Intestinal microbiota homeostasis analysis in riboflavin-treated alcoholic liver disease.

Alcoholic liver disease (ALD) is a disease with high incidence, limited therapies, and poor prognosis. The present study aims to investigate the effect of riboflavin on ALD and explore its potential therapeutic mechanisms. C57BL/6 mice were divided into the control, alcohol, and alcohol+ riboflavin groups. 16S rRNA-seq and RNA-seq analysis were utilized to analyze the polymorphism of intestinal microbiota and the transcriptome heterogeneity respectively. KEGG and GO enrichment analysis were performed. CIBERSORTx was applied to evaluate the immune cell infiltration level. Publicly available transcriptome data of ALD was enrolled and combined with the RNA-seq data to identify the immune subtypes of ALD. Pathological and histology analysis demonstrated that riboflavin reversed the progression of ALD. 16S rRNA-seq results showed that riboflavin could regulate alcohol-induced intestinal microbiota alteration. Intestinal microbiota polymorphism analysis indicated that VLIDP may contribute to the progression of ALD. Based on the VLIDP pathway, two subtypes were identified. Immune microenvironment analysis indicated that the upregulated inflammatory factors may be important regulators of ALD. In conclusion, intestinal microbiota homeostasis was associated with the protective effect of riboflavin against ALD, which was likely mediated by modulating inflammatory cell infiltration. Riboflavin emerges as a promising therapeutic candidate for the management of ALD.

Multi-level time-varying causality analysis of secondary conflicts based on hazard-based duration models.

Secondary conflicts occur frequently and would cause multi-vehicle collisions. In order to prevent multi-vehicle collisions, a better understanding of the factors that affect secondary conflict propagation is crucial. Previous studies have identified the influencing factors of primary conflicts' occurrence, but have not explored the time-varying factors that affect secondary conflicts' propagation. In addressing this gap, about 20,000 secondary conflicts are extracted from real trajectory dataset, and a multi-level variable system is established, including segment types, traffic status, front chain conflict status, and direct interaction behaviors. Further, a Kaplan-Meyer model and a random parameters hazard-based duration model are constructed to explore the single-factor and multiple-factor influence on the propagation of secondary conflicts, respectively. The results suggest that the first 2.6 s after a conflict is a critical post-monitoring period to prevent the secondary conflicts propagation. In addition, diverging and merging segments shorten the survival time of secondary conflicts by about 12%, indicating a higher occurrence probability of secondary conflicts near the ramps of expressways. More importantly, the front chain conflict status and the front direct conflict status reveal a different effect on the secondary conflicts. The high risk of chain conflict ahead would increase the occurrence probability of secondary conflicts, while the high risk of front conflict would decrease the probability. Overall, this research is of great significance to understand the influencing factors of secondary conflict and avoid secondary crashes.

Rapid online assessment of reading and phonological awareness (ROAR-PA).

Phonological awareness (PA) is at the foundation of reading development: PA is introduced before formal reading instruction, predicts reading development, is a target for early intervention, and is a core mechanism in dyslexia. Conventional approaches to assessing PA are time-consuming and resource intensive: assessments are individually administered and scoring verbal responses is challenging and subjective. Therefore, we introduce a rapid, automated, online measure of PA-The Rapid Online Assessment of Reading-Phonological Awareness-that can be implemented at scale without a test administrator. We explored whether this gamified, online task is an accurate and reliable measure of PA and predicts reading development. We found high correlations with standardized measures of PA (CTOPP-2, r = .80) for children from Pre-K through fourth grade and exceptional reliability (α = .96). Validation in 50 first and second grade classrooms showed reliable implementation in a public school setting with predictive value of future reading development.

Accelerated failure time modeling of in-lane street hawkers' lane entry and exit behaviors at signalized intersections.

In-lane street hawking is the intermittent entry of signalized intersections by traders to sell groceries to drivers and passengers. Studies have shown that hawkers get exposed to traffic injuries but the lack of quantitative analysis of their lane entry and exit behaviors in signalized intersections makes it difficult to improve traffic safety. This study analyzes the significant predictors of in-lane street hawkers' (1) lane entry within 30 s after the red signal illumination, (2) lane exit within 30 s after the green signal illumination, and (3) probability of getting injuries during the green signal time. Drone-based trajectory data were collected from a selected signalized intersection in Accra, Ghana. A Weibull accelerated failure time duration model incorporating Gamma frailty was used to evaluate hawkers' behaviors. Overall, the majority of hawkers exhibited red-light running behaviors exposing them to traffic injuries. An increase in traffic speed, especially beyond 20 km/h, exposed hawkers to injury risks significantly. Notably, hawkers' lane entry decreased significantly as the traffic speed increased. Their lane exit duration was significantly predicted by the queue lengths and traffic volumes. Accordingly, safety practitioners can enhance traffic regulation and control methods in addition to pro-poor social interventions to demotivate hawking at signalized intersections.

Characteristics identification and evolution patterns analyses of road chain conflicts.

Chain conflicts would cause chain-reaction crashes, which might result in elevated fatality rates. Chain conflicts describe a phenomenon wherein evasive actions taken by a following vehicle's driver after a conflict impact nearby vehicles, which occur frequently but are reported less often. To effectively reduce conflict risk, comprehending the evolution patterns of chain conflicts under varied traffic conditions and road segments is crucial, in order to make chain conflicts management strategies. Initially, rear-end or sideswipe conflicts between two vehicles are identified based on vehicle trajectory data captured by an unmanned aerial vehicle group. Subsequently, a chain conflict identification algorithm is proposed, considering the randomness of occurrence time and fluctuation of impact duration, to link individual conflicts. Chain conflict rates exhibit significant variations across different road segments under diverse traffic conditions. Multiple risk and propagation indicators are extracted to unveil latent characteristics of chain conflicts from a high-level perspective. Based on prominent characteristic disparities, three evolution patterns are identified, i.e., Longitudinal Risk Decrease Pattern, Longitudinal Risk Increase Pattern, and Comprehensive High-risk Persistent Pattern. Spatial-temporal high-risk areas associated with each pattern are determined, and transition probabilities between patterns are calculated. The results indicate that these patterns tend to remain stable, with transitions mainly occurring from low-risk to high-risk patterns. Moreover, strategies to reduce conflict risk are proposed based on the characteristics of different patterns. This study holds great significance in understanding chain conflict evolution patterns and preventing chain-reaction crashes.

Mapping pedestrian safety studies between 2010 and 2021: A scientometric analysis.

Pedestrian deaths constitute 23% of road traffic deaths globally. Although several research papers have contributed to pedestrian safety analysis, they did not provide a comprehensive overview of the progress in the research domain and publication trends. This makes it difficult to identify trends and insights into the pedestrian research domain in light of the voluminous number of papers. This study fills this gap with a scientometric analysis of research on pedestrian safety analysis indexed in the Web of Science. The scope covers 2594 papers published between 2010 and 2021 in English. This study analyzed the annual publications and citation trends, top ten most cited papers, influential papers in their first three years after publication, contributing authors, funding agencies, and contributing journals. The regional gaps between the proportion of pedestrian deaths and research were also analyzed. The results showed low research productivity from low and middle-income countries although they have a high incidence of pedestrian deaths. Subsequently, the main keyword clusters or frontier topics were identified and topic analysis was employed to identify the evolution of studies. Four keyword clusters were identified, i.e., "vehicle-to-pedestrian crash and injury severity analysis", "pedestrian movement and decision simulation experiments", "improving the vehicle system towards reducing body region impact injuries", "pedestrian behavior in crosswalks and signalized intersections". This study contributes an integrated knowledge map of pedestrian safety analysis, publication trends, the evolution of studies, and under-researched topics to guide future research work in pedestrian safety analysis.

Real-time safety analysis for expressways considering the heterogeneity of different segment types.

INTRODUCTION: Studies have proven that the crash possibility and crash type are not the same among different expressway segment types. However, few studies have conducted real-time safety analysis considering different segment types. This study aimed to explore the crash mechanism's heterogeneity for different segment types (i.e., merge, diverge, weaving, and basic segments). METHOD: To enable in-depth exploration, this study used detailed traffic data, which were 0-10 min before crash, at 1-min intervals, and from five detectors of both the upstream and downstream to the target segment. This study analyzed the crash mechanism's heterogeneity from the following aspects: crash characteristics, significant crash contributing variables, and variables' importance. Based on this, a variables selection method was proposed to solve the huge dimension scale in modeling. Then, a nested logit model was built, which could consider the crash mechanism's heterogeneity, to quantitatively analyze the impact of crash contributing factors on the crash risk. RESULTS: The results revealed that there are statistically significant differences in crash characteristics between each segment type. Additionally, the sources of most crash contributing factors were found to be significantly different in the spatial-temporal dimension between each segment type. Moreover, this study found that the weather parameter, indicating pavement's wet condition, had a similar effect on crash risk between different segment types. However, the geometry and traffic parameters had significantly different impacts between different segment types. Moreover, when the number of target segments' upstream ramps increases or when the distance between ramps and the target segment decreases, the crash risk would increase. Practical Applications: This study can be applied in the intelligent transportation system to improve traffic safety performance, especially in active traffic management systems.

Active traffic management strategies for expressways based on crash risk prediction of moving vehicle groups.

Active traffic management (ATM) strategies are useful methods to reduce crash risk and improve safety on expressways. Although there are some studies on ATM strategies, few studies take the moving vehicle group as the object of analysis. Based on the crash risk prediction of moving vehicle groups in a connected vehicle (CV) environment, this study developed various ATM safety strategies, that is, variable speed limits (VSLs), ramp metering (RM), and coordinated VSL and RM (VSL-RM) strategies. VSLs were updated to minimize the crash risk of multiple moving vehicle groups in the next time interval, which is 1 min, and the updated speed limits were sent directly to the CVs in the moving vehicle group. The metering rate and RM opening time were determined using mainline occupancy, the crash risk of upcoming moving vehicle groups, and the predicted time at which moving vehicle groups arrived at the on-ramp. The VSL-RM strategy was used to simultaneously control and coordinate traffic flow on the mainline and ramps. These strategies were tested in a well-calibrated and validated micro-simulation network. The crash risk index and conflict count were utilized to evaluate the safety effects of these strategies. The results indicate that the ATM strategies improved the expressway safety benefits by 2.84-15.92%. The increase in CV penetration rate would promote the safety benefits of VSL and VSL-RM. Moreover, VSL-RM was superior to VSL and RM in reducing crash risk and conflict count.

How many crashes can connected vehicle and automated vehicle technologies prevent: A meta-analysis.

The connected and automated vehicle (CAV) technologies have made great progresses. It has been commonly accepted that CV or AV technologies would reduce human errors in driving and benefit traffic safety. However, the answer of how many crashes can be prevented because of CV or AV technologies has not reached a consistent conclusion. In order to quantitatively answer this question, this study used meta-analysis to evaluate the safety effectiveness of nine common and important CV or AV technologies, and tested the safety effectiveness of these technologies for six countries. First, 73 studies about the safety impact of CV or AV technologies were filtered out from 826 CAV-related papers or reports. Second, the safety impacts of these technologies with regard to assistant types and triggering times have been compared. It shows AV technologies can play a more significant role than CV technologies, and the technologies with closer triggering time to collision time have greater safety effectiveness. Third, in the meta-analysis, the random effect model was used to evaluate the safety effectiveness, and the funnel plots and trim-and-fill method were used to evaluate and adjust publication bias, so as to objectively evaluate the safety effectiveness of each technology. Then, according to the crash data of six countries, the comprehensive safety effectiveness and compilation of safety effectiveness of the above technologies were calculated. The results show that if all of technologies were implemented in the six countries, the average number of crashes could be reduced by 3.40 million, among which the India would reduce the most (54.24%). Additionally, different countries should develop different development strategies, e.g., USA should prioritize the development of the lane change warning and intersection warning, the UK should prioritize applications related to intersection warning and rear-end warning. Overall, this study provides comprehensive and quantitative understating of the safety effectiveness of CA or AV technologies and would contribute to government, vehicle companies, and agencies in deciding the development priority of CA or AV technologies.