ABSTRACT
Traffic lights play vital roles in urban traffic management systems, providing clear directional guidance for vehicles and pedestrians while ensuring traffic safety. However, the vast quantity of traffic lights widely distributed in the transportation system aggravates energy consumption. Here, a self-powered traffic light system is proposed through wind energy harvesting based on a high-performance fur-brush dish triboelectric nanogenerator (FD-TENG). The FD-TENG harvests wind energy to power the traffic light system continuously without needing an external power supply. Natural rabbit furs are applied to dish structures, due to their outstanding characteristics of shallow wear, high performance, and resistance to humidity. Also, the grid pattern of the dish structure significantly impacts the TENG outputs. Additionally, the internal electric field and the influences of mechanical and structural parameters on the outputs are analyzed by finite element simulations. After optimization, the FD-TENG can achieve a peak power density of 3.275 W m-3. The portable and miniature features of FD-TENG make it suitable for other natural environment systems such as forests, oceans, and mountains, besides the traffic light systems. This study presents a viable strategy for self-powered traffic lights, establishing a basis for efficient environmental energy harvesting toward big data and Internet of Things applications.
ABSTRACT
G-quadruplex (G4), a non-classical deoxyribonucleic acid structure, is widely distributed in the genome and involved in various biological processes. In vivo, high-throughput sequencing has indicated that G4s are significantly enriched at functional regions in a cell-type-specific manner. Therefore, the prediction of G4s based on computational methods is necessary instead of the time-consuming and laborious experimental methods. Recently, G4 CUT&Tag has been developed to generate higher-resolution sequencing data than ChIP-seq, which provides more accurate training samples for model construction. In this paper, we present a new dataset construction method based on G4 CUT&Tag sequencing data and an XGBoost prediction model based on the machine learning boost method. The results show that our model performs well within and across cell types. Furthermore, sequence analysis indicates that the formation of G4 structure is greatly affected by the flanking sequences, and the GC content of the G4 flanking sequences is higher than non-G4. Moreover, we also identified G4 motifs in the high-resolution dataset, among which we found several motifs for known transcription factors (TFs), such as SP2 and BPC. These TFs may directly or indirectly affect the formation of the G4 structure.
ABSTRACT
An understanding of DNA-binding proteins is helpful in exploring the role that proteins play in cell biology. Furthermore, the prediction of DNA-binding proteins is essential for the chemical modification and structural composition of DNA, and is of great importance in protein functional analysis and drug design. In recent years, DNA-binding protein prediction has typically used machine learning-based methods. The prediction accuracy of various classifiers has improved considerably, but researchers continue to spend time and effort on improving prediction performance. In this paper, we combine protein sequence evolutionary information with a classification method based on kernel sparse representation for the prediction of DNA-binding proteins, and based on the field of machine learning, a model for the identification of DNA-binding proteins by sequence information was finally proposed. Based on the confirmation of the final experimental results, we achieved good prediction accuracy on both the PDB1075 and PDB186 datasets. Our training result for cross-validation on PDB1075 was 81.37%, and our independent test result on PDB186 was 83.9%, both of which outperformed the other methods to some extent. Therefore, the proposed method in this paper is proven to be effective and feasible for predicting DNA-binding proteins.
