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Abstract Selenium, a crucial antioxidant in the body, has been linked to all-cause and cause-specific mortality. However, the relationship between selenium and mortality in the general population remains unclear. A total of 5449 participants in the National Health and Nutrition Examination Survey (NHANES) (2003–2004, 2011–2016) were analyzed to track participant mortality until December 31, 2019. The COX proportional hazard model, Kaplan‒Meier survival analysis and restricted cubic spline regression analysis were used to investigate the associations. Subgroup analysis was conducted on the basis of age (≤ 60, > 60), sex (male, female), and smoking status (nonsmoker, former smoker, and current smoker). The second quartile was associated with lower all-cause mortality and noncardiovascular mortality (HR and 95% CI 0.61,0.45–0.83;0.59,0.42–0.83, respectively). The third quartile was associated with lower cardiovascular-related mortality (HR and 95% CI 0.49, 0.32–0.76). Elevated serum selenium concentrations were associated with lower all-cause mortality, noncardiovascular mortality (range ≤ 129.82 μg/L), and cardiovascular mortality (range ≤ 129.08 μg/L). Subgroup analysis revealed a positive correlation between the serum selenium concentration (range ≥ 129.82 μg/L) and all-cause mortality among the subgroup of current smokers (p

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Alterations in the human glycome have been associated with cancer and autoimmunity. Thus, constructing a site-specific map of the human glycome for biomarker research and discovery has been a highly sought-after objective. However, due to analytical barriers, comprehensive site-specific glycoprofiling is difficult to perform. To develop a platform to detect easily quantifiable, site-specific, disease-associated glycan alterations for clinical applications, we have adapted the multiple reaction monitoring mass spectrometry method for use in glycan biomarker research. The adaptations allow for highly precise site-specific glycan monitoring with minimum sample prep. Using this technique, we successfully mapped out the relative abundances of the most common 159 glycopeptides in the plasma of 97 healthy volunteers. This plasma glycome map revealed 796 significant (FDR

Abstract Road damage detection is an important task to ensure road safety and realize the timely repair of road damage. The previous manual detection methods are low in efficiency and high in cost. To solve this problem, an improved YOLOv5 road damage detection algorithm, MN-YOLOv5, was proposed. We optimized the YOLOv5s model and chose a new backbone feature extraction network MobileNetV3 to replace the basic network of YOLOv5, which greatly reduced the number of parameters and GFLOPs of the model, and reduced the size of the model. At the same time, the coordinate attention lightweight attention module is introduced to help the network locate the target more accurately and improve the target detection accuracy. The KMeans clustering algorithm is used to filter the prior frame to make it more suitable for the dataset and to improve the detection accuracy. To improve the generalization ability of the model, a label smoothing algorithm is introduced. In addition, the structure reparameterization method is used to accelerate model reasoning. The experimental results show that the improved YOLOv5 model proposed in this paper can effectively identify pavement cracks. Compared with the original model, the mAP increased by 2.5%, the F1 score increased by 2.6%, and the model volume was smaller than that of YOLOv5. 1.62 times, the parameter was reduced by 1.66 times, and the GFLOPs were reduced by 1.69 times. This method can provide a reference for the automatic detection method of pavement cracks.

Glycosphingolipids (GSLs) are found in cellular membranes of most organisms and play important roles in cell-cell recognition, signaling, growth, and adhesion, among others. A method based on nanoflow high performance liquid chromatography-chip-quadrupole-time-of-flight mass spectrometry (nanoHPLC Chip-Q-TOF MS) was applied towards identifying and quantifying intact GSLs from a variety of samples, including cultured cell lines and animal tissue. The method provides the composition and sequence of the glycan, as well as variations in the ceramide portion of the GSL. It was used to profile the changes in the glycolipidome of Caco-2 cells as they undergo differentiation. A total of 226 unique GSLs were found among Caco-2 samples from five differentiation time-points. The method provided a comprehensive glycolipidomic profile of a cell during differentiation to yield the dynamic variation of intact GSL structures.

Abstract Deep learning networks have been successfully applied to transfer functions so that the models can be adapted from the source domain to different target domains. This study uses multiple convolutional neural networks to decode the electroencephalogram (EEG) of stroke patients to design effective motor imagery (MI) brain-computer interface (BCI) system. This study has introduced ‘fine-tune’ to transfer model parameters and reduced training time. The performance of the proposed framework is evaluated by the abilities of the models for two-class MI recognition. The results show that the best framework is the combination of the EEGNet and ‘fine-tune’ transferred model. The average classification accuracy of the proposed model for 11 subjects is 66.36%, and the algorithm complexity is much lower than other models.These good performance indicate that the EEGNet model has great potential for MI stroke rehabilitation based on BCI system. It also successfully demonstrated the efficiency of transfer learning for improving the performance of EEG-based stroke rehabilitation for the BCI system.