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In complex agricultural settings, accurately and rapidly identifying the growth and health conditions of strawberries remains a formidable challenge. Therefore, this study aims to develop a deep framework, Disease-YOLO (D-YOLO), based on the YOLOv8s model to monitor the health status of strawberries. Key innovations include (1) replacing the original backbone with MobileNetv3 to optimize computational efficiency; (2) implementing a Bidirectional Feature Pyramid Network for enhanced multi-scale feature fusion; (3) integrating Contextual Transformer attention modules in the neck network to improve lesion localization; and (4) adopting weighted intersection over union loss to address class imbalance. Evaluated on our custom strawberry disease dataset containing 1301 annotated images across three fruit development stages and five plant health states, D-YOLO achieved 89.6% mAP on the train set and 90.5% mAP on the test set while reducing parameters by 72.0% and floating-point operations by 75.1% compared to baseline YOLOv8s. The framework’s balanced performance and computational efficiency surpass conventional models including Faster R-CNN, RetinaNet, YOLOv5s, YOLOv6s, and YOLOv8s in comparative trials. Cross-domain validation on a maize disease dataset demonstrated D-YOLO’s superior generalization with 94.5% mAP, outperforming YOLOv8 by 0.6%. The framework’s balanced performance (89.6% training mAP) and computational efficiency surpass conventional models, including Faster R-CNN, RetinaNet, YOLOv5s, YOLOv6s, and YOLOv8s, in comparative trials. This lightweight solution enables precise, real-time crop health monitoring. The proposed architectural improvements provide a practical paradigm for intelligent disease detection in precision agriculture.

Co3O4 nanoparticles-assembled microrods (Mic-Co3O4) were successfully synthesized with the precursor of Co-BTC (BTC = 1,3,5-benzenetricarboxylic acid) and applied for efficient propane (C3H8) oxidation. It shows a higher reaction rate of 4.14 μmolC3H8 gcat−1 s−1 at 250 °C, when it is only 1.18 μmolC3H8 gcat−1 s−1 obtained over Co3O4 nanoparticles (Np-Co3O4) via direct calcination of cobalt nitrate. Moreover, Mic-Co3O4 remains the original morphology of Co-BTC MOF, and the keeping pores enhance the microrod rigidity, hindering nanoparticles growth and thus resulting in superior thermal stability. After 12 h of durability test at 500 °C, the size of Mic-Co3O4 nanoparticles increases slightly from 62 to 70 nm, whereas it is from 97 to 130 nm for Np-Co3O4. Meanwhile, the calcination of Co-BTC precursor can induce large amounts of surface Co2+, favoring activation of adsorptive oxygen species. This can promote oxygen mobility, which is helpful for total propane oxidation.

Metaproteomics can provide valuable insights into the functions of human gut microbiota (GM), but is challenging due to the extreme complexity and heterogeneity of GM. Data-independent acquisition (DIA) mass spectrometry (MS) has been an emerging quantitative technique in conventional proteomics, but is still at the early stage of development in the field of metaproteomics. Herein, we applied library-free DIA (directDIA)-based metaproteomics and compared the directDIA with other MS-based quantification techniques for metaproteomics on simulated microbial communities and feces samples spiked with bacteria with known ratios, demonstrating the superior performance of directDIA by a comprehensive consideration of proteome coverage in identification as well as accuracy and precision in quantification. We characterized human GM in two cohorts of clinical fecal samples of pancreatic cancer (PC) and mild cognitive impairment (MCI). About 70,000 microbial proteins were quantified in each cohort and annotated to profile the taxonomic and functional characteristics of GM in different diseases. Our work demonstrated the utility of directDIA in quantitative metaproteomics for investigating intestinal microbiota and its related disease pathogenesis.

Ulcerative colitis (UC) is a chronic, relapsing inflammatory bowel disease. Despite advances in current therapies, safer, more effective drugs are urgently needed. Traditional Chinese herbal formula Gegen Qinlian Decoction (GQD) has been used for gastrointestinal disorders, including UC, though its exact mechanisms require further clarification. This study aimed to systematically evaluate the therapeutic effects of GQD in UC mice, focusing on serum metabolomics, gut microbiota, and immunomodulatory mechanisms. A dextran sulfate sodium (DSS)-induced mouse model of UC was established. Serum metabolomics and 16S rRNA sequencing analysis of GQD's effects on metabolites and gut microbiota. Correlation analysis and network pharmacology identified potential targets and pathways of GQD. Immunofluorescence detected the expression of γδT cells, TNF-α, IFN-γ, and IL-17 proteins in the colonic tissue. Using UPLC-QE-Orbitrap-MS, 71 compounds were identified in the GQD quality control analysis. GQD markedly attenuated colonic histopathological damage and suppressed serum pro-inflammatory cytokines IFN-γ, IL-17, and TNF-α. It also modulated key serum metabolites, including succinic acid, glyoxylate, and xanthine, which are primarily involved in amino acid and purine metabolic pathways. GQD further influenced intestinal microbial diversity and composition. Joint analysis revealed GQD modulates gut microbiota, serum amino acid and purine metabolism, and inflammation pathways. Immunohistochemical results demonstrated enhanced infiltration of γδT cells following GQD treatment, accompanied by reduced protein expression levels of TNF-α, IFN-γ, and IL-17. GQD exerts therapeutic effects on UC by reshaping gut microbiota composition and metabolic activities, thereby ameliorating intestinal mucosal injury, regulating γδT cell-mediated immune responses, and influencing amino acid and purine metabolic pathways.

Targeting dendritic cells (DCs) metabolism-related pathways and in-situ activation of DCs have become a new trend in DC-based immunotherapy. Studies have shown that Lycium barbarum polysaccharide can promote DCs function. This study is aimed at exploring the mechanism of LBP affecting DCs function from the perspective of metabolomics. MTT method was used to detect the activity of DC2.4 cells. ELISA kit method was used to detect the contents of IL-6, IL-12, and TNF-α in the supernatant of cells. Ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS) was used to detect general changes in DC2.4 cell metabolism. And then multidistance covariates and bioinformatics, partial least squares-discriminant analysis (PLS-DA) were used to analyze differential metabolites. Finally, metabolic pathway analysis was performed by MetaboAnalyst v5.0. The results showed that LBP had no significant inhibitory effect on the activity of DC2.4 cells at the experimental dose of 50-200 μg/ml. LBP (100 μg/ml) could significantly stimulate DC2.4 cells to secrete IL-6, TNF-α, and IL-12. Moreover, 20 differential metabolites could be identified, including betaine, hypoxanthine, L-carnitine, 5’-methylthioadenosine, orotic acid, sphingomyelin, and L-glutamine. These metabolites were involved 28 metabolic pathways and the top 5 metabolic pathways were aspartate metabolism, pyrimidine metabolism, phenylacetate metabolism, methionine metabolism, and fatty acid metabolism. These results suggest that the effect of LBP on DCs function is related to the regulation of cell metabolism.

Background Microbiota are closely associated with human health and disease. Metaproteomics can provide a direct means to identify microbial proteins in microbiota for compositional and functional characterization. However, in-depth and accurate metaproteomics is still limited due to the extreme complexity and high diversity of microbiota samples. It is generally recommended to use metagenomic data from the same samples to construct the protein sequence database for metaproteomic data analysis. Although different metagenomics-based database construction strategies have been developed, an optimization of gene taxonomic annotation has not been reported, which, however, is extremely important for accurate metaproteomic analysis. Results Herein, we proposed an accurate taxonomic annotation pipeline for genes from metagenomic data, namely contigs directed gene annotation (ConDiGA), and used the method to build a protein sequence database for metaproteomic analysis. We compared our pipeline (ConDiGA or MD3) with two other popular annotation pipelines (MD1 and MD2). In MD1, genes were directly annotated against the whole bacterial genome database; in MD2, contigs were annotated against the whole bacterial genome database and the taxonomic information of contigs was assigned to the genes; in MD3, the most confident species from the contigs annotation results were taken as reference to annotate genes. Annotation tools, including BLAST, Kaiju, and Kraken2, were compared. Based on a synthetic microbial community of 12 species, it was found that Kaiju with the MD3 pipeline outperformed the others in the construction of protein sequence database from metagenomic data. Similar performance was also observed with a fecal sample, as well as in silico mixed datasets of the simulated microbial community and the fecal sample. Conclusions Overall, we developed an optimized pipeline for gene taxonomic annotation to construct protein sequence databases. Our study can tackle the current taxonomic annotation reliability problem in metagenomics-derived protein sequence database and can promote the in-depth metaproteomic analysis of microbiome. The unique metagenomic and metaproteomic datasets of the 12 bacterial species are publicly available as a standard benchmarking sample for evaluating various analysis pipelines. The code of ConDiGA is open access at GitHub for the analysis of microbiota samples. 6yn7yKvS5c7jYK96vohJFU Video Abstract

Vascular remodeling is a prominent pathological characteristic of cardiovascular diseases (CVDs), with angiotensin II (Ang II) known to induce vascular injury and remodeling by modulating the functions of vascular smooth muscle cells (SMCs). However, the precise mechanisms underlying this process remain incompletely understood. In this study, we established a primary human aortic SMC model and employed a multi‐omics approach—including untargeted proteomics, lipidomics, and metabolomics through mass spectrometry (MS)—to investigate the molecular alterations induced by Ang II. By integrating omics data from Ang II‐treated and control groups, we identified significantly altered molecules and pathways that reflect functional changes and cellular inflammation in SMCs. Subsequent pathway enrichment and interaction network analyses of proteomics and metabolomics datasets revealed critical pathways involved in Ang II‐induced vascular remodeling. Our findings highlight metabolic reprogramming and oxidative stress responses in SMCs following Ang II exposure, emphasizing their role in regulating vascular remodeling. These insights provide a comprehensive understanding of the molecular events driving vascular remodeling and open new avenues for targeted therapeutic interventions in CVDs. This study investigates the molecular alterations underlying angiotensin II (Ang II)‐induced vascular remodeling in cardiovascular diseases using a multi‐omics approach. Through data exploration and integrated multi‐omics analysis, our findings reveal that Ang II‐driven vascular injury in vascular smooth muscle cells is mediated by metabolic reprogramming and inflammatory responses. Notably, targeting glycolytic pathways and restoring redox homeostasis emerge as promising therapeutic strategies for alleviating vascular remodeling .

In this study, a column experiment was employed to evaluate the nuclide migration behavior in the surrounding rock medium of a near-surface disposal site in China and to investigate the advection–dispersion behavior of tritium (H-3) and plutonium-238 (Pu-238) in highly weathered argillaceous shale. A reasonable numerical model was selected to fit the experimental breakthrough curves (BTCs) and to obtain the relevant migration parameters. The results show the following: (1) the internal structure of the highly weathered argillaceous shale exhibited heterogeneity, and the nuclide migration BTC showed characteristics of a “curve peak moving forward” and a “tail curve trailing”; (2) compared with other models, the stream tube mode could better fit the BTCs and obtain the average dispersion coefficient , average distribution coefficient , and other parameters; (3) compared to the results of the batch experiment, the distribution coefficient Kd obtained from the column experiment was smaller than that obtained from the batch experiment, which is speculated to be due to the influence of contact time and the contact area between the nuclide and the medium.

Advection-dispersion experiments (ADE) were effectively designed for inadequate transport models through a calibration/validation process. HTO, selenium (Se), and cesium (Cs) transport in crushed granite were studied using a highly reliable, dynamic column device in order to obtain the retardation factors (R) and the dispersion coefficients (D) by fitting experimental breakthrough curves (BTCs) for various path lengths. In order to conduct a safety assessment (SA) of a deep geological repository for high-level radioactive waste, radionuclide transport in rock systems is necessary to clarify and establish a suitable model. A dynamic column with a radiotracer (HTO, Se(IV), and Cs) was applied to 2, 4, and 8 cm path lengths using a STANMOD simulation. The results showed similar results between the BTCs of Se and Cs by fitting a non-equilibrium sorption model due to the retardation effect. In fact, there was a relatively obvious sorption of Se and Cs in the BTCs obtained by fitting a retardation factor (R) value higher than 1. In addition, a two-region (physical) and a two-site (chemical) non-equilibrium model with either the lowest sum of squared residuals (SSQ) or the root mean square error (RMSE) were applied to determine the Se and Cs sorption mechanisms on granite.

Osthole (OST) is a simple coumarin derivative with pharmacological effects in many types of cancer cells. However, its role and its mechanism of action in breast cancer 4T1 cells remain unclear. In this study, we explored the effects and potential mechanisms of action of OST in 4T1 cells. The MTT, PI, and Annexin V-FITC/PI methods were used to evaluate the effects of OST-treated and untreated 4T1 cells on viability, cell cycle, and apoptosis, respectively. UPLC-Q-TOF/MS combined with multivariate data analysis was used to screen potential biomarkers relevant to the therapeutic mechanisms of OST. Additionally, mTOR, SREBP1, and FASN protein levels were detected using western blotting in OST-treated and untreated 4T1 cells. OST inhibited 4T1 cell proliferation, blocked the cells from remaining in S-phase, and induced apoptosis. In 4T1 cells, OST mainly affected the phospholipid biosynthesis, methyl histidine metabolism, pyrimidine metabolism, and β-oxidation of very long chain fatty acid pathways, suggesting that metabolic changes related to lipid metabolism-mediated signaling systems were the most influential pathways, possibly via inhibition of mTOR/SREBP1/FASN signaling. Our findings reveal biomarkers with potential therapeutic effects in breast cancer and provide insight into the therapeutic and metabolic mechanisms of OST in 4T1 cells.