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As people’s consumption levels and health awareness have risen, the consumer demand for functional foods is steadily increasing. Medium-chain fatty acids (MCFAs), an important class of dietary fatty acids, have been regarded as high-value functional foods due to their unique transportation system and the fact that they can be metabolized rapidly in the body while producing high energy. Beginning with an overview of the types and structures of MCFAs, this paper goes on to elaborate the metabolic pathway of MCFAs in the human body and then summarize recent research on MCFAs in the adjuvant treatment of different diseases. This article focuses on the positive role of MCFAs in the adjuvant treatment of Alzheimer’s disease, epilepsy, cancer, diabetes, obesity, and other diseases, and expounds their action mechanism. Finally, an outlook on the future development of MCFAs is given. Overall, this review provides a theoretical basis for promoting the application of MCFAs in the fields of food and medicine.

Abstract The objective of this work was to establish and validate the dry matter distribution and yield prediction models based on physiological developmental timing, to compare the differences between the dry mass distribution index model and the dry mass distribution coefficient model, for the simulation of ear dry mass and to improve the accuracy of maize growth models for predicting yield. The experiments were conducted in three tropical sites (Longchuan, Mangshi, and Ruili) in the tropical region of Yunnan Province, China. The NRMS of ear dry mass and yield were generally less than 10. The dry mass distribution index method (NRMS = 5.44% and RMSE = 807.22 kg ha-1 for ear dry mass; and NRMS = 7.32% and RMSE = 707.67 kg ha-1 for grain yield) is better than the dry mass distribution coefficient method (NRMS = 7.52% and RMSE = 1115.31 kg ha-1 for ear dry mass; NRMS = 8.6% and RMSE = 830.76 kgha-1 for grain yield) to simulate maize ear dry mass and grain yield. The distribution index model improves the accuracy of the model, which is valuable for future maize production and management in Yunnan.

DABMA is a chemical molecule optimized from the parent compound ABMA and exhibits broad‐spectrum antipathogenic activity by modulating the host's endolysosomal and autophagic pathways. Both DABMA and ABMA inhibit severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in a cellular assay, which further expands their anti‐pathogen spectrum in vitro. However, their precise mechanism of action has not yet been resolved. TMEM175 is a newly characterized endolysosomal channel which plays an essential role in the homeostasis of endosomes and lysosomes as well as organelle fusion. Here, we show that DABMA increases the endosomal TMEM175 current through organelle patch clamping with an EC50 of 17.9 μm. Depletion of TMEM175 protein significantly decreases the antitoxin activity of DABMA and affects its action on acidic‐ and Rab7‐positive endosomes as well as on endolysosomal trafficking. Thus, TMEM175 is necessary for DABMA's activity and may represent a druggable target for the development of anti‐infective drugs. Moreover, DABMA, as an activator of the TMEM175 channel, may be useful for the in‐depth characterization of the physiological and pathological roles of this endolysosomal channel. [ABSTRACT FROM AUTHOR]

Chlorophyll content is an important physiological indicator reflecting the growth status of crops. Traditional methods for obtaining crop chlorophyll content are time-consuming and labor-intensive. The rapid development of UAV remote sensing platforms offers new possibilities for monitoring chlorophyll content in field crops. To improve the efficiency and accuracy of monitoring chlorophyll content in maize canopies, this study collected RGB, multispectral (MS), and SPAD data from maize canopies at the jointing, tasseling, and grouting stages, constructing a dataset with fused features. We developed maize canopy chlorophyll content monitoring models based on four machine learning algorithms: BP neural network (BP), multilayer perceptron (MLP), support vector regression (SVR), and gradient boosting decision tree (GBDT). The results showed that, compared to single-feature methods, the MS and RGB fused feature method achieved higher monitoring accuracy, with R² values ranging from 0.808 to 0.896, RMSE values between 2.699 and 3.092, and NRMSE values between 10.36% and 12.26%. The SVR model combined with MS–RGB fused feature data outperformed the BP, MLP, and GBDT models in monitoring maize canopy chlorophyll content, achieving an R² of 0.896, an RMSE of 2.746, and an NRMSE of 10.36%. In summary, this study demonstrates that by using the MS–RGB fused feature method and the SVR model, the accuracy of chlorophyll content monitoring can be effectively improved. This approach reduces the need for traditional methods of measuring chlorophyll content in maize canopies and facilitates real-time management of maize crop nutrition. [ABSTRACT FROM AUTHOR]