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Three kinds of silicon dioxide including S655, R92 and R40F were characterized, and their application in low-temperature adsorption refining of fragrant rapeseed oil was studied. The results showed that all three kinds of silicon dioxide had a loose porous structure, but the particle size distribution, specific surface area, pore volume and pore size were obviously different. After being treated with 1.0% silicon dioxide at 45 ℃ for 30 min, the dephosphorization rates of rapeseed oil by S655, R92 and R40F were 90.3%, 99.6% and 88.5%, the lightness increased by 49.9%, 52.5% and 53.9%, the redness decreased by 31.5%, 23.4% and 37.8%, the yellowness increased by 17.3%, 20.0% and 20.6%, respectively. Meanwhile, the retention rate of sterols (above 97.1%) was high, and there were no significant differences in deacidification rate, fatty acid composition or tocopherol retention rate among the three treatment groups. The highest retention rates of total phenol and canolol (98.1% and 99.4%) were found in the samples treated with R92. The contents of glucosinolate degradation products, pyrazines, aldehydes and ketones in fragrant rapeseed oil all increased significantly after refining with the three kinds of silicon dioxide.

The major route of rapeseed utilization is by the preparation of rapeseed oil. Different oil preparation technologies have a significant influence on lipid concomitants (including polyphenols, phytosterols, tocopherols, and β-carotene) in rapeseed oil. Lipid concomitants are an important part in rapeseed oil quality, so it is crucial to understand the changes of lipid concomitants during rapeseed oil production. The aim of this review is to summarize the major outcomes of recent research on the influence of different oil preparation technologies on lipid concomitants in rapeseed oil with a focus on pretreatment technologies (including pulsed electric field, microwave, and roasting/steaming), oil extraction technologies (including supercritical carbon dioxide extraction, aqueous enzymatic extraction, and solvent extraction) and refining processes (including degumming, deacidification, decolorization, and deodorization). We hope that this review will provide a theoretical basis for the development of green and efficient technology for rapeseed processing.

The metabolic enzyme methionine adenosyltransferase 2A (MAT2A) was found to elicit synthetic lethality in methylthioadenosine phosphorylase (MTAP)‐deleted cancers, which occur in about 15% of all cancers. Here, we described a novel MAT2A inhibitor, SCR‐7952 with potent and selective antitumor effects on MTAP‐deleted cancers in both in vitro and in vivo. The cryo‐EM data indicated the high binding affinity and the allosteric binding site of SCR‐7952 on MAT2A. Different from AG‐270, SCR‐7952 exhibited little influence on metabolic enzymes and did not increase the plasma levels of bilirubin. A systematic evaluation of combination between SCR‐7952 and different types of protein arginine methyltransferase 5 (PRMT5) inhibitors indicated remarkable synergistic interactions between SCR‐7952 and the S‐adenosylmethionine‐competitive or the methylthioadenosine‐cooperative PRMT5 inhibitors, but not substrate‐competitive ones. The mechanism was via the aggravated inhibition of PRMT5 and FANCA splicing perturbations. These results indicated that SCR‐7952 could be a potential therapeutic candidate for the treatment of MTAP‐deleted cancers, both monotherapy and in combination with PRMT5 inhibitors. [ABSTRACT FROM AUTHOR]

Purpose: Substantial evidence has established a strong association between non-alcoholic fatty liver disease (NAFLD), type 2 diabetes mellitus (T2DM) and insulin resistance (IR). Insulin resistance metabolic score (METS-IR) is a new and more effective comprehensive indicator for measuring IR. Our aim was to investigate the relationship between METS-IR and NAFLD in T2DM population. Patients and methods: This cross-sectional study included 1097 adult patients with T2DM. Anthropometric measurements and biochemical indicators were collected, and the NAFLD was diagnosed by ultrasound. The METS-IR was calculated. Based on the presence of NAFLD, the population was divided into non-NAFLD and NAFLD groups. The relationship between METS-IR and NAFLD was evaluated. Results: Compared with the non-NAFLD group, the METS-IR was higher in the NAFLD group (P < 0.001). The incidence rate of NAFLD increased across the quartiles of the METS-IR (P < 0.001). Spearman correlation analysis showed that METS-IR was positively correlated with NAFLD (Correlation Coefficient: 0.441, P < 0.001). The binary logistic regression analysis indicated that METS-IR was independently associated with NAFLD (OR: 1.120, 95% CI 1.080– 1.161). Furthermore, the area under the receiver operating characteristic curve of the METS-IR was 0.781 (95% CI 0.746– 0.817) and relatively higher than other evaluation variables. Conclusion: In patients with T2DM, METS-IR is closely associated with NAFLD, and might be a valuable predictor of NAFLD. Further research is needed to verify this association. [ABSTRACT FROM AUTHOR]

Forest fire danger prediction models often face challenges due to spatial and temporal limitations, as well as a lack of universality caused by regional inconsistencies in fire danger features. To address these issues, we propose a novel algorithm, squeeze-excitation spatial multi-scale transformer learning (SESMTML), which is designed to extract multi-scale fire danger features from remote sensing images. SESMTML includes several key modules: the multi-scale deep feature extraction module (MSDFEM) captures global visual and multi-scale convolutional features, the multi-scale fire danger perception module (MFDPM) explores contextual relationships, the multi-scale information aggregation module (MIAM) aggregates correlations of multi-level fire danger features, and the fire danger level fusion module (FDLFM) integrates the contributions of global and multi-level features for predicting forest fire danger. Experimental results demonstrate the model's significant superiority, achieving an accuracy of 83.18%, representing a 22.58% improvement over previous models and outperforming many widely used deep learning methods. Additionally, a detailed forest fire danger prediction map was generated using a test study area at the junction of the Miyun and Pinggu districts in Beijing, further confirming the model's effectiveness. SESMTML shows strong potential for practical application in forest fire danger prediction and offers new insights for future research utilizing remote sensing images. [ABSTRACT FROM AUTHOR]

Summary:In plants, the targets of deeply conserved microRNAs (miRNAs) were comprehensively studied. Evidence is emerging that targets of less conserved miRNAs, endogenous target mimics (eTM) and non-canonical targets play functional roles. Existing plant miRNA prediction tools lack a cross-species conservation filter and eTM prediction function. We developed a tool named TarHunter that features a strict cross-species conservation filter and capability of predicting eTMs. TarHunter has higher recall or precision rate as compared with other tools, and the conservation filter effectively increases prediction precision. TarHunter prediction combined with degradome analysis uncovered previously neglected miRNA targets including non-canonical target sites from various plant species, which are available at the TarHunter website (http://tarhunter.genetics.ac.cn/). Availability and implementation:The code of TarHunter is available on Github (https://github.com/XMaBio). Contact:xuemei.chen@ucr.edu. Supplementary information:Supplementary data are available at Bioinformatics online.