Understanding the cognitive and neuroimaging bases underlying the detrimental impact of sleep deprivation on reciprocity.

Although the impact of sleep loss on social behaviors has been widely observed in recent years, the mechanisms underpinning these impacts remain unclear. In this study, we explored the detrimental effects of sleep deprivation on reciprocity behavior as well as its underlying psychological and neuroimaging mechanisms by combining sleep manipulation, an interpersonal interactive game, computational modeling and neuroimaging. Our results suggested that after sleep deprivation, individuals showed reduced reciprocity behavior, mainly due to their reduced weights on communal concern when making social decisions.

Somatic symptoms mediate the association between subclinical anxiety and depressive symptoms and its neuroimaging mechanisms.

Background: Subclinical anxiety, depressive and somatic symptoms appear closely related. However, it remains unclear whether somatic symptoms mediate the association between subclinical anxiety and depressive symptoms and what the underlying neuroimaging mechanisms are for the mediating effect.

Methods: Data of healthy participants (n = 466) and participants in remission of major depressive disorder (n = 53) were obtained from the Human Connectome Project.

The validity and reliability of the PHQ-9 on screening of depression in neurology: a cross sectional study.

Background: This study aimed to explore the validity and reliability of the Patient Health Questionnaire-9 (PHQ-9) on screening of depression among patients with neurological disorders, and to explore factors influencing such patients.

Methods: In this study, 277 subjects who were admitted to the department of neurology of our hospital due to different neurological disorders completed the PHQ-9 questionnaire. The Mini-International Neuropsychiatric Interview (MINI) and Hamilton Rating Scale for Depression (HAMD) were employed to evaluate the depressive symptoms of patients who completed the PHQ-9 questionnaire.

Road Profile Estimation Using a 3D Sensor and Intelligent Vehicle.

Autonomous vehicles can achieve accurate localization and real-time road information perception using sensors such as global navigation satellite systems (GNSSs), light detection and ranging (LiDAR), and inertial measurement units (IMUs). With road information, vehicles can navigate autonomously to a given position without traffic accidents. However, most of the research on autonomous vehicles has paid little attention to road profile information, which is a significant reference for vehicles driving on uneven terrain.

Pose Prediction of Autonomous Full Tracked Vehicle Based on 3D Sensor.

Autonomous vehicles can obtain real-time road information using 3D sensors. With road information, vehicles avoid obstacles through real-time path planning to improve their safety and stability. However, most of the research on driverless vehicles have been carried out on urban even driveways, with little consideration of uneven terrain.