γ-MSH (γ-melanocyte-stimulating hormone: H-Tyr-Val-Met-Gly-His-Phe-Arg-Trp-Asp-Arg-Phe-Gly-OH), with its exquisite specificity and potency, has recently created much excitement as a drug lead. However, this peptide like most peptides susceptible to proteolysis in vivo which potentially decreases its beneficial activities. In our continued effort to design a proteolytically stable with specific receptor binding ligand, we have engineered peptides by cyclizing γ-MSH using a thioether bridge. A number of novel cyclic truncated γ-MSH analogues were designed and synthesized, in which a thioether bridge was incorporated between a cysteine side chain and an N-terminal bromoacyl group. One of these peptides, cyclo-[(CH2)3CO-Gly1-His2-D-Phe3-Arg4-D-Trp5-Cys(S-)6]-Asp7-Arg8-Phe9-Gly10-NH2, demonstrated potent antagonist activity and receptor selectivity for the human melanocortin 1 receptor (hMC1R) (IC50 = 17 nM). This novel peptide is the most selective antagonist for the human hMC1R to date. Further pharmacological studies have shown that this peptide can specifically target melanoma cells. The NMR analysis of this peptide in a membrane–like environment revealed a new turn structure, specific to the hMC1R antagonist, at the C terminal, wherein the side chain and backbone conformation of D-Trp5 and Phe9 of the peptide are contributors to the hMC1R selectivity. Cyclization strategies represent an approach for stabilizing bioactive peptides while keeping their full potencies and should boost applications of peptide-based drugs in human medicine.

This study investigates the time-dependent mechanical properties of concrete deteriorated by the alkali-silica reaction (ASR). Previous analytical and experimental studies have indicated the positive impact of ASR gel in the cracks against mechanical damage in concrete. To study the effects of ASR gel on cracked concrete, groups of cylinder specimens with different expansion levels were prepared and tested at different material ages. The compression test results showed that the deteriorated elastic modulus of the specimens could be recovered over time. Mechanical property data from the other ASR studies were collected and assessed to observe similar trends across the literature. It was observed that the recovery of the elastic modulus also occurred in previously reported experiments. The recovery of the elastic modulus is assumed to be due to the time-dependent chemical and physical properties of ASR gel, which fills the cracks. Moreover, the data indicated that parameters other than material age and expansion could be attributed to the time-dependent mechanical properties of concrete affected by ASR.

The detection of road facilities or roadside structures is essential for high-definition (HD) maps and intelligent transportation systems (ITSs). With the rapid development of deep-learning algorithms in recent years, deep-learning-based object detection techniques have provided more accurate and efficient performance, and have become an essential tool for HD map reconstruction and advanced driver-assistance systems (ADASs). Therefore, the performance evaluation and comparison of the latest deep-learning algorithms in this field is indispensable. However, most existing works in this area limit their focus to the detection of individual targets, such as vehicles or pedestrians and traffic signs, from driving view images. In this study, we present a systematic comparison of three recent algorithms for large-scale multi-class road facility detection, namely Mask R-CNN, YOLOx, and YOLOv7, on the Mapillary dataset. The experimental results are evaluated according to the recall, precision, mean F1-score and computational consumption. YOLOv7 outperforms the other two networks in road facility detection, with a precision and recall of 87.57% and 72.60%, respectively. Furthermore, we test the model performance on our custom dataset obtained from the Japanese road environment. The results demonstrate that models trained on the Mapillary dataset exhibit sufficient generalization ability. The comparison presented in this study aids in understanding the strengths and limitations of the latest networks in multiclass object detection on large-scale street-level datasets.

No abstract