The processing of amino- and carboxyl-propeptides of fibrillar collagens is required to generate collagen monomers that correctly assemble into fibrils. Mutations in the ADAMTS2gene, the aminopropeptidase of procollagen I and II, result in the accumulation of non-fully processed type I procollagen, causing human Ehlers-Danlos syndrome type VIIC and animal dermatosparaxis. In this study, we show that the aminopropeptide of type I procollagen can be cleaved in vivo in absence of ADAMTS-2 activity and that this processing is performed at the cleavage site for ADAMTS-2. In an attempt to identify the enzyme responsible for this alternative aminoprocollagen peptidase activity, we have cloned the cDNA and determined the primary structure of human and mouse ADAMTS-14, a novel ADAMTS displaying striking homologies with ADAMTS-2 and -3. The structure of the human gene, which maps to 10q21.3, and the mechanisms of generation of the various transcripts are described. The existence of two sites of initiation of transcription, in two different promoter contexts, suggests that transcripts resulting from these two sites can be differently regulated. The tissue distribution of ADAMTS-14, the regulation of the gene expression by various cytokines and the activity of the recombinant enzyme are evaluated. The potential function of ADAMTS-14 as a physiological aminoprocollagen peptidase in vivo is discussed.

Background: Driving fatigue affects the reaction ability of a driver. The aim of this research is to analyze the relationship between driving fatigue, physiological signals and driver’s reaction time. Methods: Twenty subjects were tested during driving. Data pertaining to reaction time and physiological signals including electroencephalograph (EEG) were collected from twenty simulation experiments. Grey correlation analysis was used to select the input variable of the classification model. A support vector machine was used to divide the mental state into three levels. The penalty factor for the model was optimized using a genetic algorithm. Results: The results show that α/β has the greatest correlation to reaction time. The classification results show an accuracy of 86%, a sensitivity of 87.5% and a specificity of 85.53%. The average increase of reaction time is 16.72% from alert state to fatigued state. Females have a faster decrease in reaction ability than males as driving fatigue accumulates. Elderly drivers have longer reaction times than the young. Conclusions: A grey correlation analysis can be used to improve the classification accuracy of the support vector machine (SVM) model. This paper provides basic research that online detection of fatigue can be performed using only a simple device, which is more comfortable for users.

Improving lighting arrangements at the highway intersections can significantly reduce the likelihood of crashes. Meanwhile, a reasonable gradient illumination scheme can increase drivers' safety by...

Distracted driving has become a major cause of road traffic accidents. There are generally four different types of distractions: manual, visual, auditory, and cognitive. Manual distractions are the most common. Previous studies have used physiological indicators, vehicle behavior parameters, or machine-visual features to support research. However, these technologies are not suitable for an in-vehicle environment. To address this need, this study examined a non-intrusive method for detecting in-transit manual distractions. Wrist kinematics data from 20 drivers were collected using wearable inertial measurement units (IMU) to detect four common gestures made while driving: dialing a hand-held cellular phone, adjusting the audio or climate controls, reaching for an object in the back seat, and maneuvering the steering wheel to stay in the lane. The study proposed a progressive classification model for gesture recognition, including two major time-based sequencing components and a Hidden Markov Model (HMM). Results show that the accuracy for detecting disturbances was 95.52%. The accuracy associated with recognizing manual distractions reached 96.63%, using the proposed model. The overall model has the advantages of being sensitive to perceptions of motion, effectively solving the problem of a fall-off in recognition performance due to excessive disturbances in motion samples.

Promoting the management of the over-limit of freight transport vehicles plays an important role in the sustainable development of the highway industry. Vehicle outer contour dimension measurement is a key element in highway over-limit detection. The current detection approaches and research methods, however, are insufficient for high-precision flow detection. Therefore, this study proposes an algorithm for measuring the dimensions of a truck’s outer contours, using unmanned aerial vehicle (UAV) binocular stereo vision. First, this study leverages a binocular camera mounted on a UAV to reconstruct the 3D point clouds of the truck. Second, the point cloud data are clustered using an FoF (Friends-of-Friends algorithm); this recognizes the cluster of truck points according to the truck’s characteristics. Finally, the principal component analysis and the Gaussian kernel density estimation are used to generate the outer contour dimensions of the trucks. Twenty model vehicles are selected as test objects to verify the reliability of the algorithm. The average error of the algorithm is represented by calculating the average value of the difference between the real size and the predicted size of the three dimensions. The experimental results demonstrate that the average error of this measurement approach is less than 2.5%, and the method is both stable and robust. This approach aligns with national regulations for over-limit detection.

There is little research on the degrees of drivers’ short-term behaviours regarding driving safety. To solve this problem, this paper investigated the concept of driving proneness and evaluated the propensities of different drivers to engage in different operations for the following scenarios of urban traffic. From the real driving data of sixteen drivers on a city road, car-following data fragments were extracted and six key parameters were obtained: throttle percentage, change rate of throttle percentage, brake pressure, change rate of brake pressure, absolute value of steering angle and absolute value of steering angle speed. Symbolic Aggregate Approximation was used to reduce the dimensionality of the parameters. The input of the Hidden Markov Model-Viterbi was obtained by the use of statistical methods. The output of the model is the probability of the three proneness states of introversion, neutrality and extroversion, from which the proneness value of each driver was calculated. The weighted proneness value of each driver was obtained by the use of the entropy weight method to assign weights to each parameter. The operating characteristics of the drivers were also analysed and described. The method presented in this paper can provide accurate and real-time warning in network-driven environments.