Low dose acute administration of N-methyl-D-aspartate receptor (NMDAR) antagonist MK-801 is widely used to model cognition impairments associated with schizophrenia (CIAS) in rodents. However, due to no unified standards for animal strain, dose, route of drug delivery, and the duration of administration, how different doses of MK-801 influence behavior and fundamental frequency bands of the local field potential (LFP) in cortical and subcortical brain regions without consistent conclusions. The optimal dose of MK-801 as a valid cognition impairers to model CIAS in C57BL/6J mice remains unclear. The current study characterizes the behavior and neural oscillation alterations induced by different low doses of MK-801 in medial prefrontal cortex (mPFC) and hippocampus CA1 of C57BL/6J mice. The results reveal that mice treated with 0.1 and 0.3 mg/kg MK-801 demonstrate increased locomotion and diminished prepulse inhibition (PPI), while not when treated with 0.05 mg/kg MK-801. We also find that MK-801 dose as low as 0.05 mg/kg can significantly diminishes spontaneous alteration during the Y-maze test. Additionally, the oscillation power in delta, theta, alpha, gamma and HFO bands of the LFP in mPFC and CA1 was potentiated by different dose levels of MK-801 administration. The current findings revealed that the observed sensitivity against spontaneous alteration impairment and neural oscillation at 0.05 mg/kg MK-801 suggest that 0.05 mg/kg will produce changes in CIAS-relevant behavior without overt changes in locomotion and sensorimotor processing in C57BL/6J mice.

Road traffic markings are made of glass beads embedded in paint. To analyze the effects of glass-bead radius, embedment depth, correlated color temperature of light sources, and incident angle on the reflective performance of glass beads, a numerical model is established using the geometric optics method, effective light conditions are analyzed, calculation methods for optical power devised, and non-standard planes of incident light calculated. Calculations were also carried out. Results show that the retroreflective properties of glass beads are positively correlated with the correlated color temperature of the light source and radius of the beads. The glass-bead radii, embedment depths, and incident angles have little effect on their reflective performance. Moreover, the correlated color temperature of the light source is an important factor affecting the accuracy of measuring glass-bead reflective performance. When using retro reflectometers, standard retro reflectance-measuring devices for retro reflectance measurement, and other equipment to evaluate the reflective performance of road traffic markings, the correlated color temperature of the light source of the equipment should be as close as possible to 2856 K.

To solve the problem of low efficiency in retroreflection maintenance of road traffic markings, in this study a vehicle-mounted LiDAR-based perception and evaluation method of retroreflection of markings is proposed. First, this method establishes a calibration prediction model for LiDAR based on a regression decision tree. Then, a marking maintenance evaluation model is constructed in combination with the decision threshold proposed by China’s national standard, and the accuracy of the maintenance evaluation model is analyzed using F1-score, recall, and precision. In this study, four marking lines were used as the calibration data source, and a dataset of an independent 1,300 m road section was used to verify the established models. The results show that the coefficient of the retroreflected luminance ( RL) and the reflection intensity of the markings are positively correlated. During the construction of the calibration prediction model, the multiple linear regression functions, the second-order polynomial functions, and the decision tree are compared, and the result indicates that decision tree has the best fit to the data with the coefficient of determination for the established calibration prediction model better than 0.95. The agreement between the maintenance decision obtained from the maintenance evaluation model and the traditional method is more than 85%. The time cost is reduced by at least 90%. The proposed calibration prediction model can accurately predict the RL, and can quickly collect the RL values of the in-service road traffic markings. The proposed maintenance evaluation model is highly efficient and can replace the traditional evaluation method for markings.