Analysis of pedestrian accident severity by considering temporal instability and heterogeneity.

The aim of this study was to investigate the effects of temporal instability and possible heterogeneity on pedestrian accident severity, 48786 accident data from 2018 to 2021 in the UK STATS database were used as the study object, and accident severity was used as the dependent variable, and 49 accident characteristics were selected as independent variables from 6 characteristics of accident pedestrian, driver, vehicle, road, environment and time to construct the pedestrian accident mean heterogeneity random-parameter logit model and examined its temporal stability. The results of model estimation and likelihood ratio tests indicate that the variables affecting pedestrian injury severity are highly variable and not stable over the years. And further demonstrates the potential of models that address unobserved heterogeneity for significant relationships in pedestrian accident severity analyses.

Analysis of factors influencing the degree of accidental injury of bicycle riders considering data heterogeneity and imbalance.

Bicycle safety has emerged as a pressing concern within the vulnerable transportation community. Numerous studies have been conducted to identify the significant factors that contribute to the severity of cyclist injuries, yet the findings have been subject to uncertainty due to unobserved heterogeneity and class imbalance. This research aims to address these issues by developing a model to examine the impact of key factors on cyclist injury severity, accounting for data heterogeneity and imbalance. To incorporate unobserved heterogeneity, a total of 3,895 bicycle accidents were categorized into three homogeneous sub-accident clusters using Latent Class Cluster Analysis (LCA). Additionally, five over-sampling techniques were employed to mitigate the effects of data imbalance in each accident cluster category. Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. The optimal BN models for each accident cluster type provided insights into the key factors associated with cyclist injury severity. The results indicate that the key factors influencing serious cyclist injuries vary heterogeneously across different accident clusters. Female cyclists, adverse weather conditions such as rain and snow, and off-peak periods were identified as key factors in several subclasses of accident clusters. Conversely, factors such as the week of the accident, characteristics of the trafficway, the season, drivers failing to yield to the right-of-way, distracted cyclists, and years of driving experience were found to be key factors in only one subcluster of accident clusters. Additionally, factors such as the time of the crash, gender of the cyclist, and weather conditions exhibit varying levels of heterogeneity across different accident clusters, and in some cases, exhibit opposing effects.

Study of AEB and active seat belt on driver injury in vehicle-vehicle frontal oblique crash.

The safety of vehicle occupants in oblique collision scenarios continues to pose challenges, even with the implementation of Automatic Emergency Braking (AEB) systems. While AEB reduces collision risks, studies indicate it may heighten injury risks for out-of-position (OOP) occupants. To counteract this issue, the integration of active seat belts in vehicles equipped with AEB systems is recommended. Firstly, this study established an oblique angle collision scenario post-AEB activation using data from the Chinese National Automobile Accident In-depth Investigation System (NAIS) database, analyzed through Prescan software. The dynamic response of the vehicle was examined. Following this, finite element (FE) models were validated to assess the effects of collision overlap rate, AEB braking strategy, and active seat belt pre-tensioning on occupant injuries and kinematics. Under specific collision conditions, the impact of the timing and amount of seat belt pre-tensioning, as well as airbag deployment timing on occupant injuries, was also explored. Findings revealed that a 75% collision overlap rate significantly increases driver injury risk. Active seat belts effectively mitigate injuries caused by OOP statuses during AEB interventions, with the lowest Weighted Injury Criterion (WIC) observed at a pre-tensioning time of 200 ms for active seat belts. The study further suggests that optimal results in reducing occupant injuries are achieved when active pre-tensioning seat belts are complemented by appropriately timed airbag deployment.

Driver Injury from Vehicle Side Impacts When Automatic Emergency Braking and Active Seat Belts Are Used.

As an advanced driver assistance system, automatic emergency braking (AEB) can effectively reduce accidents by using high-precision and high-coverage sensors. In particular, it has a significant advantage in reducing front-end collisions and rear-end accidents. Unfortunately, avoiding side collisions is a challenging problem for AEB. To tackle these challenges, we propose active seat belt pretensioning on driver injury in vehicles equipped with AEB in unavoidable side crashes. Firstly, records of impact cases from China's National Automobile Accident In-Depth Investigation System were used to investigate a scenario in which a vehicle is impacted by an oncoming car after the vehicle's AEB system is triggered. The scenario was created using PreScan software. Then, the simulated vehicles in the side impact were devised using a finite element model of the Toyota Yaris and a moving barrier. These were constructed in HyperMesh software along with models of the driver's side seatbelt, side airbag, and side curtain airbag. Moreover, the models were verified, and driver out-of-position instances and injuries were evaluated in simulations with different AEB intensities up to 0.7 g for three typical side impact angles. Last but not least, the optimal combination of seatbelt pretensioning and the timing thereof for minimizing driver injury at each side impact angle was identified using orthogonal tests; immediate (at 0 ms) pretensioning at 80 N was applied. Our experiments show that our active seatbelt with the above parameters reduced the weighted injury criterion by 5.94%, 22.05%, and 20.37% at impact angles of 90°, 105°, and 120°, respectively, compared to that of a conventional seatbelt. The results of the experiment can be used as a reference to appropriately set the collision parameters of active seat belts for vehicles with AEB.

Proteinosomes via Self-Assembly of Thermoresponsive Miktoarm Polymer Protein Bioconjugates.

To fabricate nanoscale proteinosomes, thermoresponsive miktoarm polymer protein bioconjugates were prepared through highly efficient molecular recognition between the ß-cyclodextrin modified BSA (CD-BSA) and the adamantyl group anchored at the junction point of the thermoresponsive block copolymer poly(ethylene glycol)-b-poly(di(ethylene glycol) methyl ether methacrylate) (PEG-b-PDEGMA). PEG-b-PDEGMA was synthesized by the Passerini reaction of benzaldehyde-modified PEG, 2-bromo-2-methylpropionic acid, and 1-isocyanoadamantane, followed by the atom transfer radical polymerization of DEGMA. Two block copolymers with different chain lengths of PDEGMA were prepared, and both self-assembled into polymersomes at a temperature above their lower critical solution temperatures (LCST). The two copolymers can undergo molecular recognition with the CD-BSA and form miktoarm star-like bioconjugates. The bioconjugates self-assembled into ∼160 nm proteinosomes at a temperature above their LCSTs, and the miktoarm star-like structure has a great effect on the formation of the proteinosomes. Most of the secondary structure and esterase activity of BSA in the proteinosomes were maintained. The proteinosomes exhibited low toxicity to the 4T1 cells and could deliver model drug doxorubicin into the 4T1 cells.

Hands-on childcare garden intervention: A randomized controlled trial to assess effects on fruit and vegetable identification, liking, and consumption among children aged 3-5 years in North Carolina.

Gardening at childcare centers may have a potent influence on young children's learning about fruits and vegetables and their development of healthy dietary behaviors. This randomized controlled trial examined the effect of a garden intervention on fruit and vegetable (FV) identification, FV liking, and FV consumption among 3-5-year-old children enrolled in childcare centers in Wake County, North Carolina, USA. Eligible childcare centers (serving primarily low-income families) were randomly selected and then randomly assigned to one of three groups: (1) intervention; (2) waitlist-control that served as a control in year 1 and received the intervention in year 2; or (3) no-intervention control. From the 15 participating childcare centers, 285 children aged 3-5 years were consented by their parents or guardians to participate. The intervention comprised six standardized, raised, mulched garden beds, planted with warm-season annual vegetables and fruits, and perennial fruits. A Gardening Activity Guide describing 12 age-appropriate, sequential gardening activities was distributed for teachers to lead hands-on gardening activities during the growing season. Data were gathered between Spring 2018 and Fall 2019. FV identification and liking were measured using an age-appropriate tablet-enabled protocol. FV consumption was measured by weighing each child's fruit and vegetable snack tray before and after tasting sessions. Compared to children receiving no-intervention, children who received the garden intervention showed a greater increase in accurate identification of both fruits and vegetables as well as consumption of both fruit and vegetables during the tasting sessions. Consistent with prior research, the effects on fruit consumption were greater than on vegetable consumption. There was no significant effect of the garden intervention on children's FV liking. Garden interventions implemented early in life foster learning about FV and promote healthy eating. Early exposure to gardening may yield a return on investment throughout the lifecourse, impacting healthy diet and associated health outcomes, which are particularly important within disadvantaged communities where children's health is challenged by a host of risk factors. Clinical Trials Registration #NCT04864574 (clinicaltrials.gov).

Research on safety of the intended functionality of automobile AEB perception system in typical dangerous scenarios of two-wheelers.

The statistical analysis was conducted on data of accident scenarios between cars and two-wheelers from National Automobile Accident In-depth Investigation System (NAIS) database in order to study safety of intended functionality of Autonomous emergency braking (AEB) perception system in typical dangerous scenarios of cars and two-wheelers. 11 scenario-related variables were selected, and 6 types of typical scenarios were obtained through cluster analysis and manual classification. The 6 types of typical scenarios were built by the automatic driving simulation software PreScan, and the AEB longitudinal control algorithm was built in Matlab/Simulink. Batch simulation script files were written, and the relative location distribution of car and two-wheeler with different time to collision (TTC) was obtained by batch simulation. Furthermore, the effects of car velocity, two-wheeler velocity and cyclist casualties on the parameter configuration of the perception system were analyzed. Under the premise of satisfying safety of intended functionality of the perception system, the optimal sensor detection scheme at different TTCs was obtained by comprehensively considering the death accident detection rate, detection area, and standard deviation. The results show that when the detection rate is 90%, the AEB system can adopt the detection scheme of long-range radar and short-range radar. The field of view (FOV) and detection range of the short range radar are 133.6° and 38.1 m, and those of the long range radar are 84.5° and 74.3 m. And when the detection rate was close to 100%, a single sensor can be used, and the detection parameters are 150° and 77.6 m. It provides reference for parameter optimization of AEB perception system.

Research Design, Protocol, and Participant Characteristics of COLEAFS: A Cluster Randomized Controlled Trial of a Childcare Garden Intervention.

Childcare garden interventions may be an effective strategy to increase fruit and vegetable (FV) consumption and physical activity among young children. The objective of this paper is to describe the research design, protocol, outcome measures, and baseline characteristics of participants in the Childcare Outdoor Learning Environments as Active Food Systems ("COLEAFS") study, a cluster randomized controlled trial (RCT) examining the effect of a garden intervention on outcomes related to diet and physical activity. Fifteen childcare centers in low-income areas were randomly assigned to intervention (to receive garden intervention in Year 1), waitlist control (to receive garden intervention in Year 2), and control group (no intervention). The garden intervention comprised six raised beds planted with warm-season vegetables and fruits, and a garden activity booklet presenting 12 gardening activities. FV knowledge and FV liking were measured using a tablet-enabled protocol. FV consumption was measured by weighing FV before and after a snack session. Physical activity was measured using Actigraph GT3x+ worn by children for three consecutive days while at the childcare center. Of the 543 eligible children from the 15 childcare centers, 250 children aged 3-5 years received parental consent, assented, and participated in baseline data collection. By employing an RCT to examine the effect of a garden intervention on diet and physical activity among young children attending childcare centers within low-income communities, this study offers compelling research design and methods, addresses a critical gap in the empirical literature, and is a step toward evidence-based regulations to promote early childhood healthy habits.

Stimuli-Responsive Proteinosomes Based on Biohybrid Shell Cross-Linked Micelles.

A new method of stimuli-responsive proteinosome fabrication with the shell cross-linked micelle as a template is reported in this research. A thermoresponsive diblock copolymer poly[di(ethylene glycol) methyl ether methacrylate]-b-poly[poly(ethylene glycol) methyl ether methacrylate-co-pyridyl disulfide methacrylamide] [PDEGMA-b-P(PEGMA-co-PDSMA)] was synthesized and self-assembled into micelles with PDEGMA cores and P(PEGMA-co-PDSMA) shells at the temperature above its lower critical solution temperature (LCST). Reduced bovine serum albumin (BSA) molecules with six thiol groups were used to cross-link the shells of the micelles by reacting with the pendant pyridyl disulfide groups on the P(PEGMA-co-PDSMA) block. At a temperature below the LCST of the polymer, the PDEGMA cores were dissolved in water, affording proteinosomes with a size of about 50 nm and capsule-like structures. The proteinosome was also thermoresponsive with a phase transition temperature at 35 °C. The fabrication of the proteinosome had no obvious influence on the structure and activity of BSA, and BSA retained most of its secondary structure and esterase-like activity. Because the BSA molecules were connected to the polymer chains through disulfide bonds, they could be released upon addition of dithiothreitol. The in vitro cell viability evaluation and the cellular uptake assay demonstrated that the proteinosome showed low toxicity to NIH 3T3 and 4T1 cells and could be internalized into the 4T1 cells.

Trimester-specific reference ranges for thyroid hormones in pregnant women.

The aim of this study was to determine the trimester-specific reference range of thyroid function in Nanjing.A total of 805 pregnant women in the 1st, 2nd, and 3rd trimesters were recruited in the prospective, observational study during their routine antenatal clinic visit and 282 nonpregnant subjects served as controls. A questionnaire was completed by the subjects to record their personal health history, family history of thyroid disease, and consumption of estrogen or antithyroid drugs. Thyroid palpation was performed to exclude the thyroid goiter. Thyroid function and urine iodine were measured by chemiluminescence and arsenic cerium analysis.The trimester-specific reference ranges in Nanjing were as follows: thyroid-stimulating hormone (TSH) 0.02 to 3.78 mIU/L, free thyroxine (FT4) 13.93 to 26.49 pmol/L, total thyroxine (TT4) 103.39 to 319.43 nmol/L in the 1st trimester. TSH 0.47 to 3.89 mIU/L, FT4 12.33 to 19.33 pmol/L, TT4 92.28 to 234.88 nmol/L in the 2nd trimester. TSH 0.55 to 4.91 mIU/L, FT4 11.38 to 19.21 pmol/L, TT4 83.54 to 258.12 nmol/L in the 3rd trimester. According to the TSH reference range recommended by American Thyroid Association (ATA), the prevalence of subclinical hypothyroidism, subclinical hyperthyroidism, hyperthyroidism, hypothyroxinemia, and thyroid peroxidase antibody-positive were 12.42%, 0.50%, 0.99%, 1.61%, and 11.80%, respectively, prevalence according to the trimester-specific reference range were 1.99%, 0.25%, 1.61%, 0.37%, and 1.61%, respectively, which showed elevated hypothyroxinemia incidence and declined incidence of subclinical hypothyroidism and hyperthyroidism.Trimester-specific reference range varied from that of ATA's recommendation, influencing the diagnosis, and treatment of pregnant thyroid disorders. To detect and control these disorders properly, setting up trimester-specific reference is clinically essential.

Bivariate Mixed Effects Analysis of Clustered Data with Large Cluster Sizes.

Linear mixed effects models are widely used to analyze a clustered response variable. Motivated by a recent study to examine and compare the hospital length of stay (LOS) between patients undertaking percutaneous coronary intervention (PCI) and coronary artery bypass graft (CABG) from several international clinical trials, we proposed a bivariate linear mixed effects model for the joint modeling of clustered PCI and CABG LOS's where each clinical trial is considered a cluster. Due to the large number of patients in some trials, commonly used commercial statistical software for fitting (bivariate) linear mixed models failed to run since it could not allocate enough memory to invert large dimensional matrices during the optimization process. We consider ways to circumvent the computational problem in the maximum likelihood (ML) inference and restricted maximum likelihood (REML) inference. Particularly, we developed an expected and maximization (EM) algorithm for the REML inference and presented an ML implementation using existing software. The new REML EM algorithm is easy to implement and computationally stable and efficient. With this REML EM algorithm, we could analyze the LOS data and obtained meaningful results.

A flexible model for correlated medical costs, with application to medical expenditure panel survey data.

We propose a flexible model for correlated medical cost data with several appealing features. First, the mean function is partially linear. Second, the distributional form for the response is not specified. Third, the covariance structure of correlated medical costs has a semiparametric form. We use extended generalized estimating equations to simultaneously estimate all parameters of interest. B-splines are used to estimate unknown functions, and a modification to Akaike information criterion is proposed for selecting knots in spline bases. We apply the model to correlated medical costs in the Medical Expenditure Panel Survey dataset. Simulation studies are conducted to assess the performance of our method.

Statistical Methods for Generalized Linear Models with Covariates Subject to Detection Limits.

Censored observations are a common occurrence in biomedical data sets. Although a large amount of research has been devoted to estimation and inference for data with censored responses, very little research has focused on proper statistical procedures when predictors are censored. In this paper, we consider statistical methods for dealing with multiple predictors subject to detection limits within the context of generalized linear models. We investigate and adapt several conventional methods and develop a new multiple imputation approach for analyzing data sets with predictors censored due to detection limits. We establish the consistency and asymptotic normality of the proposed multiple imputation estimator and suggest a computationally simple and consistent variance estimator. We also demonstrate that the conditional mean imputation method often leads to inconsistent estimates in generalized linear models, while several other methods are either computationally intensive or lead to parameter estimates that are biased or more variable compared to the proposed multiple imputation estimator. In an extensive simulation study, we assess the bias and variability of different approaches within the context of a logistic regression model and compare variance estimation methods for the proposed multiple imputation estimator. Lastly, we apply several methods to analyze the data set from a recently-conducted GenIMS study.

Assessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression.

Accounting for gene-environment (G×E) interactions in complex trait association studies can facilitate our understanding of genetic heterogeneity under different environmental exposures, improve the ability to discover susceptible genes that exhibit little marginal effect, provide insight into the biological mechanisms of complex diseases, help to identify high-risk subgroups in the population, and uncover hidden heritability. However, significant G×E interactions can be difficult to find. The sample sizes required for sufficient power to detect association are much larger than those needed for genetic main effects, and interactions are sensitive to misspecification of the main-effects model. These issues are exacerbated when working with binary phenotypes and rare variants, which bear less information on association. In this work, we present a similarity-based regression method for evaluating G×E interactions for rare variants with binary traits. The proposed model aggregates the genetic and G×E information across markers, using genetic similarity, thus increasing the ability to detect G×E signals. The model has a random effects interpretation, which leads to robustness against main-effect misspecifications when evaluating G×E interactions. We construct score tests to examine G×E interactions and a computationally efficient EM algorithm to estimate the nuisance variance components. Using simulations and data applications, we show that the proposed method is a flexible and powerful tool to study the G×E effect in common or rare variant studies with binary traits.

A Fast EM Algorithm for Fitting Joint Models of a Binary Response and Multiple Longitudinal Covariates Subject to Detection Limits.

Joint modeling techniques have become a popular strategy for studying the association between a response and one or more longitudinal covariates. Motivated by the GenIMS study, where it is of interest to model the event of survival using censored longitudinal biomarkers, a joint model is proposed for describing the relationship between a binary outcome and multiple longitudinal covariates subject to detection limits. A fast, approximate EM algorithm is developed that reduces the dimension of integration in the E-step of the algorithm to one, regardless of the number of random effects in the joint model. Numerical studies demonstrate that the proposed approximate EM algorithm leads to satisfactory parameter and variance estimates in situations with and without censoring on the longitudinal covariates. The approximate EM algorithm is applied to analyze the GenIMS data set.

Pathway-guided identification of gene-gene interactions.

Assessing gene-gene interactions (GxG) at the gene level can permit examination of epistasis at biologically functional units with amplified interaction signals from marker-marker pairs. While current gene-based GxG methods tend to be designed for two or a few genes, for complex traits, it is often common to have a list of many candidate genes to explore GxG. We propose a regression model with pathway-guided regularization for detecting interactions among genes. Specifically, we use the principal components to summarize the SNP-SNP interactions between a gene pair, and use an L1 penalty that incorporates adaptive weights based on biological guidance and trait supervision to identify important main and interaction effects. Our approach aims to combine biological guidance and data adaptiveness, and yields credible findings that may be likely to shed insights in order to formulate biological hypotheses for further molecular studies. The proposed approach can be used to explore the GxG with a list of many candidate genes and is applicable even when sample size is smaller than the number of predictors studied. We evaluate the utility of the proposed method using simulation and real data analysis. The results suggest improved performance over methods not utilizing pathway and trait guidance.

Flexible Modeling of Survival Data with Covariates Subject to Detection Limits via Multiple Imputation.

Models for survival data generally assume that covariates are fully observed. However, in medical studies it is not uncommon for biomarkers to be censored at known detection limits. A computationally-efficient multiple imputation procedure for modeling survival data with covariates subject to detection limits is proposed. This procedure is developed in the context of an accelerated failure time model with a flexible seminonparametric error distribution. The consistency and asymptotic normality of the multiple imputation estimator are established and a consistent variance estimator is provided. An iterative version of the proposed multiple imputation algorithm that approximates the EM algorithm for maximum likelihood is also suggested. Simulation studies demonstrate that the proposed multiple imputation methods work well while alternative methods lead to estimates that are either biased or more variable. The proposed methods are applied to analyze the dataset from a recently-conducted GenIMS study.

A flexible model for the mean and variance functions, with application to medical cost data.

Medical cost data are often skewed to the right and heteroscedastic, having a nonlinear relation with covariates. To tackle these issues, we consider an extension to generalized linear models by assuming nonlinear associations of covariates in the mean function and allowing the variance to be an unknown but smooth function of the mean. We make no further assumption on the distributional form. The unknown functions are described by penalized splines, and the estimation is carried out using nonparametric quasi-likelihood. Simulation studies show the flexibility and advantages of our approach. We apply the model to the annual medical costs of heart failure patients in the clinical data repository at the University of Virginia Hospital System.

A Seminonparametric Approach to Joint Modeling of A Primary Binary Outcome and Longitudinal Data Measured at Discrete Informative Times.

In a study conducted at the New York University Fertility Center, one of the scientific objectives is to investigate the relationship between the final pregnancy outcomes of participants receiving an in vitro fertilization (IVF) treatment and their ß-human chorionic gonadotrophin (ß-hCG) profiles. A common joint modeling approach to this objective is to use subject-specific normal random effects in a linear mixed model for longitudinal ß-hCG data as predictors in a model (e.g., logistic model) for the final pregnancy outcome. Empirical data exploration indicates that the observation times for longitudinal ß-hCG data may be informative and the distribution of random effects for longitudinal ß-hCG data may not be normally distributed. We propose to introduce a third model in the joint model for the informative ß-hCG observation times, and relax the normality distributional assumption of random effects using the semi-nonparametric (SNP) approach of Gallant and Nychka (1987) [8]. An EM algorithm is developed for parameter estimation. Extensive simulation designed to evaluate the proposed method indicates that ignoring either informative observation times or distributional assumption of the random effects would lead to invalid and/or inefficient inference. Applying our new approach to the data reveals some interesting findings the traditional approach failed to discover.

Trajectory analyses in alcohol treatment research.

BACKGROUND: Various statistical methods have been used for data analysis in alcohol treatment studies. Trajectory analyses can better capture differences in treatment effects and may provide insight on the optimal duration of future clinical trials and grace periods. This improves on the limitation of commonly used parametric (e.g., linear) methods that cannot capture nonlinear temporal trends in the data. METHODS: We propose an exploratory approach, using more flexible smoothing mixed effects models, more accurately to characterize the temporal patterns of the drinking data. We estimated the trajectories of the treatment arms for data sets from 2 sources: a multisite topiramate study, and the Combined Pharmacotherapies (acamprosate and naltrexone) and Behavioral Interventions study. RESULTS: Our methods illustrate that drinking outcomes of both the topiramate and placebo arms declined over the entire course of the trial but with a greater rate of decline for the topiramate arm. By the point-wise confidence intervals, the heavy drinking probabilities for the topiramate arm might differ from those of the placebo arm as early as week 2. Furthermore, the heavy drinking probabilities of both arms seemed to stabilize at the end of the study. Overall, naltrexone was better than placebo in reducing drinking over time yet was not different from placebo for subjects receiving the combination of a brief medical management and an intensive combined behavioral intervention. CONCLUSIONS: The estimated trajectory plots clearly showed nonlinear temporal trends of the treatment with different medications on drinking outcomes and offered more detailed interpretation of the results. This trajectory analysis approach is proposed as a valid exploratory method for evaluating efficacy in pharmacotherapy trials in alcoholism.