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While the contemporary censorship movement is driven by aggressive demands for book bans in libraries and schools, the motivations and goals fueling the movement are far more complex. Examining these motivations and goals, this article argues that the movement is intentionally deploying censorship and disinformation to pursue cultural erasure of specific marginalized communities. By drawing on not just library and information science, but economics, psychology, and other fields of knowledge, this article explores the movement's objectives, origins, political approaches, deployment of technology, and tactics learned from previous movements. This detailed excoriation of the contemporary censorship movement, and the living legacy of its powerful antecedents, is intended to provide informational professionals and their supporters with a richer understanding of the threats that they are confronting and strategies for engaging these threats. [ABSTRACT FROM AUTHOR]

Introduction: This study explores the optimization of Electric Bus (EB) performance by integrating predictive maintenance strategies, utilizing real-time data and advanced modeling techniques. Methods: The study involves installing measurement sensors to capture dynamic behavior and energy consumption during actual road trips, analyzing the collected data to refine vehicle dynamics models and assess battery degradation under various operational conditions, and employing a multi-objective optimization framework to minimize battery degradation while ensuring efficient energy use and maintaining operational requirements. Results and Discussion: The study offers valuable insights into battery management strategies, revealing that battery degradation can be reduced by 25% through optimum driving behavior, which can be achieved in real driving conditions by avoiding aggressive driving. This research supports the broader goal of promoting sustainable public transportation solutions through the effective use of electric buses, enabling operators to extend battery longevity and enhance overall vehicle performance by implementing the identified strategies. [ABSTRACT FROM AUTHOR]

Background: The rise in work zone crashes due to distracted and aggressive driving calls for improved safety measures. While Truck-Mounted Attenuators (TMAs) have helped reduce crash severity, the increasing number of crashes involving TMAs shows the need for improved warning systems. Methods: This study proposes an AI-enabled vision system to automatically alert drivers on collision courses with TMAs, addressing the limitations of manual alert systems. The system uses multi-task learning (MTL) to detect and classify vehicles, estimate distance zones (danger, warning, and safe), and perform lane and road segmentation. MTL improves efficiency and accuracy, making it ideal for devices with limited resources. Using a Generalized Efficient Layer Aggregation Network (GELAN) backbone, the system enhances stability and performance. Additionally, an alert module triggers alarms based on speed, acceleration, and time to collision. Results: The model achieves a recall of 90.5%, an mAP of 0.792 for vehicle detection, an mIOU of 0.948 for road segmentation, an accuracy of 81.5% for lane segmentation, and 83.8% accuracy for distance classification. Conclusions: The results show the system accurately detects vehicles, classifies distances, and provides real-time alerts, reducing TMA collision risks and enhancing work zone safety. [ABSTRACT FROM AUTHOR]

• The aggressive behavior and EEG changes of drivers when exiting and re-entering traffic jams were reproduced. • A framework to extract latent driving modes from continuous data was developed. • A two-dimensional risk quantification metric was used to evaluate driver performance across different road segments. Introduction: Traffic congestion is closely related to traffic accidents, as prolonged traffic congestion often results in frustration and aggressive behavior. Moreover, in daily commuting, drivers often have to pass through multiple congested road sections, and aggressive driving performance due to exiting or re-entering traffic jams has rarely been analyzed. Method: To fill this research gap, we designed an intermittent traffic congestion scenario using a driving simulator and employed unsupervised learning algorithms to extract high-level driving patterns gathered with EEG data to investigate the continuous effects of traffic jams, particularly when drivers exit and re-enter traffic jam conditions. Results: We discovered that drivers, upon exiting congested areas, engage in abrupt braking with a decrease in braking time of approximately 0.47 s and smooth lane changes with an increase in lane change time of approximately 0.5 s to maintain high-speed driving conditions. When drivers re-enter a traffic jam, they exhibit more abrupt stop-and-go behaviors to escape the traffic jam. The results of the risk assessment of driving behavior indicated that after leaving congested areas, free-flow segments have greater risk factors than other segments. Electroencephalogram (EEG) data were analyzed to identify instances of mind-wandering when a driver transitions into free-flowing segments, followed by a substantial increase in brain activity upon re-entry into congested traffic conditions. Practical Applications: The research outcomes suggest that optimizing the road segments after congestion, using appropriate entertainment systems to reduce driver stress, and implementing adaptive traffic signals to achieve smooth transitions during intermittent congestion can reduce aggressive driving behavior and enhance traffic safety. [ABSTRACT FROM AUTHOR]

This study investigates the impact of Chinese drivers' stochastic behaviour on local car-following situations using localized trajectory data. An extended stochastic car-following model (S-IDM) is established, which considers both internal and external stochasticity. External stochasticity is characterized by different driver types, while internal stochasticity is characterized by the standard deviation of acceleration under different headway and velocity differences for the same driver. The proposed model shows advantages in terms of single-vehicle simulation accuracy and traffic shock reproduction ability, compared to traditional and existing car-following models. The model can also be extended to the evolution analysis of mixed traffic flow models, where reducing the stochasticity of human-driven vehicles is critical for optimizing and controlling traffic flow. [ABSTRACT FROM AUTHOR]

Osteosarcoma (OSA) is a primary bone malignancy characterized by its aggressive nature and high propensity for metastasis. Despite advancements in multimodal therapies, the clinical outcomes for OSA patients remain suboptimal, necessitating deeper molecular insights for improved therapeutic strategies. Here, we employed single-cell RNA sequencing (scRNA-seq) to elucidate the cellular heterogeneity and transcriptional dynamics of OSA tumors. Our study identified eleven distinct tumor cell subpopulations, including osteoblastic, chondroblastic, and myeloid lineages, each exhibiting unique transcriptional profiles associated with disease progression and metastasis. Epithelial-mesenchymal transition (EMT) emerged as a critical process driving aggressive phenotypes, supported by gene set enrichment analyses (GSVA) and transcription factor regulatory network analyses. Integration of copy number variation (CNV) data highlighted genomic alterations in osteoblastic and chondroblastic cells, implicating potential therapeutic targets. Furthermore, immune cell infiltration analyses revealed distinct immune profiles across OSA subtypes, correlating with tumor mutational burden (TMB) and clinical outcomes. Our findings underscore the complexity of OSA biology and provide a foundation for developing personalized treatment strategies targeting tumor heterogeneity and immune interactions. [ABSTRACT FROM AUTHOR]

Driving is integral to many people's daily existence, but aggressive driving behavior increases the risk of road traffic collisions. Young drivers are more prone to aggressive driving and danger perception impairments. A driver's physiological state (e.g., fatigue, anger, or stress) can negatively affect their driving performance. This is especially true for young drivers who have limited driving experience. This research focuses on examining the connection between emotional arousal and aggressive driving behavior in young drivers, using predictive analysis based on electrodermal activity (EDA) data through neural networks. The study involved 20 participants aged 18 to 30, who completed 84 driving sessions. During these sessions, their EDA signals and driving behaviors, including acceleration and braking, were monitored using an Empatica E4 wristband and a telematics device. This study conducted two key analyses using neural networks. The first analysis used a comprehensive set of EDA features to predict emotional arousal, achieving an accuracy of 65%. The second analysis concentrated on predicting aggressive driving behaviors by leveraging the top 10 most significant EDA features identified from the arousal prediction model. Initially, the arousal prediction was performed using the complete set of EDA features, from which feature importance was assessed. The top 10 features with the highest importance were then selected to predict aggressive driving behaviors. Another aggressive driving behavior prediction with a refined set of difference features, representing the changes from baseline EDA values, was also utilized in this analysis to enhance the prediction of aggressive driving events. Despite moderate accuracy, these findings suggest that EDA data, particularly difference features, can be valuable in predicting emotional states and aggressive driving, with future research needed to incorporate additional physiological measures for enhanced predictive performance. [ABSTRACT FROM AUTHOR]

Driving style is the main cause of left-turn accidents at traffic intersections, and the study of the speed change of left-turning vehicles facing different driving styles of straight vehicles can effectively explain the causes of accidents. Therefore, field observations were made at three traffic intersections in Beijing's Changping, Haidian, and Pinggu districts, and the steering angles, speeds, and other indexes of the left-turning vehicles as well as the speed change of the straight vehicles were recorded by video. The observation time was selected as peak and off-peak hours on seven weekdays and four holidays. The driving styles of left-turning vehicles were categorized by steering angle and speed and compared with their effects on the speed of straight vehicles. The results show that when the left-turning vehicles are aggressive, most of the straight vehicles will keep the original speed on weekdays, which will easily lead to accidents; on holidays, most of them will choose to slow down. When facing conservative driving style, most drivers will choose to keep the original speed on weekdays, and still do so on holidays. It can be seen that under the influence of time, aggressive driving style may induce aggressive behaviors in interacting vehicles; while conservative driving induces aggressive behaviors in interacting vehicles under any time period. The results of the study may inform the prediction of potential consequences of mixed traffic, which may lead to better integration of self-driving cars into mixed traffic roads in the future. [ABSTRACT FROM AUTHOR]

Most existing two-lane traffic flow lattice models fail to fully consider the interactions between drivers' aggressive lane-changing behaviors and their desire for smooth driving, as well as their combined effects on traffic dynamics. To fill this research gap, under symmetric lane-changing rules, this paper proposes a novel two-lane lattice model that incorporates these two factors as co-influencers. Based on linear and nonlinear stability analyses, we derive the linear stability conditions of the new model, along with the density wave equation and its solutions describing traffic congestion propagation near critical points. Numerical simulations validate the theoretical findings. The results indicate that in the two-lane framework, enhancing either drivers' lane-changing aggressiveness or introducing the desire for smooth driving alone can somewhat improve traffic flow stability. However, when considering their synergistic effects, traffic flow stability is enhanced more significantly, and the traffic congestion is suppressed more effectively. [ABSTRACT FROM AUTHOR]

• Chronic cannabis use patterns were associated with psychological dysfunction. • Chronic cannabis use patterns were linked with emotion-directed driving styles. • Dependent users were at greater risk of psychological dysfunction and risky driving. • Other substance use and the tendency to drive under the influence were controlled for. • Cannabis use indirectly influenced risky driving via effects on self-regulatory deficits. Research has suggested that problematic cannabis use is associated with self-regulatory impairments, psychopathology, and the tendency to engage in risk taking behaviours. However, no research has applied the combined dynamics of these factors to the topic of risky driving behaviour specifically. This study investigated whether specific cannabis use patterns (i.e., use onset, duration, frequency, and quantity) and likely dependence influenced driving styles, via their effects towards emotional dysregulation and psychopathology (i.e., anxiety, depression, anger), among an online sample of active Australian cannabis users (N = 200). Group comparisons showed that likely dependent cannabis users reported significantly greater difficulties regulating their emotions, greater incidence of psychopathological symptoms (i.e., anxiety, depression, and anger), and more frequent engagement in risky driving styles (i.e., anxious driving, aggressive driving, dissociative driving, and reckless driving), compared to non-dependent cannabis users. Examination of bivariate correlations demonstrated significant and positive associations between specific cannabis use patterns, emotional regulation difficulties, psychopathology, and risky driving styles. Structural equation modelling highlighted that cannabis use patterns indirectly predicted participants self-reported engagement in risky driving styles via their effects towards self-regulatory difficulties and psychopathology. The findings of this study have highlighted driving related risks associated with cannabis use, outside of typical acute-related impairments. In addition, the study has emphasised the importance of psychological dysfunctioning in the engagement of both substance use and risky driving styles. Understanding this in combination is important for future interventions targeting aberrant driving behaviours. [ABSTRACT FROM AUTHOR]

• Study of psychological resilience and cognitive emotional regulation on driving anger and its expression. • The positive and negative cognitive emotion regulation mediate the effect of resilience to driving anger expression(DAX). • High-resilience drivers tend to employ positive emotion regulation and exhibit higher adaptive DAX. • Lower-resilience drivers tend to use negative emotion regulation, leading to increased DAS and nonadaptive DAX. • Proper emotional interventions could reduce driving anger and its negative consequences. Emotions that occur while driving, especially anger, can significantly impact driving-related safety. Due to the potential risks of aggressive driving behaviour, which include hazardous driving and traffic accidents, it is important to explore strategies to effectively manage anger, thereby enhancing driving-related safety. This study aimed to investigate the relationships among psychological resilience, cognitive emotional regulation, driving anger, and the expression of driving anger. A total of 350 drivers (aged 21–50 years) completed online questionnaires, including the Connor–Davidson Psychological Resilience Scale (CD-RISC), the Cognitive Emotion Regulation Questionnaire (CERQ), the Driving Anger Scale (DAS), and the Driver Anger Expression Inventory (DAX). The results indicated that a higher level of psychological resilience is associated with a greater tendency to employ positive cognitive emotion regulation strategies and a greater tendency to exhibit more adaptive expressions of driving anger. In contrast, a lower level of psychological resilience is associated with negative cognitive emotion regulation strategies, resulting in elevated levels of driving anger and a higher frequency of nonadaptive expressions. Additionally, cognitive emotion regulation mediated the relationship between psychological resilience and driving anger. These findings suggest that drivers with high levels of psychological resilience and those who engage in effective cognitive emotion regulation strategies are more likely to remain calm in irritating driving situations, thereby enhancing overall driving-related safety. [ABSTRACT FROM AUTHOR]

The article from the British Journal of Haematology discusses the validation of two biomarkers, methylation-based epitype (DME) and telomere length (TL), in chronic lymphocytic leukaemia (CLL) patients using data from UK clinical trials. The study found that both DME and TL provide independent prognostic information, with TL-S predicting poorer progression-free and overall survival. The research highlights the potential of these biomarkers in enhancing risk-adapted stratification of CLL patients and suggests the need for further studies with larger cohorts to identify patients who may benefit from targeted agents. [Extracted from the article]

In the context of an increasing interest towards the safe introduction of technologies that can help human drivers while performing their driving tasks in the ordinary traffic, the proposed work provides a contribution, investigating safety-related aspects of assisted vehicles in a simulated environment, by considering other driving behavioral parameters. In particular, the influence of Adaptive Cruise Control (ACC), combined with driver distraction and route familiarity, on the driving behavior was investigated using a driving simulator. A sample of 37 drivers, aged between 21 and 34 years performed the driving tests in the simulator environment, with different scenarios applied to one road section. The interactions between a lead vehicle and a follower were investigated, collecting the kinematic parameters of the vehicle (speed, acceleration) and its position in the road (lateral position and distance from the lead vehicle) either with or without active ACC conditions. Driving simulation scenarios differed among each other not only for the ACC turned on or off, but also for the secondary tasks presented to the drivers in specific time frames of the test. Moreover, tests were repeated to induce a route familiarity effect and to study its influence on driving behavior, thus on safety. Results highlighted that the active ACC was correlated with more cautious behaviors, in terms of speed, deceleration/acceleration and distance from the lead vehicle. Women drivers were associated to a greater variability in the driving behavior. The visual distraction induced by secondary tasks appeared to have negative effects on safety-related driving performances, especially when the secondary task was highly demanding. On the other hand, when drivers became familiar with the route, they performed more aggressive driving patterns. [ABSTRACT FROM AUTHOR]

• This is the first study to examine how the microbiome interacts with aggression through urine profiling. This analysis was particularly nuanced because not only did we compare basal urine metabolomes in response to microbiota manipulation (four unique study groups, above), but we also capture real-time metabolic responses to aggressive interactions by comparing samples taken before and after behavioral assays. • Here, we expanded the study of brain function in the aggression-microbiota interplay, quantifying serotonin, serotonin turnover and tryptophan levels using HPLC as well as identifying key gene expression patterns using transcriptomics in five different regions of the brain. We examined gene expression related to serotonin and other neurotransmitter receptors in multiple brain regions, identifying the septum as an important brain region for regulation of aggression. Gene set enrichment analysis was also used to highlight other, less studied pathways (Rho GTPase and Reelin) associated with aggressive behavior in the context of the gut-brain-microbiome axis. • The data generated in this study is rich (untargeted metabolomics of urine and transcriptomics of five brain regions), spanning four microbiota manipulation states, and will be publicly available , further extending the potential application of our research. • We include a study of humanized mice using unique fecal samples of 1-month-old infants, collected nearly a month after early-life ABX administration. In previous work (Uzan-Yulzari et al. 2021 , Nat Comm), we have demonstrated that ABX in this critical period of life can have lasting effects of childhood growth. Here, we extend these findings using samples from the same cohort. Using fecal samples collected weeks after ABX administration also reduces the direct chemical effects of ABX on the host, highlighting the causative role of the dysbiotic host microbiome and associated metabolome in driving aggressive behavior. We demonstrate that infant microbiota, perturbed within the first 48 h of life, has a lasting signature through 1 month of age that, when transplanted into GF mice, results in increased aggression (3 – 5 weeks after transplant) when compared to effects of stools of infants not exposed to any early-life antibiotics. The findings are revolutionary as they show how ABX-altered microbiota during a critical development window can lead to persisting behavioral deficits. Recent research has unveiled conflicting evidence regarding the link between aggression and the gut microbiome. Here, we compared behavior profiles of control, germ-free (GF), and antibiotic-treated mice, as well as re-colonized GF mice to understand the impact of the gut microbiome on aggression using the resident-intruder paradigm. Our findings revealed a link between gut microbiome depletion and higher aggression, accompanied by notable changes in urine metabolite profiles and brain gene expression. This study extends beyond classical murine models to humanized mice to reveal the clinical relevance of early-life antibiotic use on aggression. Fecal microbiome transplant from infants exposed to antibiotics in early life (and sampled one month later) into mice led to increased aggression compared to mice receiving transplants from unexposed infants. This study sheds light on the role of the gut microbiome in modulating aggression and highlights its potential avenues of action, offering insights for development of therapeutic strategies for aggression-related disorders. [ABSTRACT FROM AUTHOR]

Effectively regulating anger driving has become critical in ensuring road safety. The existing research lacks a feasible exploration of anger-driving regulation. This paper delves into the effect and neural mechanisms of emotional olfactory stimuli (EOS) on regulating anger driving based on EEG. First, this study designed an angry driving regulation experiment based on EOS to record EEG signals. Second, brain activation patterns under various EOS conditions are explored by analyzing functional brain networks (FBNs). Additionally, the paper analyzes dynamic alterations in anger-related characteristics to explore the intensity and persistence of regulating anger driving under different EOS. Finally, the paper studies the frequency energy of EEG changes under EOS through time–frequency analysis. The results indicate that EOS can effectively regulate a driver's anger emotions, especially with the banana odor showing superior effects. Under banana odor stimulus, synchronization between the parietal and temporal lobes significantly decreased. Notably, the regulatory effect of banana odor is optimal and exhibits sustained efficacy. The regulatory effect of banana odor on anger emotions is persistent. Furthermore, the impact of banana odor significantly reduces the distribution of high-energy activation states in the parietal lobe region. Our findings provide new insights into the dynamic characterization of functional connectivity during anger-driving regulation and demonstrate the potential of using EOS as a reliable tool for regulating angry driving. [ABSTRACT FROM AUTHOR]

Objectives: Aggressive driving behavior can lead to potential traffic collision risks, and abnormal weather conditions can exacerbate this behavior. This study aims to develop recognition models for aggressive driving under various climate conditions, addressing the challenge of collecting sufficient data in abnormal weather. Methods: Driving data was collected in a virtual environment using a driving simulator under both normal and abnormal weather conditions. A model was trained on data from normal weather (source domain) and then transferred to foggy and rainy weather conditions (target domains) for retraining and fine-tuning. The K-means algorithm clustered driving behavior instances into three styles: aggressive, normal, and cautious. These clusters were used as labels for each instance in training a CNN model. The pre-trained CNN model was then transferred and fine-tuned for abnormal weather conditions. Results: The transferred models showed improved recognition performance, achieving an accuracy score of 0.81 in both foggy and rainy weather conditions. This surpassed the non-transferred models' accuracy scores of 0.72 and 0.69, respectively. Conclusions: The study demonstrates the significant application value of transfer learning in recognizing aggressive driving behaviors with limited data. It also highlights the feasibility of using this approach to address the challenges of driving behavior recognition under abnormal weather conditions. [ABSTRACT FROM AUTHOR]

This study is a significant endeavor involving the development and testing of a comprehensive methodology to incorporate driving behavior into the analysis and prediction of vehicle fuel consumption. It underscores the crucial importance of understanding how different driving behavior affect fuel efficiency. The framework we present is a theoretical construct and a practical tool. It provides a robust, multi-step process for linking driving behavior to fuel consumption, leveraging both traditional statistical methods and advanced machine learning techniques to derive actionable insights. To test the framework, we used a naturalistic data that includes about 5408 different road users in a mixed traffic environment and urban settings in Germany. We applied a microscopic fuel consumption model to calibrate the framework and an unsupervised clustering algorithm to classify the behavior of the driver interacting with each other and with vulnerable road users. The framework includes developing Linear regression model as a baseline, which yields an R-squared of 0.511 and a Mean Squared Error (MSE) of 0.031, indicating moderate predictive accuracy. The final step includes choosing Random Forest as a better model, which achieves a higher R-squared of 0.956 and a lower MSE of 0.003. We also found that conservative and aggressive driving leads to significantly higher and more discrepancy in fuel consumption than normal driving behavior. These insights can promote more efficient driving practices, leading to significant fuel savings and environmental benefits. Article highlights: Driving behavior significantly impacts fuel consumption; smooth, controlled driving leads to greater fuel efficiency. Insights from this study can inform ADAS development, enabling real-time, fuel-efficient driving assistance systems. Promoting normal driving habits can reduce fuel use, aiding eco-friendly driving and sustainable transport solutions. [ABSTRACT FROM AUTHOR]

• The subjective assessment of compliance with traffic rules in Bulgaria is investigated in two different studies. • Study 1 finds that anxiety, sensation seeking, driving anger, and aggressive and risky driving behaviour significantly predict compliance with traffic rules. • Study 2 highlights the role of driving style revealing risky, irrational, distress-reduction, and patient and careful driving styles as significant predictors of this assessment. • Significant differences are found according to age, sex, driving experience, and negative driving outcomes. It is generally accepted that driving is safer when the rules are obeyed. Therefore, the pursuit of greater compliance with traffic rules is often seen as an intermediate goal in ensuring road safety. The article presents two studies that aim to investigate the subjective assessment of traffic rules compliance and the role of personality and behavioural factors such as anxiety, sensation seeking, driving anger, aggressive driving, and risky driving (Study 1; N=1433) on the one hand, and driving style (Study 2; N=456) on the other hand. The studies also examine the differences in traffic rules compliance by age, sex, driving experience, and negative driving outcomes such as registered violations, driving licence suspensions, and involvement traffic accidents. Self-report methods are used to assess personality factors, driving style, traffic rules compliance, and negative driving outcomes. The results show a generally positive assessment of compliance with traffic rules among the participants, with the majority reporting compliance in their daily driving behaviour. Study 1 indicates that all personality factors investigated are significant predictors of traffic rules compliance. Study 2 demonstrates that the risky style, the irrational style, the distress-reduction style, and the patient and careful style are significant predictors of traffic rule compliance. Demographic differences are also observed, with women being significantly more likely than men to perceive themselves as compliant drivers. In terms of negative driving outcomes, both studies demonstrate that individuals with a record of driving violations in the last three years, a suspended driving licence, and involvement in a traffic accident are significantly less likely to rate themselves as compliant. The results can contribute to the development of prevention programmes and road safety strategies to promote safer driving behaviour. [ABSTRACT FROM AUTHOR]

Anger impairs a driver's control and risk assessment abilities, heightening traffic accident risks. Constructing a multimodal dataset during driving tasks is crucial for accurate anger recognition. This study developed a multimodal physiological -vehicle driving dataset (DPV-MFD) based on drivers' self-reported anger during simulated driving tasks. In Experiment 1, responses from 624 participants to anger-inducing videos and driving scenarios were collected via questionnaires to select appropriate materials. In Experiments 2 and 3, multimodal dynamic data and self-reported SAM emotion ratings were collected during simulated and real-vehicle tasks, capturing physiological and vehicle responses in neutral and anger states. Spearman's correlation coefficient analysis validated the DPV-MFD's effectiveness and explored the relationships between multimodal data and emotional dimensions. The CNN-LSTM deep learning network was used to assess the emotion recognition performance of the DPV-MFD across different time windows, and its applicability in real-world driving scenarios was validated. Compared to using EEG data alone, integrating multimodal data significantly improved anger recognition accuracy, with accuracy and F1 scores rising by 4.49% and 9.14%, respectively. Additionally, real-vehicle data closely matched simulated data, confirming the dataset's effectiveness for real-world applications. This research is pivotal for advancing emotion-aware human–machine- interaction and intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Triple negative breast cancer (TNBC) subtype is characterized with higher EMT/stemness properties and immune suppressive tumor microenvironment (TME). Women with advanced TNBC exhibit aggressive disease and have limited treatment options. Although immune suppressive TME is implicated in driving aggressive properties of basal/TNBC subtype and therapy resistance, effectively targeting it remains a challenge. Minnelide, a prodrug of triptolide currently being tested in clinical trials, has shown anti-tumorigenic activity in multiple malignancies via targeting super enhancers, Myc and anti-apoptotic pathways such as HSP70. Distinct super-enhancer landscape drives cancer stem cells (CSC) in TNBC subtype while inducing immune suppressive TME. We show that Minnelide selectively targets CSCs in human and murine TNBC cell lines compared to cell lines of luminal subtype by targeting Myc and HSP70. Minnelide in combination with cyclophosphamide significantly reduces the tumor growth and eliminates metastasis by reprogramming the tumor microenvironment and enhancing cytotoxic T cell infiltration in 4T1 tumor-bearing mice. Resection of residual tumors following the combination treatment leads to complete eradication of disseminated tumor cells as all mice are free of local and distant recurrences. All control mice showed recurrences within 3 weeks of post-resection while single Minnelide treatment delayed recurrence and one mouse was free of tumor. We provide evidence that Minnelide targets tumor intrinsic pathways and reprograms the immune suppressive microenvironment. Our studies also suggest that Minnelide in combination with cyclophosphamide may lead to durable responses in patients with basal/TNBC subtype warranting its clinical investigation. [ABSTRACT FROM AUTHOR]

Summary: In our study, we focused on a particular subtype of aggressive breast cancer called Triple-Negative Breast Cancer (TNBC). We investigated a complex series of events that contribute to poor outcomes in this disease and uncovered a crucial signaling cascade driving tumor growth and progression. At the core of this signaling cascade are three key proteins: SRC, AKT, and ERK2. Together, they form a pathway that activates a transcription factor called Slug. Transcription factors act like molecular switches, controlling the expression of genes. Once Slug is activated, it strongly suppresses genes that would normally restrict cell growth and cell spread. One of the genes downregulated by Slug is TGFB2-AS1. This product of the TGFB2-AS1 gene normally controls levels of its target protein called TGF-beta2 (TGFB2), a protein which has roles in cell growth, cell migration and differentiation. Slug downregulation of TGFB2-AS1 results in higher TGFB2 levels, and this in turn contributes to the uncontrolled growth and spread of cancer cells. TGFB2, and other proteins in this pathway (SRC, AKT, ERK2, and a Slug interactor called LSD1) all maintain the stability of Slug, meaning that Slug levels remain high and drive the aggressive features of this subtype of breast cancer. Overall, our research sheds light on the intricate molecular mechanisms driving aggressive TNBC. It also identifies potential targets for future therapies, aimed at disrupting this harmful signaling pathway and potentially improving patient outcomes for this disease. Background: Treatment options for the Triple-Negative Breast Cancer (TNBC) subtype remain limited and the outcome for patients with advanced TNBC is very poor. The standard of care is chemotherapy, but approximately 50% of tumors develop resistance. Methods: We performed gene expression profiling of 58 TNBC tumor samples by microarray, comparing chemosensitive with chemoresistant tumors, which revealed that one of the top upregulated genes was TGFβ2. A connectivity mapping bioinformatics analysis predicted that the SRC inhibitor Dasatinib was a potential pharmacological inhibitor of chemoresistant TNBCs. Claudin-low TNBC cell lines were selected to represent poor-outcome, chemoresistant TNBC, for in vitro experiments and in vivo models. Results: In vitro, we identified a signaling axis linking SRC, AKT and ERK2, which in turn upregulated the stability of the transcription factors, Slug and Snail. Slug was shown to repress TGFβ2-antisense 1 to promote TGFβ2 signaling, upregulating cell survival via apoptosis and DNA-damage responses. Additionally, an orthotopic allograft in vivo model demonstrated that the SRC inhibitor Dasatinib reduced tumor growth as a single agent, and enhanced responses to the TNBC mainstay drug, Epirubicin. Conclusion: Targeting the SRC-Slug-TGFβ2 axis may therefore lead to better treatment options and improve patient outcomes in this highly aggressive subpopulation of TNBCs. [ABSTRACT FROM AUTHOR]

This study examines the effects of vehicle size on driver impressions and behavioral intentions. Study 1 tested whether vehicle size (large vs. small) affects perceived physical size (height, body shape) through socioeconomic status (SES). We found that large (vs. small) vehicle drivers were perceived as tall (vs. short), and this perception was mediated by the drivers’ estimated SES (but not by body shape). Study 2 focused on aggressive behavioral intentions (e.g. honking) toward other drivers, examining whether the relationship between vehicle size and intention was serially mediated by estimated physical size and traits (aggression, power). Here, large (vs. small) vehicle driver were perceived as tall (heavy) and possessing high power (high aggression), which is related to less (more) aggressive behavioral intention toward the driver. Our study suggests that individuals perceive other drivers’ physical sizes differently, and this perception is associated with differences in behavioral responses toward other drivers. [ABSTRACT FROM AUTHOR]

• Confirmed the three factor 23-item MAD. • MAD was invariant across gender, age group, aggressive driving tendencies. • Men had higher anger means than women. • Younger drivers had higher anger than older drivers. • Anger tendencies were related to greater aggression. 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. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate the impact of automated vehicle (AV) interaction mode on drivers' trust and preferred driving styles in response to pedestrian- and traffic-related road events. Background: The rising popularity of AVs highlights the need for a deeper understanding of the factors that influence trust in AV. Trust is a crucial element, particularly because current AVs are only partially automated and may require manual takeover; miscalibrated trust could have an adverse effect on safe driver-vehicle interaction. However, before attempting to calibrate trust, it is vital to comprehend the factors that contribute to trust in automation. Methods: Thirty-six individuals participated in the experiment. Driving scenarios incorporated adaptive SAE Level 2 AV algorithms, driven by participants' event-based trust in AVs and preferences for AV driving styles. The study measured participants' trust, preferences, and the number of takeover behaviors. Results: Higher levels of trust and preference for more aggressive AV driving styles were found in response to pedestrian-related events compared to traffic-related events. Furthermore, drivers preferred the trust-based adaptive mode and had fewer takeover behaviors than the preference-based adaptive and fixed modes. Lastly, participants with higher trust in AVs favored more aggressive driving styles and made fewer takeover attempts. Conclusion: Adaptive AV interaction modes that depend on real-time event-based trust and event types may represent a promising approach to human-automation interaction in vehicles. Application: Findings from this study can support future driver- and situation-aware AVs that can adapt their behavior for improved driver-vehicle interaction. [ABSTRACT FROM AUTHOR]

Objective: Using brain haemodynamic responses to measure perceived risk from traffic complexity during automated driving. Background: Although well-established during manual driving, the effects of driver risk perception during automated driving remain unknown. The use of fNIRS in this paper for assessing drivers' states posits it could become a novel method for measuring risk perception. Methods: Twenty-three volunteers participated in an empirical driving simulator experiment with automated driving capability. Driving conditions involved suburban and urban scenarios with varying levels of traffic complexity, culminating in an unexpected hazardous event. Perceived risk was measured via fNIRS within the prefrontal cortical haemoglobin oxygenation and from self-reports. Results: Prefrontal cortical haemoglobin oxygenation levels significantly increased, following self-reported perceived risk and traffic complexity, particularly during the hazardous scenario. Conclusion: This paper has demonstrated that fNIRS is a valuable research tool for measuring variations in perceived risk from traffic complexity during highly automated driving. Even though the responsibility over the driving task is delegated to the automated system and dispositional trust is high, drivers perceive moderate risk when traffic complexity builds up gradually, reflected in a corresponding significant increase in blood oxygenation levels, with both subjective (self-reports) and objective (fNIRS) increasing further during the hazardous scenario. Application: Little is known regarding the effects of drivers' risk perception with automated driving. Building upon our experimental findings, future work can use fNIRS to investigate the mental processes for risk assessment and the effects of perceived risk on driving behaviours to promote the safe adoption of automated driving technology. [ABSTRACT FROM AUTHOR]

AbstractThe necessity of human supervision and intervention during autonomous driving has long been a topic of controversial discussion. From a developer’s perspective, it is expected that users will readily adapt to well-calibrated autonomous driving systems (ADS) due to their superior performance in dynamic driving tasks (DDT) compared to conventional human-driven vehicles. However, when passengers experience an autonomous vehicle (AV), there may be an adjustment period during which they modify their behavior to accommodate the driving patterns of the ADS. Additionally, some passengers might not adapt to autonomous driving at all, highlighting potential limitations in the current ADS development strategy. This work studies the dynamics of human-automation interaction and introduces an “objective method”, which employs a Virtual Reality (VR)-enabled simulation approach for in-depth behavioral analysis concerning riders’ behavioral adaptation to autonomous driving. Specifically, we examined how participants interacted with and intervened in Level 4 ADS operating under conservative, moderate, and aggressive driving patterns in a fully autonomous environment. A realistic urban road network was recreated in VR, integrated with traffic microsimulation to generate various driving scenarios. Twenty-seven participants completed driving tasks across different AV modes, with their intervention behaviors analyzed in relation to traffic conditions and AV aggressiveness. Key findings include: (1) Participants showed higher intention to intervene but lower actual intervention rates under aggressive AV modes compared to moderate and conservative modes, suggesting quicker adaptation to more challenging driving scenarios. (2) Interventions generally proved unnecessary and sometimes detrimental to overall traffic performance in a full-AV environment. (3) Aggressive AV modes significantly improved traffic efficiency, with a 40% increase in average travel speed and a 53% reduction in waiting time. However, human interventions posed the greatest challenge to achieving optimal traffic conditions. This research provides insights into the complex dynamics of human-AV interaction and adaptation, offering valuable implications for AV interface design, implementation strategies, and public acceptance of autonomous driving technologies. [ABSTRACT FROM AUTHOR]

The Ultimatum Game is an effective tool for understanding how social decision-making is influenced by emotions in both research and clinical settings. Previous findings have shown that the Ultimatum Game can evoke negative emotions, especially anger and aggression. In a sample of non-clinical adults (N = 143) we evaluated the sensitivity of an anger-infused version of the Ultimatum Game to individual differences in anger and irritability. Findings showed significant relationships between anger and aggressive behaviors in the Ultimatum game, but no association between irritability and aggressive behavior were observed. This indicates that the anger-infused Ultimatum Game is a promising method for studying individual differences in trait anger and anger expression. However, the relationship between decision-making in the anger-infused Ultimatum Game and irritability is less straight forward and needs further investigation. Therefore, when studying the behavioral responses of irritability, it would be beneficial to capture other behaviors beyond aggressive responses. [ABSTRACT FROM AUTHOR]

• Driving simulator variables form factors aligning with driving behavior theories. • Driving simulator variables factor into operational and tactical driving behaviors. • Operational driving is informed by simulator variables quantifying variability. • Tactical driving is most informed by simulator variables representing speed. Although driving simulators are powerful tools capable of measuring a wide-ranging set of tactical and operational level driving behaviors, comparing these behaviors across studies is problematic because there is no core set of driving variables to report when assessing driving behavior in simulated driving scenarios. To facilitate comparisons across studies, researchers need consistency in how driving simulator variables combine to assess driving behavior. With inter-study consistency, driving simulator research could support stronger conclusions about safe driving behaviors and more reliably identify future driver training goals. The purpose of the current study was to derive empirically and theoretically meaningful composite scores from driving behaviors of young people in a driving simulator, utilizing driving data from across a variety of driving environments and from within the individual driving environments. One hundred ninety adolescent participants aged 16 years or 18 years at enrollment provided demographic data and drove in a high-fidelity driving simulator. The simulated scenario included 4 distinct environments: Urban, Freeway, Residential, and a Car Following Task (CFT). A Principal Components Analysis (PCA) was conducted on the variable output from the driving simulator to select optimal factor solutions and loadings both across the multi-environmental drive and within the four individual driving environments. The PCA suggested two components from the multi-environmental simulated drive: vehicle control and speed. The individual driving environments also indicated two components: vehicle control and tactical judgment. These findings are among the first steps for identifying composite driving simulator variables to quantify theoretical conceptualizations of driving behavior. Currently, driving behavior and performance measured by driving simulators lack "gold standards" via driving scores or benchmarks. The composites derived in this analysis may be studied for further use where driving behavior standards are increasingly sought by clinicians and practitioners for a variety of populations, as well as by parents concerned about the readiness of their novice driving teen. [ABSTRACT FROM AUTHOR]

Aggressiveness is a concerning issue due to its contribution to road crashes. This study aims to provide an overview on how Spanish drivers perceive aggressive driving, analyzing how this perception is linked to sociodemographic features. Thus, a cross-sectional study was designed, where 1,079 Spanish drivers took part in a telephone survey on aggressiveness' importance, influence on traffic accidents, and frequency of performance. For data processing, Exploratory Data Analysis and Unifactorial Variance Analysis were conducted. The results showed that the perception of aggressive driving has a high valuation and a high-perceived risk, with differences for sex, age, and driving experience. The findings suggest that some groups perceive more aggressiveness than others, meaning it is necessary to develop measures to reduce biases through a gender perspective. [ABSTRACT FROM AUTHOR]

The prevalence of aggressive behavior in traffic represents a pressing issue in contemporary society. In recent times, there has been a notable surge in aggressive conduct among drivers, ranging from excessive honking to involvement in road accidents. The statistical data regarding accident rates in Romania and the Republic of Moldova underscore the necessity of investigating the underlying causes of this behavioral deviation among drivers and devising strategies for revention or intervention to mitigate this maladaptive behavior. This article presents the findings of an experimental study examining two variables that may influence the occurrence and expression of aggressive behavior among drivers in traffic: religious affiliation and level of driver education. The experimental hypothesis tested is as follows: We posit that statistically significant differences exist in the level of aggressive behavior exhibited in traffic—such as aggressive driving, dangerous driving, and risky driving—depending on the drivers’ level of education and their religious affiliation.

Autophagy, an intrinsic catabolic mechanism that eliminates misfolded proteins, dysfunctional organelles, and lipid droplets, plays a vital function in energy balance and cytoplasmic quality control, in addition to maintaining cellular homeostasis. Liver cancer such as hepatocellular carcinoma (HCC) is one of the most common causes of cancer deaths globally and shows resistance to several anticancer drugs. Despite the rising incidence and poor prognosis of malignant HCC, the underlying molecular mechanisms driving this aggressive cancer remain unclear. Several natural compounds, such as phytochemicals of dietary and non-dietary origin, affect hepatocarcinogenesis signaling pathways in vitro and in vivo, which may help prevent and treat HCC cells. Current HCC cells treatments include chemotherapy, radiation, and surgery. However, these standard therapies have substantial side effects, and combination therapy enhances side effects for an acceptable therapeutic benefit. Therefore, there is a need to develop treatment strategies for HCC cells that are more efficacious and have fewer adverse effects. Multiple genetic and epigenetic factors are responsible for the HCC cells to become resistant to standard treatment. Autophagy contributes to maintain cellular homeostasis, which activates autophagy for biosynthesis and mitochondrial regulation and recycling. Therefore, modifying autophagic signaling would present a promising opportunity to identify novel therapies to treat HCC cells resistant to current standard treatments. This comprehensive review illustrates how natural compounds demonstrate their anti-hepatocellular carcinoma function through autophagy. [ABSTRACT FROM AUTHOR]

This study aims to examine the differences in drivers' reaction time (RTs) while driving on horizontal curves and straight roadway segments, among different driver classes, and in different driving environments to better understand human driver behavior in typical car-following situations. Therefore, behavioral measures were extracted from naturalistic car-following trajectories to estimate the RT. The RT was estimated for two stimulus–response pairs, namely, the speed–gap and relative speed–acceleration pairs, by using the cross-classification method. The RT was estimated separately for each driver and aggregated based on location and based on driver class. The results reveal that drivers' RTs on curves are consistently higher than their RTs on straight segments, and this difference is statistically significant. The comparison between normal drivers and aggressive drivers indicates that regardless of the location, aggressive drivers have a significantly longer RT than normal drivers, as aggressive drivers can accept closer gaps and higher relative speed. Also, cautious drivers have a longer RT compared with normal drivers; however, the difference is not significant in most cases. Furthermore, cautious and normal drivers have longer RTs on curves compared with their RTs on straight segments. Additionally, the RT on rural horizontal curves is longer than the RT on urban curves, yet the differences are insignificant. [ABSTRACT FROM AUTHOR]

Drivers with aggressive driving style driving electric city buses with rapid response and high acceleration performance characteristics are more prone to have traffic accidents in the starting stage. It is of great importance to accurately identify the drivers with aggressive driving style for preventing traffic accidents of city buses. In this article, a starting driving style recognition method of electric city bus is firstly proposed based on deep learning with in-vehicle Controller Area Network (CAN) bus data. The proposed model can automatically extract the deep spatiotemporal features of multi-channel time series data and achieve end-to-end data processing with higher accuracy and generalization ability. The sample data set of driving style is established by pre-processing the collected in-vehicle CAN bus data including the status of driving and vehicle motion, the data pre-processing method includes data cleaning, normalization and sample segmentation. Data set is labelled with subjective evaluation method. The starting driving style recognition method based on Convolutional Neural Network (CNN) model is constructed. Multiple sets of convolutional layers and pooling layers are used to automatically extract the spatiotemporal characteristics of starting driving style hidden in the data such as velocity and pedal position etc. The fully connected neural network and incentive function Softmax are applied to establish the relationship mapping between driving data characteristics and the starting driving styles, which are categorized as cautious, normal and aggressive. The results show that the proposed model can accurately recognize the starting driving style of electric city bus drivers with an accuracy of 98.3%. In addition, the impact of different model structures on model performance such as accuracy and F1 scores was discussed, and the performance of the proposed model was also compared with Support Vector Machine (SVM) and random forest model. The method can be used to accurately identify drivers with aggressive starting driving style and provide references for driver's safety education, so as to prevent accidents at the starting stage of electric city bus and reduce crash accidents. [ABSTRACT FROM AUTHOR]

This study employs a comparative analytical framework to enhance our understanding of the conducive opportunity structures that foster emotionally charged political discourse. We examined 175,539 Facebook posts characterized by variations in content (in terms of themes and populist rhetoric), authorship (including populist politicians, traditional news, and alternative media), and geographic context (Belgium, France, Germany, Switzerland, UK, US). We then analyzed 360,000 emotional responses from Facebook users to determine which posts create the most conducive conditions for eliciting angry emotions. Our key findings show that posts from alternative and hyperpartisan media, as well as those from populist politicians and parties, tend to elicit elevated levels of angry reactions. These posts often use anti-elitist and exclusionary language. This finding has significant implications, as the anger generated by such accounts can propagate incivility and polarization and facilitate the spread of ideologically driven misinformation. A particular case is Donald Trump, who, as a populist governing figure, manages to elicit positive emotions, including "love," despite delivering seemingly antagonistic messages. To strengthen the robustness of our findings, we conducted a replication analysis with 67,620 Facebook posts from three of the six countries and examined two different time periods. This analysis confirmed the persistence of our findings over time. Our opportunity-structure framework offers valuable insights for designing targeted strategies to improve the quality of public discourse and promote informed and constructive political engagement in diverse societies. [ABSTRACT FROM AUTHOR]

Objective: This study investigated the impact of driving styles of drivers and automated vehicles (AVs) on drivers' perception of automated driving maneuvers and quantified the relationships among drivers' perception of AV maneuvers, driver trust, and acceptance of AVs. Background: Previous studies on automated driving styles focused on the impact of AV's global driving style on driver's attitude and driving performance. However, research on drivers' perception of automated driving maneuvers at the specific driving style level is still lacking. Method: Sixteen aggressive drivers and sixteen defensive drivers were recruited to experience twelve driving scenarios in either an aggressive AV or a defensive AV on the driving simulator. Their perception of AV maneuvers, trust, and acceptance was measured via questionnaires, and driving performance was collected via the driving simulator. Results: Results revealed that drivers' trust and acceptance of AVs would decrease significantly if they perceived AVs to have a higher speed, larger deceleration, smaller deceleration, or shorter stopping distance than expected. Moreover, defensive drivers perceived significantly greater inappropriateness of these maneuvers from aggressive AVs than defensive AVs, whereas aggressive drivers didn't differ significantly in their perceived inappropriateness of these maneuvers with different driving styles. Conclusion: The driving styles of automated vehicles and drivers influenced drivers' perception of automated driving maneuvers, which influence their trust and acceptance of AVs. Application: This study suggested that the design of AVs should consider drivers' perceptions of automated driving maneuvers to avoid undermining drivers' trust and acceptance of AVs. [ABSTRACT FROM AUTHOR]

• Trait of aggression and aggressive driving behaviors are positively correlated. • SIP deficits in daily and driving contexts are positively and strongly correlated. • Individuals attribute significantly more hostility in driving than in daily contexts. • Self-efficacy as the most important SIP to act aggressively. Questions such as "Are people always aggressive?" or "Is it only inside the car?" have been studied in previous research. However, the focus is mostly on anger and anger expression and not on an interpersonal perspective. The current study aims to explore this gap in the research by analyzing aggressive driving behavior through the application of Social Information Processing Theory (SIP). For that purpose, the trait of aggression was compared with aggressive driving behavior and SIP patterns on and off the road. To achieve these goals, 562 Portuguese drivers completed a self-reported survey concerning trait aggression, aggressive driving behavior, and social information processing in various contexts. The results indicated that deficits in SIP positively correlate with aggressive behavior in daily and driving contexts. Secondly, the trait of aggression is positively correlated with aggressive driving behavior. Finally, SIP deficits in the daily context were also positively correlated with SIP deficits in the driving context. Nevertheless, significant differences were found between SIP variables in the daily context and the driving context. The results, implications, and limitations of the study are discussed. [ABSTRACT FROM AUTHOR]

• Traffic climate affect safety driving. • Psychological factors play mediating roles. In the research field of safe driving behavior, the emergence of prosocial driving research complements the more traditional dangerous driving research. In recent years, studies on the relationship between the urban traffic climate and prosocial driving have even been taken as a new direction for further improving urban traffic safety based on the construction of traffic safety facilities and the development of traffic safety regulations. The mechanism by which urban traffic climate affects prosocial driving is explored in this study. From a dual research perspective, i.e., a variable-centered and person-centered perspective, a theoretical model of prosocial driving is constructed based on the following three levels: environmental, psychological and behavioral. According to the results, First, rational, affective and moral factors at the individual psychological level play mediating roles between urban traffic climate at the environmental level and prosocial driving behavior at the behavioral level. As indicated by the results of a mediating pathway analysis, rational, affective and moral factors are intricately linked by five complex mediating paths at the psychological level. Second, there is a differentiated environmental-psychological activation mechanism between individuals' prosocial driving and aggressive driving. Third, urban traffic climate consists of a variety of typical safety climate types, and individuals have diverse psychological tendencies regarding safe driving in traffic climates of varying safety levels. The study concludes with a systematic discussion of its theoretical contributions and practical value. [ABSTRACT FROM AUTHOR]

• Problematic use patterns were related to psychological dysfunction and risky driving. • Frequent cannabis users were more problematic than frequent alcohol users. • Dependency predicted risky driving behaviour via psychological dysfunctioning. • Dependency influenced driving behaviours while controlling for DUI frequency. • Self-regulation is an intervention point for problem substance use and risky driving. Problematic alcohol and cannabis use has been linked with psychological dysfunction and engagement in risky behaviour. This study aimed to investigate the relationships between alcohol and cannabis use patterns with self-regulatory dysfunction (i.e., worry; anger rumination; mindlessness; impulsivity), psychopathology (i.e., anxiety; depression; anger), and risky driving styles (i.e., anxious; dissociative; angry; reckless driving). An online survey was administered to three unique samples (n = 200 each) of Australian adults: frequent alcohol users, frequent cannabis users, and non-frequent substance users (i.e., the control group). Eligible participants did not use any other substance more than once per month. Comparative analysis (MANOVA) showed that frequent cannabis users had the highest self-reported psychological dysfunctioning and risky driving styles, followed by frequent alcohol users, and then the control group. Bivariate correlations revealed that most prominently, the severity of substance dependency and the frequency of driving under the influence (DUI) of alcohol and cannabis were positively related to specific forms of psychological dysfunctioning and risky driving styles. Such findings highlight the potential relevance of both chronic and acute influences on driving behaviour. Finally, four separate path analyses were implemented which predicted specific risky driving styles (e.g., anxious driving), with substance use dependency as the moderator, and the corresponding self-regulatory dysfunction (e.g., worry) and psychopathology (e.g., anxiety) variables as mediators. The results suggested that substance use dependency indirectly affected risky driving styles, through its influence on specific self-regulatory dysfunction, and psychopathology, whilst controlling for the effects of DUI. Together these findings highlight the increased road safety risks associated with problematic alcohol and cannabis use, outside of acute-related influences. [ABSTRACT FROM AUTHOR]

In recent years, the utilization of intelligent transportation systems (ITS) has been significantly augmented due to advancements in the fields of computer science and information technology. Connected and autonomous vehicle (CAV) technology has gained a significant amount of attention in the research community. The advancements in connected and autonomous vehicle technology are expected to have an influence on the current transportation networks in the near future. The main purpose of this study is to investigate the influence of different driving behaviors of CAVs, namely defensive, normal, and aggressive, on a grid urban network consisting of unsignalized intersections in terms of traffic efficiency, traffic emissions, fuel consumption, and traffic safety by utilizing an open-source simulation tool called SUMO. Three different demand configurations and varying penetration rates of different behaviors for CAVs were taken into account in the scope of this study. According to the findings of the study, while CAVs with defensive behavior lead to an increase in average time loss, CAVs with aggressive behavior reduce the average time loss with increased penetration rates. The reduction in vehicle emissions and fuel consumption is not valid for all driving behaviors of CAVs. It was observed that it depends on the driving behavior of CAVs, penetration rates, and demand configurations. In terms of traffic safety, the worst performance was obtained by CAV-aggressive behavior. Furthermore, it was found that there is a trade-off between traffic efficiency and traffic safety metrics regarding the driving behavior of CAVs. [ABSTRACT FROM AUTHOR]

This study delves into the effects of autonomous vehicle behaviors on travel times and vehicle arrivals along the Kirkuk-Sulaymaniyah Highway, employing simulations spanning 3600, 5400, and 7200 seconds. Across varied traffic volumes ranging from 350 to 950 vehicles and autonomous vehicle behaviors categorized as cautious, normal, aggressive, aggressive platoons, and a mix alongside human-driven vehicles, the research unveils significant findings. Results highlight substantial reductions in average travel times and heightened vehicle arrivals among autonomous vehicles, particularly those exhibiting aggressive behaviors, compared to their human-driven counterparts. Across all simulation scenarios, aggressive autonomous vehicles consistently demonstrate superior performance, showcasing potential efficiency gains through aggressive driving algorithms. Furthermore, with increasing traffic volume, the advantages of aggressive autonomous behaviors become more pronounced, suggesting their adaptability to congested conditions. However, safety implications and traffic flow dynamics warrant caution, especially in scenarios with high volumes and aggressive behaviors. These insights underscore the importance of further research and policy considerations to leverage the full potential of autonomous vehicles while ensuring safety and efficiency on highways. [ABSTRACT FROM AUTHOR]

Traffic collisions are the seventh leading cause of death in Ecuador, with reckless driving being one of the main causes. Although there are statistical data on traffic crashes, there has not yet been a comprehensive investigation of the causes. Therefore, the main objective of this study is to investigate unsafe driving behavior using a modified version of the Spanish Driving Behavior Questionnaire (SDBQ) adapted for Ecuador. The 34-item SDBQ we used has four main dimensions: lapses, errors, violations, and aggressive driving. To apply the SDBQ, a stratified random probability sample of 470 drivers with valid driver's licenses aged 18–69 was used. Of the drivers, 68.8% were male, while 33.2% were female. We used a chi-square test and descriptive statistics to analyze the data for the SDBQ application items. Finally, four generalized linear Poisson models were used. The results show that taxi drivers have the highest scores on three of the four main dimensions of the SDBQ and male drivers are more likely than female drivers to cause traffic accidents. Drivers are also more likely to cause traffic accidents if they drive more hours per day. This research is the first of its kind to analyze driver behavior-based solutions in Ecuador to reduce traffic accidents. The error factor is the most critical outcome of dangerous behavior in the city of Cuenca. The SDBQ aims to foster a culture of safety and sustainability by promoting road safety measures through legislation and traffic regulations. [ABSTRACT FROM AUTHOR]

CARD-BCL10-MALT1 (CBM) signalosomes connect distal signaling of innate and adaptive immune receptors to proximal signaling pathways and immune activation. Four CARD scaffold proteins (CARD9, 10, 11, 14) can form seeds that nucleate the assembly of BCL10-MALT1 filaments in a cell- and stimulus-specific manner. MALT1 (also known as PCASP1) serves a dual function within the assembled CBM complexes. By recruiting TRAF6, MALT1 acts as a molecular scaffold that initiates IkB kinase (IKK)/NF-kB and c-Jun N-terminal kinase (JNK)/AP-1 signaling. In parallel, proximity-induced dimerization of the paracaspase domain activates the MALT1 protease which exerts its function by cleaving a set of specific substrates. While complete MALT1 ablation leads to immune deficiency, selective destruction of either scaffolding or protease function provokes autoimmune inflammation. Thus, balanced MALT1-TRAF6 recruitment and MALT1 substrate cleavage are critical to maintain immune homeostasis and to promote optimal immune activation. Further, MALT1 protease activity drives the survival of aggressive lymphomas and other nonhematologic solid cancers. However, little is known about the relevance of the cleavage of individual substrates for the pathophysiological functions of MALT1. Unbiased serendipity, screening and computational predictions have identified and validated ~20 substrates, indicating that MALT1 targets a quite distinct set of proteins. Known substrates are involved in CBM auto-regulation (MALT1, BCL10 and CARD10), regulation of signaling and adhesion (A20, CYLD, HOIL-1 and Tensin-3), or transcription (RelB) and mRNA stability/translation (Regnase-1, Roquin-1/2 and N4BP1), indicating that MALT1 often targets multiple proteins involved in similar cellular processes. Here, we will summarize what is known about the fate and functions of individual MALT1 substrates and how their cleavage contributes to the biological functions of the MALT1 protease. We will outline what is needed to better connect critical pathophysiological roles of the MALT1 protease with the cleavage of distinct substrates. [ABSTRACT FROM AUTHOR]