific PLM. Our results show that GAN-BERT outperforms traditional fine-tuning in multiple text classification tasks. In this paper, we examine the impact of further pre-training on the GAN-BERT model. We experiment with different hyper parameters to determine the best combination of models and fine-tuning parameters. Our findings suggest that the combination of GAN and ChouBERT can enhance the generalizability of the text classifier but may also lead to increased instability during training. Finally, we provide recommendations to mitigate these instabilities.