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As the global traffic environment becomes increasingly complex, driving safety issues have become more prominent, making manual-response driving warning systems (DWSs) essential. Augmented reality head-up display (AR-HUD) technology can project information directly, enhancing driver attention; however, improper design may increase cognitive load and affect safety. Thus, the design of AR-HUD driving warning interfaces must focus on improving attention and reducing cognitive load. Currently, systematic research on AR-HUD DWS interfaces is relatively scarce. This paper proposes an ecological interface cognitive balance design strategy for AR-HUD DWS based on cognitive load theory and environmental interface design theory. The research includes developing design models, an integrative framework, and experimental validation suitable for warning scenarios. Research results indicate that the proposed design effectively reduces cognitive load and significantly decreases driver response and comprehension times, outperforming existing interfaces. This design strategy and framework possess promotional value, providing theoretical references and methodological guidance for AR-HUD warning interface research.

Controlling the in-car environment, including temperature and ventilation, is necessary for a comfortable driving experience. However, it often distracts the driver’s attention, potentially causing critical car accidents. In the present study, we implemented an in-car environment control system utilizing a brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). In the experiment, four visual stimuli were displayed on a laboratory-made head-up display (HUD). This allowed the participants to control the in-car environment by simply staring at a target visual stimulus, i.e., without pressing a button or averting their eyes from the front. The driving performances in two realistic driving tests—obstacle avoidance and car-following tests—were then compared between the manual control condition and SSVEP-BCI control condition using a driving simulator. In the obstacle avoidance driving test, where participants needed to stop the car when obstacles suddenly appeared, the participants showed significantly shorter response time (1.42 ± 0.26 s) in the SSVEP-BCI control condition than in the manual control condition (1.79 ± 0.27 s). No-response rate, defined as the ratio of obstacles that the participants did not react to, was also significantly lower in the SSVEP-BCI control condition (4.6 ± 14.7%) than in the manual control condition (20.5 ± 25.2%). In the car-following driving test, where the participants were instructed to follow a preceding car that runs at a sinusoidally changing speed, the participants showed significantly lower speed difference with the preceding car in the SSVEP-BCI control condition (15.65 ± 7.04 km/h) than in the manual control condition (19.54 ± 11.51 km/h). The in-car environment control system using SSVEP-based BCI showed a possibility that might contribute to safer driving by keeping the driver’s focus on the front and thereby enhancing the overall driving performance.

The style of geovisualization influences drivers’ cognition, decision-making, and spatial learning abilities. With the advancement of in-vehicle navigation technologies, Augmented Reality Head-Up Displays (AR-HUDs) have been widely applied in driving contexts. However, whether AR-HUDs impair drivers’ spatial learning and lead to over-reliance on navigation tools remains unclear. This study evaluates the impact of 2D and 3D Arrow Navigation Interfaces (ANIs) within AR-HUD systems on drivers’ spatial learning, using continuous and objective physiological measures, including Electroencephalography (EEG), Electrodermal Activity (EDA), Heart Rate (HR), and Heart Rate Variability (HRV). A pilot experiment conducted under real-road conditions indicates that the 3D-ANI enhances drivers’ spatial memory and reduces cognitive load. Notably, the depth perception and landmark cues provided by the 3D-ANI facilitate spatial memory encoding under turn-by-turn navigation, mitigating the typical limitations of such systems in supporting spatial knowledge acquisition. These findings offer critical insights into spatial cognitive mechanisms and provide valuable guidance for optimizing navigation interface design.

The main goal of this review is to provide an updated overview of the involvement of the RNA-binding protein (RBP) HuD, encoded by the ELAVL4 gene, in nervous system development, maintenance, and function, and its emerging role in nervous system diseases. A particular focus is on recent studies reporting altered HuD levels, or activity, in disease models and patients. Substantial evidence suggests HuD involvement in Parkinson’s disease (PD), Alzheimer’s disease (AD), and amyotrophic lateral sclerosis (ALS). Interestingly, while possible disease-causing mutations in the ELAVL4 gene remain elusive, a common theme in these diseases seems to be the altered regulation of HuD at multiple steps, including post-transcriptional and post-translational levels. In turn, the changed activity of HuD can have profound implications for its target transcripts, which are overly stabilized in case of HuD gain of function (as proposed in PD and ALS) or reduced in case of decreased HuD binding (as suggested by some studies in AD). Moreover, the recent discovery that HuD is a component of pathological cytoplasmic inclusion in both familial and sporadic ALS patients might help uncover the common molecular mechanisms underlying such complex diseases. We believe that deepening our understanding of the involvement of HuD in neurodegeneration could help developing new diagnostic and therapeutic tools.

Alzheimer's disease (AD) is the most common neurodegenerative pathology in older persons. The accumulation of amyloid β (Aβ) plaques is a major contributor to AD development. The RNA‐binding protein HuD/ELAVL4 has been implicated in the formation of Aβ plaques, but its role in AD is unclear. Here, we report that ablation of HuD from CAMK2A+ neurons (HuDcKO) in the 5xFAD mouse model of AD results in a significant reduction of Aβ plaques and the alleviation of some AD‐associated behaviors. Given the lack of effective therapies for AD, we propose that reducing HuD levels or function can contribute to diminishing Aβ plaque formation and AD‐associated pathology. The effect of ablating expression of the RNA‐binding protein HuD (ELAVL4) in neurons was evaluated in the Alzheimer's mouse model 5xFAD. We found that conditional removal of HuD resulted in diminished Aβ plaque formation and reduced AD‐associated hyperactivity in 5xFAD/HuDcKO mice relative to 5xFAD mice.

The neuronal Hu/ELAV-like proteins HuB, HuC and HuD are a class of RNA-binding proteins that are crucial for proper development and maintenance of the nervous system. These proteins bind to AU-rich elements (AREs) in the untranslated regions (3′-UTRs) of target mRNAs regulating mRNA stability, transport and translation. In addition to these cytoplasmic functions, Hu proteins have been implicated in alternative splicing and alternative polyadenylation in the nucleus. The purpose of this study was to identify transcriptome-wide effects of HuD deletion on both of these nuclear events using RNA sequencing data obtained from the neocortex of Elavl4–/– (HuD KO) mice. HuD KO affected alternative splicing of 310 genes, including 17 validated HuD targets such as Cbx3, Cspp1, Snap25 and Gria2. In addition, deletion of HuD affected polyadenylation of 53 genes, with the majority of significantly altered mRNAs shifting towards usage of proximal polyadenylation signals (PAS), resulting in shorter 3′-UTRs. None of these genes overlapped with those showing alternative splicing events. Overall, HuD KO had a greater effect on alternative splicing than polyadenylation, with many of the affected genes implicated in several neuronal functions and neuropsychiatric disorders.

This paper focuses on the establishment of an ARHUD assisted driving test system based on a VR platform, which has the advantages of high security and immersion, repeatable experiments and the ability to perform eye-movement analysis. This paper first defines and designs the vehicle driving safety icons based on human-computer interaction principles and engineering psychology, supplemented by PS to define and design the AR-HUD interface while combining mental load and other factors, then uses 3Dsmax software to build the 3D model material required for driving, then builds the driving environment and designs various driving emergencies in Unity based on featured technologies such as multi-channel rendering and global illumination, and then combines HTC VIVE Pro eye display. Thirty drivers were then tested on a distraction task. Analysis of the subjects’ eye-movement data revealed that the AR-HUD system improved the cognitive efficiency of the drivers compared to the traditional driving method while allocating cognitive resources to the central driving area, speed module, navigation information, and hazard warnings in a balanced manner, thus improving the ability to react to unexpected driving events.

Neuropathic pain is the most difficult-to-treat pain syndrome in multiple sclerosis. Evidence relates neuropathic pain to demyelination, which often originates from unresolved neuroinflammation or altered immune response. Posttranscriptional regulation of gene expression might play a fundamental role in the regulation of these processes. The ELAV RNA-binding proteins HuR and HuD are involved in the promotion of inflammatory phenomena and in neuronal development and maintenance, respectively. Thus, the aim of this study was to investigate the role of HuR and HuD in demyelination-associated neuropathic pain in the mouse experimental autoimmune encephalomyelitis (EAE) model. HuR resulted overexpressed in the spinal cord of MOG 35-55 –EAE and PLP 139-151 –EAE mice and was detected in CD11b + cells. Conversely, HuD was largely downregulated in the MOG–EAE spinal cord, along with GAP43 and neurofilament H, while in PLP-EAE mice, HuD and neuronal markers remained unaltered. Intranasal antisense oligonucleotide (ASO) delivery to knockdown HuR, increased myelin basic protein expression, and Luxol Fast Blue staining in both EAE models, an indication of increased myelin content. These effects temporally coincided with attenuation of pain hypersensitivity. Anti-HuR ASO increased the expression of HuD in GAP43-expressing cells and promoted a HuD-mediated neuroprotective activity in MOG–EAE mice, while in PLP–EAE mice, HuR silencing dampened pro-inflammatory responses mediated by spinal microglia activation. In conclusion, anti-HuR ASO showed myelin protection at analgesic doses with multitarget mechanisms, and it deserves further consideration as an innovative agent to counteract demyelination in neuropathic pain states. Graphical Abstract

To reduce drivers’ cognitive load during the driving process, The present study concentrates on the cognitive evaluation and analysis of the Head-Up Display (HUD) interface layout, aiming to enhance human cognitive efficiency. Initially, a combination of eye-tracking technology and cognitive load theory is used to investigate users’ attention allocation and changes in eye movement indicators, followed by the conversion of these indicators. A comprehensive HUD interface layout evaluation system is established, considering structural layout esthetics, task efficiency, and cognitive load. To achieve this, an intelligent cognitive evaluation method for the automotive HUD interface layout is proposed, based on the Bayesian BWM and Gray-TOPSIS. Bayesian BWM is employed to determine the weights of evaluation indicators, followed by Gray-TOPSIS to assess and rank the layout candidate solutions. Experimental results indicate that in the optimal layout design, users exhibit fewer eye movements, shorter gaze durations, esthetically pleasing interface structures, and lower cognitive loads. Furthermore, comparative experiments validate the effectiveness and stability of the Bayesian BWM and Gray-TOPSIS methods. These findings offer guidance and reference for further optimizing the layout of intelligent automotive HUD interfaces.

Self-driving vehicles are emerging as a result of technological advances, and the range of human behavior is expanding. The collateral information on driving is increasing, and head-up displays (HUDs) can be coupled with augmented reality displays to convey additional information to drivers in innovative ways. Interference between the actual driving environment and the displayed information can cause distractions. Research is required to find out what information should be displayed and how to properly display it considering the number of information, as well as the location and arrangement of the HUD. This study aims to examine the types of HUD information presentation that enhance the driver’s intuitive understanding. The first experiment identified which information affects drivers more in self-driving conditions in terms of error rate and importance. As a result, information that the drivers consider to be of greater importance or more relevant to their safety was selected. The level of HUD information complexity was assessed in the second experiment. The independent variables were the number of symbols, location of the HUD, and arrangement of the HUD. The results showed that the number of symbols was most affected and that fewer than six should be displayed. Besides, the arrangement of contents was more intuitive when a vertical alignment was used, and the main content should be placed in the center of the windshield area. Finally, ergonomic design guidelines of the information presentation type are proposed in this study.