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The article analyses the influencing factors of the anti-fouling properties of road traffic markings and researches a double-coated, anti-fouling road marking with good heat stability and high coating density. The anti-fouling performance of the road marking during its use period was experimentally analysed using SPSS software. The results indicate that the anti-fouling performance of the double-coated road marking is significantly better than that of thermoplastic markings and two-component markings, and it has a promising prospect for widespread application.

In the dynamic and rapid detection of retroreflection coefficient in road traffic markings, different factors may affect the detection results. This study investigates the significance of different levels of environmental light intensity and vehicle speed on the detection results of retroreflection coefficient in road markings using one-way ANOVA analysis. The results show that within a 95% confidence interval, there is no statistically significant difference in the detection results of retroreflection coefficient under different levels of environmental light intensity and vehicle speed. The variance effect size calculation indicates that for the detection of retroreflection coefficient in road markings, vehicle speed control is more important than light intensity. To obtain more reliable detection results, it is recommended to maintain a stable and uniform speed of no more than 80 km/h during the detection process.

Abstract Background Because a priori knowledge about function of G protein-coupled receptors (GPCRs) can provide useful information to pharmaceutical research, the determination of their function is a quite meaningful topic in protein science. However, with the rapid increase of GPCRs sequences entering into databanks, the gap between the number of known sequence and the number of known function is widening rapidly, and it is both time-consuming and expensive to determine their function based only on experimental techniques. Therefore, it is vitally significant to develop a computational method for quick and accurate classification of GPCRs. Results In this study, a novel three-layer predictor based on support vector machine (SVM) and feature selection is developed for predicting and classifying GPCRs directly from amino acid sequence data. The maximum relevance minimum redundancy (mRMR) is applied to pre-evaluate features with discriminative information while genetic algorithm (GA) is utilized to find the optimized feature subsets. SVM is used for the construction of classification models. The overall accuracy with three-layer predictor at levels of superfamily, family and subfamily are obtained by cross-validation test on two non-redundant dataset. The results are about 0.5% to 16% higher than those of GPCR-CA and GPCRPred. Conclusion The results with high success rates indicate that the proposed predictor is a useful automated tool in predicting GPCRs. GPCR-SVMFS, a corresponding executable program for GPCRs prediction and classification, can be acquired freely on request from the authors.

CRISPR‐based genomic‐imaging systems have been utilized for spatiotemporal imaging of the repetitive genomic loci in living cells, but they are still challenged by limited signal‐to‐noise ratio (SNR) at a non‐repetitive genomic locus. Here, an efficient genomic‐imaging system is proposed, termed CRISPR/Pepper‐tDeg, by engineering the CRISPR sgRNA scaffolds with the degron‐binding Pepper aptamers for binding fluorogenic proteins fused with Tat peptide derived degron domain (tDeg). The target‐dependent stability switches of both sgRNA and fluorogenic protein allow this system to image repetitive telomeres sensitively with a 5‐fold higher SNR than conventional CRISPR/MS2‐MCP system using "always‐on" fluorescent protein tag. Subsequently, CRISPR/Pepper‐tDeg is applied to simultaneously label and track two different genomic loci, telomeres and centromeres, in living cells by combining two systems. Given a further improved SNR by the split fluorescent protein design, CRISPR/Pepper‐tDeg system is extended to non‐repetitive sequence imaging using only one sgRNA with two aptamer insertions. Neither complex sgRNA design nor difficult plasmid construction is required, greatly reducing the technical barriers to define spatiotemporal organization and dynamics of both repetitive and non‐repetitive genomic loci in living cells, and thus demonstrating the large application potential of this genomic‐imaging system in biological research, clinical diagnosis and therapy. [ABSTRACT FROM AUTHOR]