Does Marriage Moderate Genetic Effects on Delinquency and Violence?: Marriage, Genes, and Antisocial Behavior

Using data from the National Longitudinal Study of Adolescent to Adult Health (N = 1,254), the authors investigated whether marriage can foster desistance from delinquency and violence by moderating genetic effects. In contrast to existing gene–environment research that typically focuses on one or a few genetic polymorphisms, they extended a recently developed mixed linear model to consider the collective influence of 580 single nucleotide polymorphisms in 64 genes related to aggression and risky behavior. The mixed linear model estimates the proportion of variance in the phenotype that is explained by the single nucleotide polymorphisms. The authors found that the proportion of variance in delinquency/violence explained was smaller among married individuals than unmarried individuals. Because selection, confounding, and heterogeneity may bias the estimate of the Gene × Marriage interaction, they conducted a series of analyses to address these issues. The findings suggest that the Gene × Marriage interaction results were not seriously affected by these issues

Gene by Social-Environment Interaction for Youth Delinquency and Violence: Thirty-Nine Aggression-Related Genes

Complex human traits are likely to be affected by many environmental and genetic factors, and the interactions among them. However, previous gene-environment interaction (G×E) studies have typically focused on one or only a few genetic variants at a time. To provide a broader view of G×E, this study examines the relationship between 403 genetic variants from 39 genes and youth delinquency and violence. We find evidence that low social control is associated with greater genetic risk for delinquency and violence and high/moderate social control with smaller genetic risk for delinquency and violence. Our findings are consistent with prior G×E studies based on a small number of genetic variants, and, more importantly, we show that these findings still hold when a large number of genetic variants are considered simultaneously. A key implication of these findings is that the expression of multiple genes related to delinquency depends on the social environment: gene expression is likely to be amplified in low-social-control environments but, tends to be suppressed in high/moderate-social-control environments. This study not only deepens our understanding of how the social environment shapes individual behavior, but also provides important conceptual and methodological insights for future G×E research on complex human traits

Road Infrastructure Challenges Faced by Automated Driving: A Review

Automated driving can no longer be referred to as hype or science fiction but rather a technology that has been gradually introduced to the market. The recent activities of regulatory bodies and the market penetration of automated driving systems (ADS) demonstrate that society is exhibiting increasing interest in this field and gradually accepting new methods of transport. Automated driving, however, does not depend solely on the advances of onboard sensor technology or artificial intelligence (AI). One of the essential factors in achieving trust and safety in automated driving is road infrastructure, which requires careful consideration. Historically, the development of road infrastructure has been guided by human perception, but today we are at a turning point at which this perspective is not sufficient. In this study, we review the limitations and advances made in the state of the art of automated driving technology with respect to road infrastructure in order to identify gaps that are essential for bridging the transition from human control to self-driving. The main findings of this study are grouped into the following five clusters, characterised according to challenges that must be faced in order to cope with future mobility: international harmonisation of traffic signs and road markings, revision of the maintenance of the road infrastructure, review of common design patterns, digitalisation of road networks, and interdisciplinarity. The main contribution of this study is the provision of a clear and concise overview of the interaction between road infrastructure and ADS as well as the support of international activities to define the requirements of road infrastructure for the successful deployment of ADS

Gene–Environment Correlation: Difficulties and a Natural Experiment–Based Strategy

Objectives. We explored how gene–environment correlations can result in endogenous models, how natural experiments can protect against this threat, and if unbiased estimates from natural experiments are generalizable to other contexts

A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving

Radar sensors were among the first perceptual sensors used for automated driving. Although several other technologies such as lidar, camera, and ultrasonic sensors are available, radar sensors have maintained and will continue to maintain their importance due to their reliability in adverse weather conditions. Virtual methods are being developed for verification and validation of automated driving functions to reduce the time and cost of testing. Due to the complexity of modelling high-frequency wave propagation and signal processing and perception algorithms, sensor models that seek a high degree of accuracy are challenging to simulate. Therefore, a variety of different modelling approaches have been presented in the last two decades. This paper comprehensively summarises the heterogeneous state of the art in radar sensor modelling. Instead of a technology-oriented classification as introduced in previous review articles, we present a classification of how these models can be used in vehicle development by using the V-model originating from software development. Sensor models are divided into operational, functional, technical, and individual models. The application and usability of these models along the development process are summarised in a comprehensive tabular overview, which is intended to support future research and development at the vehicle level and will be continuously updated

Caffeine intake antagonizes salt sensitive hypertension through improvement of renal sodium handling

High salt intake is a major risk factor for hypertension. Although acute caffeine intake produces moderate diuresis and natriuresis, caffeine increases the blood pressure (BP) through activating sympathetic activity. However, the long-term effects of caffeine on urinary sodium excretion and blood pressure are rarely investigated. Here, we investigated whether chronic caffeine administration antagonizes salt sensitive hypertension by promoting urinary sodium excretion. Dahl salt-sensitive (Dahl-S) rats were fed with high salt diet with or without 0.1% caffeine in drinking water for 15 days. The BP, heart rate and locomotor activity of rats was analyzed and urinary sodium excretion was determined. The renal epithelial Na+ channel (ENaC) expression and function were measured by in vivo and in vitro experiments. Chronic consumption of caffeine attenuates hypertension induced by high salt without affecting sympathetic nerve activity in Dahl-S rats. The renal α-ENaC expression and ENaC activity of rats decreased after chronic caffeine administration. Caffeine increased phosphorylation of AMPK and decrease α-ENaC expression in cortical collecting duct cells. Inhibiting AMPK abolished the effect of caffeine on α-ENaC. Chronic caffeine intake prevented the development of salt-sensitive hypertension through promoting urinary sodium excretion, which was associated with activation of renal AMPK and inhibition of renal tubular ENaC

Thermal Positioning Error Modeling of Servo Axis Based on Empirical Modeling Method

In order to investigate the thermal effect of a servo axis’ positioning error on the accuracy of machine tools, an empirical modeling method was proposed, which considers both the geometric and thermal positioning error. Through the analysis of the characteristics of the positioning error curves, the initial geometric positioning error was modeled with polynomial fitting, while the thermal positioning error was built with an empirical modeling method. Empirical modeling maps the relationship between the temperature points and thermal error directly, where the multi-collinearity among the temperature variables exists. Therefore, fuzzy clustering combined with principal component regression (PCR) is applied to the thermal error modeling. The PCR model can preserve information from raw variables and eliminate the effect of multi-collinearity on the error model to a certain degree. The advantages of this modeling method are its high-precision and strong robustness. Experiments were conducted on a three-axis machine tool. A criterion was also proposed to select the temperature-sensitivity points. The fitting accuracy of the comprehensive error modeling could reach about 89%, and the prediction accuracy could reach about 86%. The proposed modeling method was proven to be effective and accurate enough to predict the positioning error at any time during the machine tool operation

A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving

Radar sensors were among the first perceptual sensors used for automated driving. Although several other technologies such as lidar, camera, and ultrasonic sensors are available, radar sensors have maintained and will continue to maintain their importance due to their reliability in adverse weather conditions. Virtual methods are being developed for verification and validation of automated driving functions to reduce the time and cost of testing. Due to the complexity of modelling high-frequency wave propagation and signal processing and perception algorithms, sensor models that seek a high degree of accuracy are challenging to simulate. Therefore, a variety of different modelling approaches have been presented in the last two decades. This paper comprehensively summarises the heterogeneous state of the art in radar sensor modelling. Instead of a technology-oriented classification as introduced in previous review articles, we present a classification of how these models can be used in vehicle development by using the V-model originating from software development. Sensor models are divided into operational, functional, technical, and individual models. The application and usability of these models along the development process are summarised in a comprehensive tabular overview, which is intended to support future research and development at the vehicle level and will be continuously updated

Lateral Control Calibration and Testing in a Co-Simulation Framework for Automated Vehicles

Lateral vehicle control is of high importance in automated vehicles as it directly influences the vehicle’s performance and safety during operation. The linear quadratic regulator (LQR) controller stands out due to its high-performance characteristics and is used in the open source for self-driving functions. However, a notable limitation of the current approach is the manual calibration of LQR controllers based on the experience and intuition of the designers, leading to empirical uncertainties. To address this issue and enhance the lateral control performance, this paper concentrates on refining the LQR by employing three optimization algorithms: artificial bee colony optimization (ABC), genetic algorithm (GA), and particle swarm optimization (PSO). These algorithms aim to overcome the reliance on empirical methods and enable a data-driven approach to LQR calibration. By comparing the outcomes of these optimization algorithms to the manual LQR controller within an offline multibody simulation as a testing platform, this study highlights the superiority of the best-performing optimization approach. Following this, the optimal algorithm is implemented on a real-time system for the full vehicle level, revealing the model-in-the-loop and the hardware-in-the-loop gap up to 78.89% with lateral velocity when we use the relative error criterion (REC) method to validate and 2.35 m with lateral displacement when considering the maximum absolute value method