Understanding risky driving behaviors among young novice drivers in Nigeria: A latent class analysis coupled with association rule mining approach.

Traffic crashes are significant public health concern in Nigeria, particularly among young drivers. The study aims to explore the underlying pattern of risky driving behaviors and the associations with demographic factors among young drivers in Nigeria. A combined approach of Latent Class Analysis (LCA) and Association Rule Mining is applied to the dataset comprising responses from 684 young drivers who complete the "Behavior of Young Novice Drivers Scale" (BYND) questionnaires. The LCA identifies four distinct classes of drivers based on the risky behavior profiles: Reckless-Speedsters, Cautious Drivers, Distracted Multitaskers, and Emotion-impacted Drivers. Association rule mining further connects these driver classes to demographic and driving history variables, uncovering intriguing insights. Reckless-Speedsters predominantly consist of young males who engage in riskier driving behaviors, including exceeding speed limits and disregarding traffic rules. Conversely, Cautious Drivers, also predominantly young males, exhibit a safer driving profile marked by rule adherence and a notably lower crash rate. Distracted Multitaskers, sharing a demographic profile with Cautious Drivers, diverge significantly due to their higher crash involvement, hinting at a propensity for distracted driving practices. Lastly, Emotion-Impacted Drivers, primarily comprising young employed males, display behaviors influenced by emotions, shorter driving distances, and prior unsupervised driving experience. Most of the behaviors are attributed to inadequate traffic control, absence of traffic signs in most of the roads, preferential treatment, and lack of strict law enforcement in the country. The findings hold substantial implications for road safety interventions in Nigeria, urging targeted approaches to address the unique challenges presented by each driver class. With acknowledging the study limitations and advocating for future research in objective measures and emotion-behavior interactions, the comprehensive approach provides a robust foundation for enhancing road safety in the Nigerian context.

Pericytes contribute to pulmonary vascular remodeling via HIF2α signaling.

Vascular remodeling is the process of structural alteration and cell rearrangement of blood vessels in response to injury and is the cause of many of the world's most afflicted cardiovascular conditions, including pulmonary arterial hypertension (PAH). Many studies have focused on the effects of vascular endothelial cells and smooth muscle cells (SMCs) during vascular remodeling, but pericytes, an indispensable cell population residing largely in capillaries, are ignored in this maladaptive process. Here, we report that hypoxia-inducible factor 2α (HIF2α) expression is increased in the lung tissues of PAH patients, and HIF2α overexpressed pericytes result in greater contractility and an impaired endothelial-pericyte interaction. Using single-cell RNAseq and hypoxia-induced pulmonary hypertension (PH) models, we show that HIF2α is a major molecular regulator for the transformation of pericytes into SMC-like cells. Pericyte-selective HIF2α overexpression in mice exacerbates PH and right ventricular hypertrophy. Temporal cellular lineage tracing shows that HIF2α overexpressing reporter NG2+ cells (pericyte-selective) relocate from capillaries to arterioles and co-express SMA. This novel insight into the crucial role of NG2+ pericytes in pulmonary vascular remodeling via HIF2α signaling suggests a potential drug target for PH.

Who might encounter hard-braking while speeding? Analysis for regular speeders using low-frequency taxi trajectories on arterial roads and explainable AI.

Regular speeders are those who commit speeding recidivism during a period. Among their speeding behaviors, some occurring in specific scenarios may cause more hazards to road users. Therefore, there is a need to evaluate the driving risks if the regular speeders have different speeding propensities. This study considers speeding-related hard-braking events (SHEs) as a safety surrogate measure and recognizes the regular speeders who encounter at least one SHEs during the study period as risky individuals. To identify speeding behaviors and hard-braking events from low-frequency GPS trajectories, we compare the average travel speed between pairwise adjacent GPS points to the posted speed limit and examine the speed curve and the corresponding travel distance between these GPS points, respectively. Thereafter, a logistic model, XGBoost, and three 1D Convolutional Neural Networks (CNNs) including AlexNet CNN, Mini-AlexNet CNN, and Simple CNN are respectively developed to recognize the regular speeders who encountered SHEs based on their speeding propensities. The proposed Mini-AlexNet CNN achieves a global F1-score of 91% and recall of 90% on the testing data, which are superior to other models. Further, the study uses the Shapley Additive exPlanation (SHAP) framework to visually interpret the contribution of speeding propensities on SHE likelihood. It is found that speeding by 50% or greater for no more than 285 m is the most dangerous kind among all the speeding behaviors. Speeding on roads without bicycle lanes or on roads with roadside parking and excessive accesses increases the probability of encountering SHEs. Based on the analyses, we put forward tailored recommendations that aim to restrict hazard-related speeding behaviors rather than speeding behaviors of all kinds.

Addressing endogeneity between hazardous actions and motorcyclist injury severity by integrating generalized propensity score approach and instrumental variable model.

Motorcyclist hazardous actions (e.g., particularly speed too fast or failing to stop in assured clear distance (ACD)) are commonly identified as risk factors that significantly impact the motorcyclist injury severity. However, endogenous effects resulting from motorcyclist hazardous actions have seldom been considered, which may cause the biased estimates. Specifically, two important sources of endogeneities (i.e., endogeneity arising from observed confounding factors and endogeneity caused by unobserved confounders) tend to yield a biased relationship between hazardous actions and motorcyclist injury severity. To jointly account for two sources of endogeneities and provide more robust estimates, the study tries to assess the effects of speed-too-fast and failing to stop in ACD on motorcyclist injury severity via a hybrid method by integrating the generalized propensity score approach with instrumental variable model. Specifically, we adopt a generalized propensity score matching method to reduce the endogeneity bias arising from observed confounders. Furthermore, the matched data are used to develop an instrumental variable model with random parameters to handle the endogeneity resulting from unobserved confounders and unobserved heterogeneity, which consists of random parameters binary logit models modelling the motorcyclist hazardous actions in the first stage and a random parameters logit model with heterogeneity in means modelling the motorcyclist injury severity in the second stage. The proposed approach is estimated based on Michigan motorcycle crash data from 2015 to 2018. Results suggest that alcohol use leads motorcyclists to engage in speed-too-fast, while alcohol use and signal control cause motorcyclists to be involved in failing to stop in ACD. Middle-aged and elderly motorcyclists, alcohol use, speed too fast, speed limit ≥50 mph, wet surface, and head-on/angle crashes significantly increase the injury severity of motorcyclists. Moreover, failing to stop in ACD produces a random parameter with heterogeneity in means, while intersection increases the mean effects of failing to stop in ACD on motorcyclist minor injury. These findings further provide insights for a better understanding of hazardous actions and motorcyclist injury severity via the impact analysis of various explanatory variables.

Abnormal Lymphatic Sphingosine-1-Phosphate Signaling Aggravates Lymphatic Dysfunction and Tissue Inflammation.

BACKGROUND: Lymphedema is a global health problem with no effective drug treatment. Enhanced T-cell immunity and abnormal lymphatic endothelial cell (LEC) signaling are promising therapeutic targets for this condition. Sphingosine-1-phosphate (S1P) mediates a key signaling pathway required for normal LEC function, and altered S1P signaling in LECs could lead to lymphatic disease and pathogenic T-cell activation. Characterizing this biology is relevant for developing much needed therapies. METHODS: Human and mouse lymphedema was studied. Lymphedema was induced in mice by surgically ligating the tail lymphatics. Lymphedematous dermal tissue was assessed for S1P signaling. To verify the role of altered S1P signaling effects in lymphatic cells, LEC-specific S1pr1-deficient (S1pr1LECKO) mice were generated. Disease progression was quantified by tail-volumetric and -histopathologic measurements over time. LECs from mice and humans, with S1P signaling inhibition, were then cocultured with CD4 T cells, followed by an analysis of CD4 T-cell activation and pathway signaling. Last, animals were treated with a monoclonal antibody specific to P-selectin to assess its efficacy in reducing lymphedema and T-cell activation. RESULTS: Human and experimental lymphedema tissues exhibited decreased LEC S1P signaling through S1P receptor 1 (S1PR1). LEC S1pr1 loss-of-function exacerbated lymphatic vascular insufficiency, tail swelling, and increased CD4 T-cell infiltration in mouse lymphedema. LECs, isolated from S1pr1LECKO mice and cocultured with CD4 T cells, resulted in augmented lymphocyte differentiation. Inhibiting S1PR1 signaling in human dermal LECs promoted T-helper type 1 and 2 (Th1 and Th2) cell differentiation through direct cell contact with lymphocytes. Human dermal LECs with dampened S1P signaling exhibited enhanced P-selectin, an important cell adhesion molecule expressed on activated vascular cells. In vitro, P-selectin blockade reduced the activation and differentiation of Th cells cocultured with shS1PR1-treated human dermal LECs. P-selectin-directed antibody treatment improved tail swelling and reduced Th1/Th2 immune responses in mouse lymphedema. CONCLUSIONS: This study suggests that reduction of the LEC S1P signaling aggravates lymphedema by enhancing LEC adhesion and amplifying pathogenic CD4 T-cell responses. P-selectin inhibitors are suggested as a possible treatment for this pervasive condition.

Abnormal lymphatic S1P signaling aggravates lymphatic dysfunction and tissue inflammation.

BACKGROUND: Lymphedema is a global health problem with no effective drug treatment. Enhanced T cell immunity and abnormal lymphatic endothelial cell (LEC) signaling are promising therapeutic targets for this condition. Sphingosine-1-phosphate (S1P) mediates a key signaling pathway required for normal LEC function, and altered S1P signaling in LECs could lead to lymphatic disease and pathogenic T cell activation. Characterizing this biology is relevant for developing much-needed therapies. METHODS: Human and mouse lymphedema was studied. Lymphedema was induced in mice by surgically ligating the tail lymphatics. Lymphedematous dermal tissue was assessed for S1P signaling. To verify the role of altered S1P signaling effects in lymphatic cells, LEC-specific S1pr1 -deficient ( S1pr1 LECKO ) mice were generated. Disease progression was quantified by tail-volumetric and -histopathological measurements over time. LECs from mice and humans, with S1P signaling inhibition, were then co-cultured with CD4 T cells, followed by an analysis of CD4 T cell activation and pathway signaling. Finally, animals were treated with a monoclonal antibody specific to P-selectin to assess its efficacy in reducing lymphedema and T cell activation. RESULTS: Human and experimental lymphedema tissues exhibited decreased LEC S1P signaling through S1PR1. LEC S1pr1 loss-of-function exacerbated lymphatic vascular insufficiency, tail swelling, and increased CD4 T cell infiltration in mouse lymphedema. LECs, isolated from S1pr1 LECKO mice and co-cultured with CD4 T cells, resulted in augmented lymphocyte differentiation. Inhibiting S1PR1 signaling in human dermal LECs (HDLECs) promoted T helper type 1 and 2 (Th1 and Th2) cell differentiation through direct cell contact with lymphocytes. HDLECs with dampened S1P signaling exhibited enhanced P-selectin, an important cell adhesion molecule expressed on activated vascular cells. In vitro , P-selectin blockade reduced the activation and differentiation of Th cells co-cultured with sh S1PR1 -treated HDLECs. P-selectin-directed antibody treatment improved tail swelling and reduced Th1/Th2 immune responses in mouse lymphedema. CONCLUSION: This study suggests that reduction of the LEC S1P signaling aggravates lymphedema by enhancing LEC adhesion and amplifying pathogenic CD4 T cell responses. P-selectin inhibitors are suggested as a possible treatment for this pervasive condition. Clinical Perspective: What is New?: Lymphatic-specific S1pr1 deletion exacerbates lymphatic vessel malfunction and Th1/Th2 immune responses during lymphedema pathogenesis. S1pr1 -deficient LECs directly induce Th1/Th2 cell differentiation and decrease anti-inflammatory Treg populations. Peripheral dermal LECs affect CD4 T cell immune responses through direct cell contact.LEC P-selectin, regulated by S1PR1 signaling, affects CD4 T cell activation and differentiation.P-selectin blockade improves lymphedema tail swelling and decreases Th1/Th2 population in the diseased skin.What Are the Clinical Implications?: S1P/S1PR1 signaling in LECs regulates inflammation in lymphedema tissue.S1PR1 expression levels on LECs may be a useful biomarker for assessing predisposition to lymphatic disease, such as at-risk women undergoing mastectomyP-selectin Inhibitors may be effective for certain forms of lymphedema.

Reliability analysis of motorcycle crash severity outcomes: Consideration of model selection uncertainty.

OBJECTIVE: While a large amount of work has been conducted on different types of crash injury severity models, model selection uncertainty remains a critical issue in traffic safety research. The objective of this study is to handle model selection uncertainty by combining multiple models. METHODS: Motorcycle crashes in Michigan from 2010 to 2014 are collected for the analysis. A model averaging approach is used to integrate useful information from three commonly used crash injury severity models: multinomial logit model, ordered logit model, and ordered probit model to deal with the situation where the model selection uncertainty exists in crash data analysis. The ratios of model posterior probabilities between models are used to quantify the model selection uncertainty. In addition, the effectiveness of the method is illustrated by comparing it with the single-best model. RESULTS: The ratios of model posterior probabilities among models approximate to 1. It means that three models have the same importance in statistical analysis of motorcycle injury severity, resulting in model selection uncertainty. The comparison between the results of model averaging approach and single-best model shows that the single-best model tends to overestimate the effects of risk factors on motorcycle injury severities because of ignoring the model selection uncertainty; parameter errors and confidence intervals of model averaging are greater and wider than those of the single-best model due to between-model uncertainty included in the model averaging; some risk factors are significant in the model averaging approach while not in the single-best model. Results from model averaging approach reveal that drunk or riding under influence, angle/sideswipe/head on crashes, speed limit of 35 mph or higher, and signal control play significant roles in the motorcycle crashes. CONCLUSIONS: The study contributes to the existing crash injury-severity literature by developing a model averaging approach to explore the relationship between motorcyclist's injury-severity and its contributing factors. The model averaging approach overcomes the limitations of the current crash injury-severity modeling approaches by (1) revealing the potential model selection uncertainty among injury-severity models with model posterior probabilities; (2) more reliably accounting for the effects of risk factors on motorcyclist' injury severities through integrating all information from the candidate models; and (3) better presenting the underlying unreliability of the analysis results from each individual model.

Causal mediation analysis of the impacts of distracted driving on crash injury risks.

Distracted driving can pose great risks to road traffic safety. Although there is a rich body of literature devoted to identifying the statistical association between distracted driving and crash risks, few are available to examine the causal effect mechanism of distracted driving. Thus, the study attempts to conduct the causal mediation analysis to reveal the impact mechanism of distracted driving on crash injury risks, in which various hazardous driving actions are used as the mediators between driver distraction and crash injuries. Sensitivity analysis is also carried out to validate the underlying assumption of causal mediation analysis. The analytic results indicate that 1) distracted driving can lead to a higher likelihood of hazardous driving actions such as failing to yield, disobeying traffic control devices, driving left of lane center, and failing to stop in assured clear distance, 2) both the driver distraction and hazardous actions are the contributory factors to the severe crash injuries, and 3) distracted driving is identified to have significant mediation effects on crash injury risks. The study confirms the causal mediation effects of distracted driving on crash injury risks, which can serve to propose specific safety countermeasures to mitigate the crash injury risks.

Bayesian spatial correlation, heterogeneity and spillover effect modeling for speed mean and variance on urban road networks.

Analyzing speed mean and variance is vital to safety management in urban roadway networks. However, modeling speed mean and variance on structured roads could be influenced by the spatial effects, which are rarely addressed in the existing studies. The inadequacy may lead to biased conclusions when considering vehicle speed as a surrogate safety measure. The current study focuses on developing a Bayesian modeling approach with three types of spatial effects, i.e., spatial correlation, spatial heterogeneity, and spillover effect. To capture the spatial correlation, the study employs the intrinsic conditional autoregressive (ICAR) models, spatial lag models (SLM), and spatial error models (SEM). Spatial heterogeneity and spillover effect are considered by the random parameters approach and spatially lagged covariates (SLCs). Speed data are collected from the float cars running on 134 urban arterials in Chengdu, China. The results indicate that the random parameters ICAR model with SLCs (RPICAR-SLC) outperforms others in terms of goodness-of-fit, accuracy, and efficiency for modeling speed mean, while the random parameters ICAR model (RPICAR) is the best for modeling speed variance. Moreover, RPICAR-SLC and RPICAR models are beneficial to address spatial correlation of residuals, explaining the unobserved influence among the observations, and are less likely to cause biased or overestimated parameters. The study also discusses how traffic conditions, road characteristics, traffic management strategies, and facilities on roadway networks influence speed mean and variance. The findings highlight the importance of multi-type spatial effects on modeling speed mean and variance along the structured roadways.

Exploring disease interrelationships in patients with lymphatic disorders: A single center retrospective experience.

BACKGROUND: The lymphatic contribution to the circulation is of paramount importance in regulating fluid homeostasis, immune cell trafficking/activation and lipid metabolism. In comparison to the blood vasculature, the impact of the lymphatics has been underappreciated, both in health and disease, likely due to a less well-delineated anatomy and function. Emerging data suggest that lymphatic dysfunction can be pivotal in the initiation and development of a variety of diseases across broad organ systems. Understanding the clinical associations between lymphatic dysfunction and non-lymphatic morbidity provides valuable evidence for future investigations and may foster the discovery of novel biomarkers and therapies. METHODS: We retrospectively analysed the electronic medical records of 724 patients referred to the Stanford Center for Lymphatic and Venous Disorders. Patients with an established lymphatic diagnosis were assigned to groups of secondary lymphoedema, lipoedema or primary lymphovascular disease. Individuals found to have no lymphatic disorder were served as the non-lymphatic controls. The prevalence of comorbid conditions was enumerated. Pairwise co-occurrence pattern analyses, validated by Jaccard similarity tests, was utilised to investigate disease-disease interrelationships. RESULTS: Comorbidity analyses underscored the expected relationship between the presence of secondary lymphoedema and those diseases that damage the lymphatics. Cardiovascular conditions were common in all lymphatic subgroups. Additionally, statistically significant alteration of disease-disease interrelationships was noted in all three lymphatic categories when compared to the control population. CONCLUSIONS: The presence or absence of a lymphatic disease significantly influences disease interrelationships in the study cohorts. As a physiologic substrate, the lymphatic circulation may be an underappreciated participant in disease pathogenesis. These relationships warrant further, prospective scrutiny and study.

Hypoxia and Hypoxia-Inducible Factors in Lymphedema.

Lymphedema is a chronic inflammatory disorder characterized by edema, fat deposition, and fibrotic tissue remodeling. Despite significant advances in lymphatic biology research, our knowledge of lymphedema pathology is incomplete. Currently, there is no approved pharmacological therapy for this debilitating disease. Hypoxia is a recognized feature of inflammation, obesity, and fibrosis. Understanding hypoxia-regulated pathways in lymphedema may provide new insights into the pathobiology of this chronic disorder and help develop new medicinal treatments.

Spatial statistics and random forest approaches for traffic crash hot spot identification and prediction.

Crash hot spot identification and prediction using spatial statistics and random forest methods on the interstate of Michigan are evaluated. The Getis-Ord statistics are adopted to identify hot spots using location, frequency, and equivalent property damage only weights computed from the cost and severity of crashes. In the random forest approach, data patterns between 2010 and 2017 are determined to predict hot spots of crashes in 2018. Accordingly, the results indicate that: (i) interstate routes have witnessed 13,089 crashes on significant hot spots, 7,413 on cold spots, and the rest in other locations; (ii) random forest shows 76.7% and 74% accuracy for validation and prediction, respectively. The performance of the model is further affirmed with precision, recall, and F-scores of 75%, 74%, and 70%, respectively; and (iii) clustering of the crashes exhibits spatial dependence of high and low equivalent property damage only crashes. The practical significance of the approach is highlighted in the identification and prediction of hot spots.

The Role of Regulatory T Cells in Pulmonary Arterial Hypertension.

Pulmonary arterial hypertension (PAH) is a chronic, incurable condition characterized by pulmonary vascular remodeling, perivascular inflammation, and right heart failure. Regulatory T cells (Tregs) stave off autoimmunity, and there is increasing evidence for their compromised activity in the inflammatory milieu of PAH. Abnormal Treg function is strongly correlated with a predisposition to PAH in animals and patients. Athymic Treg-depleted rats treated with SU5416, an agent causing pulmonary vascular injury, develop PAH, which is prevented by infusing missing CD4+CD25highFOXP3+ Tregs. Abnormal Treg activity may also explain why PAH disproportionately affects women more than men. This mini review focuses on the role of Tregs in PAH with a special view to sexual dimorphism and the future promise of Treg therapy.

[Preparation and Evaluation of Intranasal in situ Gel of Bupleuri Radix Volatile Oil and Baicalin].

OBJECTIVE: To prepare and evaluate a new formulation of thermosensitive and ion-sensitive in situ gel for nasal administration, using the volatile oil of Bupleuri radix and baicalin, the effective component extracted from Scutellariae radix . METHODS: Formulation of in situ nasal gel of Bupleuri radix volatile oil and baicalin was prepared by using poloxamer 407 and deacetylated gellan gum as the gel base, 10% pharmasolve and 2% polysorbate 80 as the solubilizer, and 0.8% triethanolamine as the pH regulator. The physical appearance, phase transition temperature, and baicalin release performance of the prepared gel were examined. The pharmacodynamic evaluation was done with the rat fever model developed with dry yeast and the mouse auricle swelling inflammation model. RESULTS: The phase transition temperature of the gel was optimized to be 36 ℃. The release of baicalin from the gel showed obvious features of sustained release, which accorded well the zero-order kinetics equation. The results of experiments with the rat dry yeast fever model and the mouse xylene auricle swelling inflammation model showed that the gel had significant antipyretic and anti-inflammatory effects that were significantly better than those of the groups treated with the blank gel base and the Bupleuri radix and Scutellariae radix granule. Results from the cilia toxicity test showed that the gel did not have obvious toxic effect on toad palate mucosal cilia. CONCLUSION: The in situ nasal gel of Bupleuri radix volatile oil and baicalin prepared in the study had a rapid onset time, high efficiency, and prolonged release of active ingredients, thus showing promises for further applicational development.

Operational factor analysis of the aggressive taxi speeders using random parameters Bayesian LASSO modeling approach.

Partial taxi speeders are observed with both high speeding frequency and severity (range). They thereby can be viewed as aggressive speeders whose behaviors may result in more hazards than others. Among the factors contributing to taxi speeding, the operational factors are proven to be deterministic. However, previous studies mainly investigate the operational factors of taxi speeding frequency, which fail to comprehensively unveil the impact of factors on speeders, especially for aggressive speeders. This study intends to disclose the operational factors affecting the aggressive taxi speeders with the random parameters Bayesian least absolute shrinkage and selection operator (LASSO) modeling approach. Taxi speeding behaviors and several operational factors are extracted from taxi GPS trajectory data in Chengdu, China. Based on the hourly speeding frequency and average speeding severity of each speeder, the fuzzy C-means clustering algorithm is employed to categorize taxi speeders into three cohorts: restrained speeder (RS), moderate speeder (MS), and belligerent speeder (BS). Compared to RS, MS and BS are treated as the aggressive taxi speeders. Several binary logistic models are developed with RS as the reference category. The random parameters Bayesian binary logistic LASSO model that captures the unobserved heterogeneity and tackles the multicollinearity is found to be the best fit model to identify the significant operational factors. The results indicate that aggressive taxi speeders are linked to longer daily driving distance and cruise distance, shorter delivery time, higher hourly income, driving at night, and driving on low-speed limit roads. However, intensive lane-changes and sufficient daily naps do not contribute to aggressive taxi speeders. Moreover, BS is more sensitive to the operational factors than MS. This study stresses the necessity of implementing speeder classification in taxi driver management and conceiving countermeasures considering the operational factors which are significantly associated with the aggressive taxi speeders.

Rethinking CRITID Procedure of Brain Targeting Drug Delivery: Circulation, Blood Brain Barrier Recognition, Intracellular Transport, Diseased Cell Targeting, Internalization, and Drug Release.

The past decades have witnessed great progress in nanoparticle (NP)-based brain-targeting drug delivery systems, while their therapeutic potentials are yet to be fully exploited given that the majority of them are lost during the delivery process. Rational design of brain-targeting drug delivery systems requires a deep understanding of the entire delivery process along with the issues that they may encounter. Herein, this review first analyzes the typical delivery process of a systemically administrated NPs-based brain-targeting drug delivery system and proposes a six-step CRITID delivery cascade: circulation in systemic blood, recognizing receptor on blood-brain barrier (BBB), intracellular transport, diseased cell targeting after entering into parenchyma, internalization by diseased cells, and finally intracellular drug release. By dissecting the entire delivery process into six steps, this review seeks to provide a deep understanding of the issues that may restrict the delivery efficiency of brain-targeting drug delivery systems as well as the specific requirements that may guarantee minimal loss at each step. Currently developed strategies used for troubleshooting these issues are reviewed and some state-of-the-art design features meeting these requirements are highlighted. The CRITID delivery cascade can serve as a guideline for designing more efficient and specific brain-targeting drug delivery systems.

Validating the underlying assumption of quasi-induced exposure technique disaggregated by crash injury severity.

INTRODUCTION: Quasi-induced exposure (QIE) technique has been popularly applied in the field of traffic safety research for decades. One of the basic assumptions of QIE theory is that the not-at-fault driving parties (D2s) involved in the crashes are the random selection of overall driving population at the event of crash occurrence. Very few literatures, however, can be identified to validate the assumption for crashes with specific injury severities that may not be satisfied in reality. METHOD: The study aims to check the validity of the assumption categorized by crash injury severity with the use of Michigan crash data. Latent class analysis is employed to generate several latent classes for the crashes with specific injury outcomes. Chi-square test is adopted to identify the significance of the similarity of D2 distributions among the latent classes. RESULTS: The results indicate that: (a) for fatal crashes the statistical tests do not identify the significant discrepancies for D2 distributions of driver gender, age, and vehicle type between latent classes; (b) for injury crashes, both D2 driver gender and age have the similar distributions between/among various classes, while the D2 vehicle types show the inconsistent distributions; and (c) with respect to property damage only crashes, the distributions of three vehicle-driver characteristics are significantly different among the latent classes. It implies that the underlying assumption may not entirely hold true for all the injury severities and driver-vehicle characteristics. Practical Applications: The findings pinpoint the applicability of the QIE technique under specific scenarios and highlight the importance of validating the underlying assumption of QIE prior to its application.

Bayesian networks for imbalance data to investigate the contributing factors to fatal injury crashes on the Ghanaian highways.

The crash data are often predominantly imbalanced, among which the fatal injury (or minority) crashes are significantly underrepresented relative to the non-fatal injury (or majority) ones. This unbalanced phenomenon poses a huge challenge to most of the statistical learning methods and needs to be addressed in the data preprocessing. To this end, we comparatively apply three data balance methods, i.e., the Synthetic Minority Oversampling Technique (SMOTE), the Borderline SMOTE (BL-SMOTE), and the Majority Weighted Minority Oversampling (MWMOTE). Then, we examine different Bayesian networks (BNs) to explore the contributing factors of fatal injury crashes. The 2016 highway crash data of Ghana are retrieved for the case study. The results show that the accuracy of the injury severity classification is improved by using the preprocessed data. Highest improvement is observed on the data preprocessed by the MWMOTE technique. Statistical verification is done by the Wilcoxon signed-rank test. The inference results of the best BNs show the significant factors of fatal crashes which include off-peak time, non-intersection area, pedestrian involved collisions, rural road environment, good tarred road, roads without shoulders, and multiple vehicles involved crash.

Ensemble-based model selection for imbalanced data to investigate the contributing factors to multiple fatality road crashes in Ghana.

The study aims to identify relevant variables to improve the prediction performance of the crash injury severity (CIS) classification model. Unfortunately, the CIS database is invariably characterized by the class imbalance. For instance, the samples of multiple fatal injury (MFI) severity class are typically rare as opposed to other classes. The imbalance phenomenon may introduce a prediction bias in favour of the majority class and affect the quality of the learning algorithm. The paper proposes an ensemble-based variable ranking scheme that incorporates the data resampling. At the data pre-processing level, majority weighted minority oversampling (MWMOTE) is employed to treat the imbalanced training data. Ensemble of classifiers induced from the balanced data is used to evaluate and rank the individual variables according to their importance to the injury severity prediction. The relevant variables selected are then applied to the balanced data to form a training set for the CIS classification modelling. An empirical comparison is conducted through considering the variable ranking by: 1) the learning of single inductive algorithm with imbalanced data where the relevant variables are applied to the imbalanced data to form the training data; 2) the learning of single inductive algorithm with MWMOTE data and the relevant variables identified are applied to the balanced data to form the training data; and 3) the learning of ensembles with imbalanced data where the relevant variables identified are applied to the imbalanced data to form the training data. Bayesian Networks (BNs) classifiers are then developed for each ranking method, where nested subsets of the top ranked variables are adopted. The model predictions are captured in four performance indicators in the comparative study. Based on three-year (2014-2016) crash data in Ghana, the empirical results show that the proposed method is effective to identify the most prolific predictors of the CIS level. Finally, based on the inference results of BNs developed on the best subset, the study offers the most probable explanations to the occurrence of MFI crashes in Ghana.