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Driver assistance systems play an important role in enhancing vehicle active safety. However, most of the existing driver assistance systems overlook the impact of driver characteristics on control performance, making it difficult to optimize the intervention strength of the controller for different drivers to achieve optimal performance. This paper proposed a novel vehicle active collision avoidance control strategy considering driver characteristics. By adjusting the time domain parameters of the model predictive control (MPC), collision avoidance assistance can be more effectively tailored to different types of drivers. Firstly, the collected lane-changing and collision avoidance data of 10 drivers with varying levels of experience were analyzed and preprocessed. Based on this analysis, a comprehensive index of collision avoidance characteristics was established that integrated trajectory tracking ability and driver burden, enabling the derivation of the collision avoidance characteristic curve representing the driver's preference and habit was obtained. Secondly, a sixth-order polynomial trajectory planning method considering driver characteristics was proposed. This method was optimized under vehicle stability and driver matching constraints to generate personalized collision avoidance trajectories for different drivers. The proposed comprehensive index and personalized collision avoidance trajectory were incorporated into the active collision avoidance control system, and a personalized time-variable domain MPC controller was designed. Test results based on a driver-in-the-loop bench demonstrate that the proposed personalized MPC could better match diverse driving characteristics, enhance the driver's trajectory tracking ability in the process of collision avoidance, reduce the driving workload, thereby improving overall driving safety and comfort.

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This study aims to elucidate the impact of A-pillar blind spots on drivers' visibility of pedestrians during left and right turns at an intersection. An experiment was conducted using a sedan and a truck, with a professional test driver participating. The driver was instructed to maintain sole focus on a designated pedestrian model from the moment it was first sighted during each drive. The experimental results revealed how the blind spots caused by A-pillars occur and clarified the relationship between the pedestrian visible trajectory distance and specific vehicle windows. The results indicated that the shortest trajectory distance over which a pedestrian remained visible in the sedan was 17.6 m for a far-side pedestrian model during a right turn, where visibility was exclusively through the windshield. For the truck, this distance was 20.9 m for a near-side pedestrian model during a left turn, with visibility through the windshield of 9.5 m (45.5% of 20.9 m) and through the passenger-side window of 11.4 m (54.5% of 20.9 m). Additionally, we quantified the trajectory distances where pedestrians became invisible when the driver's view was obstructed by A-pillars. The sedan exhibited the highest invisibility rate at 46.1% for a far-side pedestrian model during a right turn, followed by the truck at 17.8% for the same model. These findings will be instrumental in developing new driving support systems aimed at enhancing visibility in situations where pedestrians are obscured by A-pillars.

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INTRODUCTION: Vehicles driving, or being swept, into floodwaters is a leading cause of flood-related death. Establishing safe behaviors among learner drivers may reduce risk throughout their driving lifetime. METHODS: An environmental scan of publicly available government issued learner and driver handbooks across the eight Australian jurisdictions was conducted to identify information provided regarding floodwaters. Search terms included 'flood,' 'rain,' 'water,' and 'wet.' A visual audit of flood-related signage was also conducted. RESULTS: Twelve documents, across eight jurisdictions, were analyzed. Four jurisdictions' documents provided no information on flooding. Of the four jurisdictions that provided information, content varied. This included highlighting risks and discouraging entering floodwaters in a vehicle, including penalties associated with travel on closed roads, to advising depth and current checks if crossing a flooded roadway, with recommendations based on vehicle size (preference given to bigger vehicles, i.e., 4wds). Information on flood-related signage was found in one jurisdiction. DISCUSSION: Learner and driver handbooks represent a missed opportunity to provide flood safety information. Currently, information is not provided in all jurisdictions, despite flood-related vehicle drowning deaths of drivers and passengers being a national issue. Where information is presented, it is limited, often lacks practical guidance on how to assess water depth, current, and road base stability, and could better use evidence regarding the psychological factors underpinning, and behavioral prompts for performing, or avoiding, risky driving behavior during floods. CONCLUSIONS: The provision and content of information in learner driver and driver handbooks must be improved, particularly within the context of increasing flooding and extreme weather associated with the effects of climate change. PRACTICAL APPLICATIONS: We encourage all jurisdictions to provide practical information that draws on evidence-based risk factors and empirically established psychological factors for behavioral change to help establish safe driver behaviors around floods in the formative years of learning to drive.

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INTRODUCTION: Driver anger and aggression have been linked to crash involvement and injury outcomes. Improved road safety outcomes may be achieved through understanding the causes of driver anger, and interventions designed to reduce this anger or prevent it from becoming aggression. Scales to measure anger propensities will be an important tool in this work. The measure for angry drivers (MAD; Stephens et al., 2019) is a contemporary scale designed to measure tendencies for anger across three types of driving scenarios: perceived danger from others, travel delays, and hostility or aggression from other drivers. METHOD: This study aimed to validate MAD using a representative sample of Australian drivers, stratified across age, gender, and location. Participants completed a 10-minute online survey that included MAD, sought demographic information (age, gender, driving purpose, crash history), as well as the frequency of aggressive driving. Multigroup confirmatory factor analyses (MGCFA) assessed how stable the structure of the MAD was across drivers of different ages, gender, purposes for driving and those who do or do not display anger aggressively. MAD was invariant across all groups, showing that all drivers interpreted and responded to MAD in the same way. RESULTS: A comparison of latent means showed anger tendencies were higher for men compared to women, for younger drivers compared to older drivers, and for those who drive mainly for work compared to those who mainly drive for other reasons. When controlling for driver factors, driving anger was associated with increased odds of being aggressive while driving. PRACTICAL APPLICATIONS: Overall, this study demonstrated that MAD is an appropriate scale to measure anger tendencies and can be used to support interventions, and evaluation of interventions, to reduce anger and aggressive driving.

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OBJECTIVE: Electronic Stability Control (ESC) is a standard feature on most modern cars, due to its reported efficiency to reduce the number of crashes of several types. However, empirical studies of safety effects of ESC for passenger vehicles have not considered some methodological problems that might have inflated the effects. This includes self-selection of drivers who buy/use ESC and behavioral adaptation to the system over long time periods, but also the dominant method of induced exposure. This study aimed to investigate whether such methodological problems might have influenced the results. METHOD: A meta-analysis was undertaken to investigate whether there are systematic differences between published studies. Moderators tested included when the study was undertaken, the type of vehicle studied, the percent ESC in the sample, size of sample, the length of the study, whether matched or un-matched vehicles were studied, whether induced exposure was used, and two variants of types of crashes used as controls. RESULTS: The effects found ranged from 38% to 75% reduction of crashes for the main targets of singles, running off road and rollover crashes. However, these effects were heterogeneous, and differed depending on the methods used. Most importantly, information that could have allowed more precise analyses of the moderators were missing in most publications. CONCLUSIONS: Although average effects were large and in agreement with previous meta-analyses, heterogeneity of the data was large, and lack of information about important moderators means that firm conclusions about what kind of mechanisms were influencing the effects cannot be drawn. The available data on ESC efficiency are not unanimous, and further investigations into the effects of ESC on safety using different methodologies are warranted.

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BACKGROUD: Supernatants from various cytological samples, including body cavity effusion, sputum, bronchoalveolar lavage fluid (BALF), and needle aspiration, have been validated for detecting genetic alterations using cell-free DNA (cfDNA) in patients with non-small cell lung cancer (NSCLC). However, the sensitivity of fusion variations detection remains challenging. The protection of cell-free RNA (cfRNA) is critical for resolving the issue. METHODS: A protective solution (PS) was applied for preserving cfRNA in cytological supernatant (CS), and the quality of protected cfRNA was assessed by cycle threshold (CT) values from reverse transcription quantitative polymerase chain reaction (RT-qPCR). Furthermore, we collected an additional set of malignant cytological and matched tumor samples from 84 NSCLC patients, cfDNA & cfRNA extraction and double detection for driver gene mutations was validated using the multi-gene mutations detection by RT-qPCR. RESULTS: Under the optimal protection system, 91.0% (101/111) of cfRNA were protected effectively. Among the 84 NSCLC patient samples, seven cytological samples failed the tests. In comparison with tumor samples, the overall sensitivity and specificity of detecting driver genes of supernatant cfDNA and cfRNA were 93.8% (74/77) and 100% (77/77), respectively. Notably, when focusing exclusively on patients with fusion gene changes, both sensitivity and specificity reached 100% (11/11) for EML4-ALK, ROS1, RET fusions, and MET ex14 skipping. CONCLUSION: These findings suggest that cfDNA & cfRNA extraction and double detection strategy recommended in this study improve the accuracy of driver genes mutations test, especially for RNA-based assay.

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A considerable number of vehicular accidents occur in low-millage zones like school streets, neighborhoods, and parking lots, among others. Therefore, the proposed work aims to provide a novel ADAS system to warn about dangerous scenarios by analyzing the driver's attention and the corresponding distances between the vehicle and the detected object on the road. This approach is made possible by concurrent Head Pose Estimation (HPE) and Object/Pedestrian Detection. Both approaches have shown independently their viable application in the automotive industry to decrease the number of vehicle collisions. The proposed system takes advantage of stereo vision characteristics for HPE by enabling the computation of the Euler Angles with a low average error for classifying the driver's attention on the road using neural networks. For Object Detection, stereo vision is used to detect the distance between the vehicle and the approaching object; this is made with a state-of-the-art algorithm known as YOLO-R and a fast template matching technique known as SoRA that provides lower processing times. The result is an ADAS system designed to ensure adequate braking time, considering the driver's attention on the road and the distances to objects.

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Most computational methods for predicting driver mutations have been trained using positive samples, while negative samples are typically derived from statistical methods or putative samples. The representativeness of these negative samples in capturing the diversity of passenger mutations remains to be determined. To tackle these issues, we curated a balanced dataset comprising driver mutations sourced from the COSMIC database and high-quality passenger mutations obtained from the Cancer Passenger Mutation database. Subsequently, we encoded the distinctive features of these mutations. Utilizing feature correlation analysis, we developed a cancer driver missense mutation predictor called CDMPred employing feature selection through the ensemble learning technique XGBoost. The proposed CDMPred method, utilizing the top 10 features and XGBoost, achieved an area under the receiver operating characteristic curve (AUC) value of 0.83 and 0.80 on the training and independent test sets, respectively. Furthermore, CDMPred demonstrated superior performance compared to existing state-of-the-art methods for cancer-specific and general diseases, as measured by AUC and area under the precision-recall curve. Including high-quality passenger mutations in the training data proves advantageous for CDMPred's prediction performance. We anticipate that CDMPred will be a valuable tool for predicting cancer driver mutations, furthering our understanding of personalized therapy.

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Parkinson's disease (PD) negatively affects driver fitness. Few studies document the benefits of in-vehicle information systems (IVIS) and advanced-driver assistance systems (ADAS), the focus of this study, for drivers with PD. This study quantified the impact of IVIS and ADAS on the number of on-road driving errors. Drivers with PD (N = 107) drove a vehicle equipped with IVIS and ADAS in traffic. The activation of IVIS and ADAS resulted in fewer driver errors. Specifically, adaptive cruise control reduced the number of speeding errors on the highway. Bradykinesia correlated with driving errors with deactivated systems. Memory impairments correlated with the total number of driving errors with activated systems. Impairments in executive function and visuospatial ability were associated with more errors during system deactivation. IVIS and ADAS reduced the total number of driving errors for PD drivers; ameliorated effects of individual variations; but memory declines posed a challenge while using these technologies.

A randomized controlled trial study of self-driving in-vehicle technologies on driver fitness for people with Parkinson's diseaseThis study investigated the effects of in-vehicle information systems (IVIS) and advanced driver-assistance systems (ADAS) on the driver fitness of individuals with Parkinson's disease (PD). Notably, 107 drivers with PD drove a vehicle equipped with IVIS and ADAS. The results showed that activating these systems led to fewer speeding errors on the highway. The study identified correlations between bradykinesia, executive function, visuospatial ability, and increased errors with deactivated systems, where memory impairments correlated with increased driving errors during system activation. Although IVIS and ADAS had a positive overall effect, challenges related to memory decline existed when these technologies were in use.

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BACKGROUND: Hepatocellular carcinoma (HCC), theseventh most common cancer worldwide, is characterized by a high mortality rate, advanced diagnosis, and susceptibility to extrahepatic metastasis. Numerous studies have shown that DNA methylation is a crucial factor in epigenetic modifications and regulation of carcinogenesis. METHODS: HCC patient data were sourced from the TCGA dataset as a training set, while GSE116174 was used as an external validation set for verification. Differential methylation and expression analyses were performed on HCC samples with and without extrahepatic metastasis. In the intersecting genes, the relationship between methylation and expression levels of the intersecting genes was analyzed. Genes with a correlation coefficient≥|0.30| and P<0.05 were identified as methylation driver genes. Cox regression analysis was conducted to identify genes associated with HCC prognosis and establish a risk score. Subsequently, a prognostic model was established and validated using Cox regression analysis incorporating the risk score and other clinical factors. Using immunohistochemistry to evaluate the expression of DHX58 and EIF5A2 in HCC tissues with and without extrahepatic metastasis. Immunoinfiltration analysis was performed on the HCC samples using CIBERSORT. RESULTS: Our research identified eight methylation driver genes for HCC extrahepatic metastasis, of which two genes (DHX58 and EIF5A2) were associated with HCC patient prognosis. And the study further constructed and validated the risk score and prognostic model. Immunoinfiltration analysis showed that M0 macrophage abundance was correlated with the prognosis of HCC patients. Immunohistochemistry revealed differences in DHX58 and EIF5A2 expression between HCC tissues with and without extrahepatic metastasis, consistent with our bioinformatics findings.

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Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver's arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system.

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As one of the advanced automotive chassis technologies, the steer-by-wire system offers a high level of precision, responsiveness, and controllability in the driving experience. It can also adjust and optimize parameters to adapt to the preferences of different drivers. However, when faced with the steer-by-wire system, both experienced drivers and novice drivers are in the novice stage, exhibiting learning or adaptation behaviors when using this steering system. In this paper, a small-scale pilot evaluation was conducted by means of a questionnaire survey and driving-simulator experiment, and the learning behavior and adaptability of four experienced and four novice drivers to the steer-by-wire system were analyzed when using the traditional steering system. The study found that experienced drivers show significant changes in their adaptation to the steering system, mainly due to their habitual driving with traditional steering systems. In contrast, novice drivers show no significant changes in their adaptation to the steering system, which is attributed to their lack of driving experience and skills, resulting in less sensitivity to changes in the steering system. Additionally, the study found that novice drivers under the steer-by-wire system grasp control over speed and steering-wheel angle more quickly. This research provides a reference for improving drivers' learning and adaptation abilities to the steer-by-wire system and optimizing the design of the steer-by-wire system.

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It is estimated that 10% to 20% of road accidents are related to fatigue, with accidents caused by drowsiness up to twice as deadly as those caused by other factors. In order to reduce these numbers, strategies such as advertising campaigns, the implementation of driving recorders in vehicles used for road transport of goods and passengers, or the use of drowsiness detection systems in cars have been implemented. Within the scope of the latter area, the technologies used are diverse. They can be based on the measurement of signals such as steering wheel movement, vehicle position on the road, or driver monitoring. Driver monitoring is a technology that has been exploited little so far and can be implemented in many different approaches. This work addresses the evaluation of a multidimensional drowsiness index based on the recording of facial expressions, gaze direction, and head position and studies the feasibility of its implementation in a low-cost electronic package. Specifically, the aim is to determine the driver's state by monitoring their facial expressions, such as the frequency of blinking, yawning, eye-opening, gaze direction, and head position. For this purpose, an algorithm capable of detecting drowsiness has been developed. Two approaches are compared: Facial recognition based on Haar features and facial recognition based on Histograms of Oriented Gradients (HOG). The implementation has been carried out on a Raspberry Pi, a low-cost device that allows the creation of a prototype that can detect drowsiness and interact with peripherals such as cameras or speakers. The results show that the proposed multi-index methodology performs better in detecting drowsiness than algorithms based on one-index detection.

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Taxis are essential to economic growth due to the ease and comfort they offer passengers. This is evident as most cities, especially in Africa, are dominated by taxis providing door-to-door services. However, their susceptibility to road traffic accidents (RTA) raises serious concerns due to their risky driving behaviours. In contrast, studies on taxi driver involvement in RTA due to their risky driving behaviours are sparse, especially in African countries. Consequently, the study examined the relationship between risky driving behaviour and traffic accident involvement among Nigerian commercial taxi drivers using the structural equation modeling (SEM) approach. Prior to the structural model analysis, the modified driver behaviour questionnaire (DBQ) was valid. This was assessed through the measurement model, and the results showed that the composite reliability, average variance extracted, and discriminant validity were greater than 0.7, greater than 0.5, and less than 0.90, respectively. Furthermore, the structural equation modeling results show that the driving violation and driving error constructs influenced road traffic accidents among taxi drivers, while inattention error was insignificant (p > 0.05). Although driving violations and errors significantly increase the chances of RTA among taxi drivers, driving violations had a more substantial influence than driving errors. Also, the regression coefficient indicates the risky driving behaviour of commercial taxi drivers accounts for 5.2 % of the RTAs in Nigeria. This research contributed to validating the DBQ for commercial taxi drivers in Nigeria, examining the influence of their driving violations, driving errors, and inattention errors on accident involvement and that inattention error may not necessarily influence accidents, which will aid policymakers in formulating mitigative strategies for RTA reductions. Moreso, it will guide driver trainers in curriculum development for specific commercial taxi driver training.

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Road corridors contain countless sources of distraction, each carrying the potential to draw drivers' eyes and minds off roads, thus increasing the risk of a crash. While the impact of billboards on driver distraction has received considerable attention, empirical data for other types of roadside advertising signs is very limited. Furthermore, the existing research examining other potential external sources of distraction is fragmentary, which makes it difficult to develop evidence-based road safety policies to mitigate risk. Therefore, the aim of this study was to conduct an exploratory review to ascertain and synthesise the available distraction-related literature for various types of roadside advertising signs (excluding billboards) and other potential external sources of distraction in the road corridor. Based on distraction-related theory and existing literature, 58 predefined categories of potential distraction sources were developed to guide an exploratory literature search using the Scopus database. Fourteen documents relating to external distraction sources were identified in the database search with a further five studies found using a backwards citation chaining search (N=19). However, no studies for non-billboard types of roadside advertising signs were found. Studies included those that investigated driver distraction in relation to construction zones, commercial logos contained within official traffic management Dynamic Message Signs (DMS) and service (logo) signs, drones, murals, roadside memorials and wind turbines. While one study investigated whether an external source of distraction can have a positive effect on driver behaviour, the remainder focused on the potential negative impacts on road safety of these sources of distraction. A key finding of the review is that crash risk increases when drivers continue to visually and cognitively engage with a source of distraction. Overall, the studies provide insights into the types of distractions that can be encountered in road corridors and the impact they can have on driver behaviour. However, more research is required before the mechanisms and pathways involved in these types of distractions can be effectively conceptualised, and thereby mitigated to reduce the burden of distraction risk and road trauma.

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BACKGROUND: CD155 is a transmembrane protein that inhibits antitumor immune response and represents a predictor of worse prognosis in non-small-cell lung cancer (NSCLC). However, it remains unexplored its association with clinical characteristics and genomic status of Latin American patients. This study characterizes the CD155 expression and its clinical implications in this population. METHODS: Tissue biopsies from 86 patients with locally-advanced or metastatic NSCLC were assessed for CD155 protein expression, ALK rearrangements and EGFR mutations. Cutoff values for high CD155 expression (CD155high) were determined from receiver operating characteristic (ROC) curves according to 2-year survival. It was evaluated its association with clinicopathological features, median progression-free survival (mPFS) and overall survival (mOS). RESULTS: the cutoff score for CD155high was 155 in the entire cohort and in patients without oncogenic alterations, and it was 110 in patients with oncogenic alterations. Eighty-four patients (97.7%) were CD155 positive, of which fifty-six (65.0%) had CD155high. EGFR L858R mutation related to lower CD155 IHC score than exon 19 deletion. Individuals with CD155high showed a shorter mOS (13.0 vs. 30.8 months; HR: 1.96 [95% CI, 1.15-3.35]; p = 0.014). Patients without oncogenic alterations having a CD155high displayed shorter mPFS (1.6 vs. 6.4 months, HR: 2.09 [95% CI, 1.06-4.20]; p = 0.034) and mOS (2.9 vs. 23.1 months; HR: 1.27 [95% CI, 1.07- 4.42]; p = 0.032). Patients with oncogenic alterations having CD155high only showed a trend to shorter mOS (26.3 vs. 52.0 months; HR: 2.39 [95% CI, 0.98-5.83]; p = 0.058). CONCLUSION: CD155high is a predictor of worse outcomes in patients with advanced NSCLC, predominantly among those without oncogenic alterations. CD155 could be a potential biomarker and a molecular target in patients with poor responses to current therapies.

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Autonomous vehicles (AVs) offer a range of substantial safety and mobility benefits. However, successful deployment of AVs will involve interacting with other road users, such as pedestrians and other human-driven vehicles. While previous research has focused on factors that influence perceptions of AVs, less work has addressed how specific interactions with other road users influence acceptability from multiple perspectives. Accordingly, we conducted six studies examining how vehicles, either human-driven or autonomous, should behave at a zebra crossing in terms of stopping distance from the crossing, how long a vehicle should wait before setting off, and the influence of traffic context. Using computer-generated videos we found that: the optimal stopping distance was just before the stop line; participants were generally accepting of a vehicle that waited until a pedestrian had fully cleared the crossing before setting off, and sometime earlier; the presence of other vehicles, context and observer viewpoint can affect judgements of vehicle behaviour; autonomous vehicles were judged more harshly than human drivers with learner drivers judged less harshly in some circumstances, and that vehicle size appeared to have little influence over the acceptability of vehicle behaviours. The results are important for informing the design of autonomous vehicle manoeuvres from the viewpoints of vehicle occupants and other road users.

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BACKGROUND: Cancer driver genes (CDGs) have been reported as key factors influencing the progression of lung adenocarcinoma (LUAD). However, the role of CDGs in LUAD prognosis has not been fully elucidated. METHODS: LUAD transcriptome data and CDG-related data were obtained from public databases and literature. Differentially expressed CDGs (DE-CDGs) greatly associated with LUAD survival (P < 0.05) were identified to establish a prognostic model. In addition, immune analysis of high-risk (HR) and low-risk (LR) groups was conducted by utilizing the CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms to assess immune differences. Subsequently, mutation analysis was conducted using maftools. Finally, candidate drugs were identified using the CellMiner database. RESULTS: 40 DE-CDGs significantly associated with LUAD survival and 11 DE-CDGs associated with prognosis were identified through screening. Regression analysis revealed that risk score can independently predict LUAD prognosis (P < 0.05). Immune landscape analysis revealed that compared to the HR group, the LR group had higher immune scores and high infiltration of various immune cells such as follicular helper B cells and T cells. Mutation landscape analysis demonstrated that missense mutation was the most common mutation type in both risk groups. Drug prediction analysis revealed strong correlations of fulvestrant, S-63845, sapacitabine, lomustine, BLU-667, SR16157, motesanib, AZD-9496, XK-469, dimethylfasudil, P-529, and imatinib with the model genes, suggesting their potential as candidate drugs targeting the model genes. CONCLUSION: This study identified 11 effective biomarkers, DE-CDGs, which can predict LUAD prognosis and explored the biological significance of CDGs in LUAD prognosis, immunotherapy, and treatment.

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Advanced driver-assistance systems (ADAS) are technologies that can enhance drivers' safety by relieving them from some driving related activities. However, police driving conditions and demands are different from those of civilian drivers. The objective of this study was to assess the impact of ADAS such as forward collision warning (FCW), automatic emergency braking (AEB), and blind spot monitoring (BSM) on police officers' driving performance, workload, and trust in vehicle safety to provide personalised solutions for police vehicles. A driving simulation study was conducted with 18 police officers. ADAS use was assessed under various driving conditions and while officers were engaged in non-driving related tasks. Findings suggested that the FCW and AEB systems improved officers' driving performance, while the BSM system had limited effectiveness due to low salience. ADAS were beneficial under normal driving conditions and when officers were using in-vehicle technology; however, they did not help officers in pursuit conditions.

A driving simulation study was conducted to assess the effect of ADAS in police vehicles under various driving and non-driving related task conditions. The results can help vehicle manufacturers improve the design and usability of ADAS in police cars.

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Cigarette smoking causes multiple cancers by directly influencing mutation burden of driver mutations. However, the mechanism between somatic mutation caused by cigarette smoking and bladder tumorigenesis remains elusive. Smoking-related mutation profile of bladder cancer was characterized by The Cancer Genome Atlas cohort. Integraticve OncoGenomics database was utilized to detect the smoking-related driver genes, and its biological mechanism predictions were interpreted based on bulk transcriptome and single-cell transcriptome, as well as cell experiments. Cigarette smoking was associated with an increased tumor mutational burden under 65 years old (p = 0.031), and generated specific mutational signatures in smokers. RB1 was identified as a differentially mutated driver gene between smokers and nonsmokers, and the mutation rate of RB1 increased twofold after smoking (p = 0.008). RB1 mutations and the 4-aminobiphenyl interference could significantly decrease the RB1 expression level and thus promote the proliferation, invasion, and migration ability of bladder cancer cells. Enrichment analysis and real-time quantitative PCR (RT-qPCR) data showed that RB1 mutations inhibited cytochrome P450 pathway by reducing expression levels of UGT1A6 and AKR1C2. In addition, we also observed that the component of immunological cells was regulated by RB1 mutations through the stronger cell-to-cell interactions between epithelial scissor+ cells and immune cells in smokers. This study highlighted that RB1 mutations could drive smoking-related bladder tumorigenesis through inhibiting cytochrome P450 pathway and regulating tumor immune microenvironment.