FUR-DETR: A Lightweight Detection Model for Fixed-Wing UAV Recovery

Due to traditional recovery systems lacking visual perception, it is difficult to monitor UAVs’ real-time status in communication-constrained or GPS-denied environments. This leads to insufficient ability in decision-making and parameter adjustment and increase uncertainty and risk of recovery. Visual inspection technology can make up for the limitations of GPS and communication and improve the autonomy and adaptability of the system. However, the existing RT-DETR algorithm is limited by single-path feature extraction, a simplified fusion mechanism, and high-frequency information loss, which makes it difficult to balance detection accuracy and computational efficiency. Therefore, this paper proposes a lightweight visual detection model based on transformer architecture to further optimize computational efficiency. Firstly, aiming at the performance bottleneck of existing models, the Parallel Backbone is proposed, which captures local features and global semantic information by sharing the initial feature extraction module and the double-branch structure, respectively, and uses the progressive fusion mechanism to realize the adaptive integration of multiscale features so as to balance the accuracy and lightness of target detection. Secondly, an adaptive multiscale feature pyramid network (AMFPN) is designed, which effectively integrates different scales of information through multi-level feature fusion and information transmission mechanism, alleviates the problem of information loss in small-target detection, and improves the detection accuracy in complex backgrounds. Finally, a wavelet frequency–domain-optimized reverse feature fusion mechanism (WT-FORM) is proposed. By using the wavelet transform to decompose the shallow features into multi-frequency bands and combining the weighted calculation and feature compensation strategy, the computational complexity is reduced, and the representation ability of the global context is further enhanced. The experimental results show that the improved model reduces the parameter size and computational load by 43.2% and 58% while maintaining detection accuracy comparable to the original RT-DETR in three datasets. Even in complex environments with low light, occlusion, or small targets, it can provide more accurate detection results

Identification and Evaluation of Microplastics from Tea Filter Bags Based on Raman Imaging

Microplastic (MP) contamination is a public issue for the environment and for human health. Plastic-based food filter bags, including polyethylene terephthalate, polypropylene, nylon 6 (NY6), and polyethylene, are widely used for soft drink sub-packaging, increasing the risk of MPs in foods and the environment. Three types of commercially available filter bags, including non-woven and woven bags, were collected, and MPs released after soaking were mapped using Raman imaging combined with chemometrics. Compared with peak area imaging at a single characteristic peak, Raman imaging combined with direct classical least squares calculation was more efficient and reliable for identifying MP features. Up to 94% of the bags released MPs after soaking, and there was no significant correlation with soaking conditions. Most MPs were tiny fragments and particles, and a few were fibrous MPs 620–840 μm in size. Woven NY6 filter bags had the lowest risk of releasing MPs. Source exploration revealed that most MPs originated from fragments and particles adsorbed on the surface of bags and strings. The results of this study are applicable to filter bag risk assessment and provide scientific guidance for regulating MPs in food

N6-methyladenosine modification contributes to respiratory syncytial virus infection

Background: Respiratory syncytial virus (RSV) is the second leading cause of death due to lower respiratory tract infections. Effective prevention and treatment measures are lacking, posing a huge socioeconomic burden to the world. N6-methyladenosine (m6A) is the most common internal modification in messenger RNA and noncoding RNA. Numerous recent studies have shown that the dysregulation of m6A modification is associated with diseases caused by pathogenic viruses. Methods: The changes in m6A modification were evaluated using m6A RNA methylation assay. The differences in gene expression levels of various m6A-modifying enzymes were observed using Quantitative Real-time PCR (qRT-PCR) during RSV infection. The autophagosomes were observed using transmission electron microscopy, and the expression of autophagy-associated protein Microtubule Associated Protein 1 Light Chain 3 Beta Ⅱ/Ⅰ (LC3B Ⅱ/Ⅰ) and Beclin1 in Human Normal Lung Epithelial Cells (BEAS-2B) cells using Western blot during RSV infection. The significantly differentially expressed genes were screened guided by bioinformatics. Their relationship with m6A-modifying enzymes was analyzed through protein–protein interaction network and expression correlation analysis. Results: The m6A abundance decreased and demethylase Fat Mass and Obesity- Associated Protein (FTO) significantly increased during RSV infection after 24 h. We also found that the DNA Damage-Inducible Transcript 3 Protein (DDIT3) level significantly increased during RSV infection after 24 h and observed autophagosomes in BEAS-2B cells. In addition, RSV infection could cause the upregulation of LC3B Ⅱ/Ⅰ and Beclin1. The expression correlation analysis showed that DDIT3 levels were positively correlated with the FTO level, and Methyltransferase Like 3 (METTL3), RNA Binding Motif Protein 15B (RBM15B), YTH Domain-Containing Family Protein 1 (YTHDF1), and levels were negatively correlated with the DDIT3 level. Conclusions: We uncovered a significant role for m6A modification during RSV infection. Also, a correlation was found between m6A and autophagy, providing new ideas for therapeutic advancements in RSV treatment

Needle−tip effect promoted flexible electrochemical sensor for detecting chloride ions in food by in−situ deposited silver dendrimers

Salt plays a crucial role in food processing and consumption, and the rapid detection of chloride ions in food and feed has great significance for practical applications. In this work, Ag−based nanomaterials were deposited on the surface of a flexible integrated electrochemical sensor for the detection of Cl− in food. In order to enhance the detection performance, a unique needle−tip structure was formed by manipulating the electro−engraving process during the electrodeposition growth. Theoretical calculations and electrochemical investigations have demonstrated that the dendrimer’s rich tip structure significantly enhanced its electrochemical performance. A sensitive and flexible integrated electrochemical sensor was creatively developed for the detection of Cl− using needle−tip effect−promoted Ag micro dendrimers. The sensor achieved quantitative detection of Cl− over a dynamic range of 10.0 μM–100.0 mM, with a low limit of detection of 0.148 μM. The flexible electrochemical sensor proposed in this work exhibited good repeatability, selectivity and recoveries in real food samples