Impaired Cognitive Function and Altered Hippocampal Synaptic Plasticity in Mice Lacking Dermatan Sulfotransferase Chst14/D4st1

Chondroitin sulfate (CS) and dermatan sulfate (DS) proteoglycans (PGs) are major extracellular matrix (ECM) components of the central nervous system (CNS). A large body of evidence has shown that CSPGs/DSPGs play critical roles in neuronal growth, axon guidance, and plasticity in the developing and mature CNS. It has been proposed that these PGs exert their function through specific interaction of CS/DS chains with its binding partners in a manner that depends on the sulfation patterns of CS/DS. It has been reported that dermatan 4-O-sulfotransferase-1 (Chst14/D4st1) specific for DS, but not chondroitin 4-O-sulfotransferase-1 (Chst11/C4st1) specific for CS, regulates proliferation and neurogenesis of neural stem cells (NSCs), indicating that CS and DS play distinct roles in the self-renewal and differentiation of NSCs. However, it remains unknown whether specific sulfation profiles of DS has any effect on CNS plasticity. In the present study, Chst14/D4st1-deficient (Chst14−/−) mice was employed to investigate the involvement of DS in synaptic plasticity. First, behavior study using Morris Water Maze (MWM) showed that the spatial learning and memory of Chst14−/− mice was impaired when compared to their wild type (WT) littermates. Corroborating the behavior result, long-term potentiation (LTP) at the hippocampal CA3-CA1 connection was reduced in Chst14−/− mice compared to the WT mice. Finally, the protein levels of N-Methyl-D-aspartate (NMDA) receptor, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor, postsynaptic density 95 (PSD95), growth associated protein 43 (GAP-43), synaptophysin (SYN) and N-ethylmaleimide sensitive factor (NSF) which are important in synaptic plasticity were examined and Chst14/D4st1 deficiency was shown to significantly reduce the expression of these proteins in the hippocampus. Further studies revealed that Akt/mammalian target rapamycin (mTOR) pathway proteins, including protein kinase B (p-Akt), p-mTOR and p-S6, were significantly lower in Chst14−/− mice, which might contribute to the decreased protein expression. Together, this study reveals that specific sulfation of DS is critical in synaptic plasticity of the hippocampus and learning and memory, which might be associated with the changes in the expression of glutamate receptors and other synaptic proteins though Akt/mTOR pathway

Does a prosocial attitude reduce risky driving behaviour under time pressure?

Time pressure could make drivers exhibit more risky driving behaviour. Attitudes can influence people&rsquo;s behaviours, but few studies have explored the influence of prosocial attitudes on driving behaviour. The purpose of this study was to explore the influence of prosocial attitudes on driving behaviour under time pressure. A 2 (high/low prosocial attitude) *2 (present/no time pressure) mixed design was used to investigate the interaction between prosocial attitude and time pressure on driving behaviour. Prosocial attitudes and time pressure have a significant main effect on driving behaviour. Drivers with high prosocial attitudes made lane changes at a greater distance from pedestrians and decelerated to a greater degree than drivers with low prosocial attitudes when interacting with pedestrians. Under time pressure, people drive faster and accelerate more quickly. Specifically, we found an interaction between time pressure and prosocial attitudes on driving behaviour. Drivers with low prosocial attitudes showed higher speeds than drivers with high prosocial attitudes under the time pressure scenario on foggy roads. The results showed that high prosocial attitudes lead to friendly interactions with pedestrians and careful driving in specific situations, even under time pressure. The present study not only expands the research on driving behaviour and attitude but can also provide some data support and guidance for driver selection and training.</p

Can prosocial attitude reduce the risk behavior in simulated driving?

High traffic density may lead to more traffic accidents because of more frequent lane change and overtaking behaviors, but drivers with different characteristics may exhibit different driving behaviors. The present study explored the difference in driving behaviors between drivers with a high/low prosocial attitude under high/low traffic density. In this study, a 2 (high/low prosocial attitude) *2 (high/low traffic density) mixed design was used to investigate the interaction between prosocial attitude and traffic density on lane change and overtaking behavior. The implicit association test paradigm was used to measure prosocial attitude, and drivers were divided into two groups. Forty subjects were asked to complete simulated driving tasks under the two conditions of high and low traffic density, and driving behaviors were recorded by driving simulators. The results show that high traffic density leads to more lane change and overtaking behavior. Drivers with a high prosocial attitude have better driving performance under both high and low traffic density, but drivers with a low prosocial attitude maintain a smaller transverse distance from adjacent vehicles in high traffic density, which may increase risk. This study provides support for the selection, training and intervention of professional drivers.</p

The different effects of personality on prosocial and aggressive driving behaviour in a Chinese sample

Dangerous driving behaviours, as a direct cause of accidents and death, are the focus of considerable research attention. However, unlike unsafe driving behaviours, few studies have explored safe driving behaviours and their effects on road traffic. This study aims to verify the Chinese version of the Prosocial and Aggressive Driving Inventory (PADI) and then investigate the relationship between personality and aggressive/prosocial driving behaviours. A total of 303 licensed drivers were recruited, and they voluntarily and anonymously completed the PADI, the Driving Behaviours Questionnaire (DBQ), and personality scales (anger, sensation-seeking and altruism). The results of this research confirmed the reliability and validity of the Chinese PADI. Most importantly, it was found that different relationships between different personalities and aggressive/prosocial driving behaviours. Specifically, individuals with high altruism exhibited more prosocial driving behaviours, while individuals with high sensation seeking presented more aggressive driving behaviours. The importance of these findings lies in two main potential implications: developing an effective measurement of prosocial driving behaviours in China and providing favourable evidence to guide drivers toward more prosocial driving behaviours. (C) 2018 Elsevier Ltd. All rights reserved

The relationship between personalities and self-report positive driving behavior in a Chinese sample.

Driving behaviors play an important role in accident involvement. Concretely speaking, aberrant driving behaviors would cause more accidents, and oppositely positive driving behaviors would promote to build safety traffic environment. The main goals of this study were to explore the positive driving behavior and its relationship with personality in a Chinese sample. A total of 421 licensed drivers (286 male and 135 female) from Beijing, China completed the Positive Driver Behavior Scale (PDBS), the Driver Behavior Questionnaire (DBQ), the Dula Dangerous Driving Index (DDDI) and the Big Five Inventory (BFI) on a voluntary and anonymous basis. The results showed that the Chinese version of the PDBS has both reliability and validity and that the PDBS was significantly correlated with the BFI. Specifically, the PDBS was negatively correlated with neuroticism (r = -0.38) and positively correlated with extraversion, agreeableness, conscientiousness and openness to experience (the correlation coefficient ranged from 0.36 to 0.55). In contrast with previous research, age was negatively correlated with the PDBS (r = -0.38) in our sample, which may have resulted from less driving experience or a lack of available cognitive resources

Differences in visual-spatial working memory and driving behavior between morning-type and evening-type drivers

Circadian rhythms are changes in life activities over a cycle of approximately 24 hours. Studies on chronotypes have found that there are significant differences in physiology, personality, cognitive ability and driving behavior between morning-type and evening-type people. The purpose of this study is to explore the relationship between visual-spatial working memory and driving behavior between morning-type and evening-type drivers in China. A total of 42 Chinese drivers were selected to participate in this study according to their score on the Momingness-Eveningness Questionnaire, including 22 morning-type drivers and 20 evening-type drivers. During the experiment, the participants completed one cognitive task (visual-spatial working memory), two simulated driving tasks (car-following task and pedestrian-crossing task), and the Dula Dangerous Driving Index (DDDI). The results showed that evening-type drivers self-reported more dangerous driving behaviors but had better lateral control on the simulated driving task than morning-type drivers. In addition, evening-type drivers had greater accuracy when performing the visual-spatial working memory task. Moreover, the accuracy on the visual-spatial working memory task positively predicted the percentage of time over the speed limit by 10 mph (POS10) and negatively correlated with the reaction time measure (time to meet pedestrians) in the pedestrian-crossing task. The relationships among chronotype, cognitive ability and driving behavior are also discussed. Understanding the underlying mechanisms could help explain why evening-type drivers perform dangerous driving behaviors more often

The descriptive statistics of the PDBS items and subscales (<i>N</i> = 421).

<p>The descriptive statistics of the PDBS items and subscales (<i>N</i> = 421).</p

Hierarchical regression models of full PDBS.

<p>Hierarchical regression models of full PDBS.</p

Correlations among the PDBS, DBQ, DDDI and demographic variables.

<p>Correlations among the PDBS, DBQ, DDDI and demographic variables.</p

Participant demographics (<i>N</i> = 421).

<p>Participant demographics (<i>N</i> = 421).</p