Novel use of a virtual driving assessment to classify driver skill at the time of licensure.

Motor vehicle crash rates are highest immediately after licensure, and driver error is one of the leading causes. Yet, few studies have quantified driving skills at the time of licensure, making it difficult to identify at-risk drivers before independent driving. Using data from a virtual driving assessment implemented into the licensing workflow in Ohio, this study presents the first population-level study classifying degree of skill at the time of licensure and validating these against a measure of on-road performance: license exam outcomes. Principal component and cluster analysis of 33,249 virtual driving assessments identified 20 Skill Clusters that were then grouped into 4 major summary "Driving Classes"; i) No Issues (i.e. careful and skilled drivers); ii) Minor Issues (i.e. an average new driver with minor vehicle control skill deficits); iii) Major Issues (i.e. drivers with more control issues and who take more risks); and iv) Major Issues with Aggression (i.e. drivers with even more control issues and more reckless and risk-taking behavior). Category labels were determined based on patterns of VDA skill deficits alone (i.e. agnostic of the license examination outcome). These Skill Clusters and Driving Classes had different distributions by sex and age, reflecting age-related licensing policies (i.e. those under 18 and subject to GDL and driver education and training), and were differentially associated with subsequent performance on the on-road licensing examination (showing criterion validity). The No Issues and Minor Issues classes had lower than average odds of failing, and the other two more problematic Driving Classes had higher odds of failing. Thus, this study showed that license applicants can be classified based on their driving skills at the time of licensure. Future studies will validate these Skill Cluster classes in relation to their prediction of post-licensure crash outcomes.

Licensing Examination and Crash Outcomes Postlicensure in Young Drivers.

Importance: Despite US graduated driver licensing laws, young novice driver crash rates remain high. Study findings suggest comprehensive license policy that mandates driver education including behind-the-wheel (BTW) training may reduce crashes postlicensure. However, only 15 states mandate BTW training. Objective: To identify differences in licensing and crash outcomes for drivers younger than 18 years who are subject to comprehensive licensing requirements (graduated driver licensing, driver education, and BTW training) vs those aged 18 to 24 years who are exempt from these requirements. Design, Setting, and Participants: This prospective, population-based cohort study used Ohio licensing data to define a cohort of 2018 license applicants (age 16-24 years, n = 136 643) and tracked licensed driver (n = 129 897) crash outcomes up to 12 months postlicensure. The study was conducted from January 1, 2018, to December 31, 2019, and data analysis was performed from October 7, 2019, to February 11, 2022. Main Outcomes and Measures: Licensing examination performance and population-based, police-reported crash rates in the first 2 months and 12 months postlicensure across age groups, sex, and census tract-level sociodemographic variables were measured. Poisson regression models compared newly licensed driver crash rates, with reference to individuals licensed at 18 years, while controlling for census tract-level sociodemographic factors, time spent in the learner permit period, and licensing examination performance measures. Results: Of 136 643 novice drivers, 69 488 (50.9%) were male and 67 152 (49.1%) were female. Mean (SD) age at enrollment (age at first on-road examination) was 17.7 (2.1) years. License applicants aged 16 and 17 years performed best on license examinations (15 466 [21.6%] and 5112 [30.9%] failing vs 7981 [37.5%] of applicants aged 18 years). Drivers licensed at 18 years had the highest crash rates of all those younger than 25 years. Compared with drivers licensed at 18 years, crash rates were 27% lower in individuals aged 16 years and 14% lower in those aged 17 years during the first 2 months postlicensure when controlling for socioeconomic status, time spent in learner permit status, and license examination performance measures (adjusted relative risk [aRR] at age 16 years: 0.73; 95% CI, 0.67-0.80; age 17 years: aRR, 0.86; 95% CI, 0.77-0.96). At 12 months postlicensure, crash rates were 19% lower for individuals licensed at age 16 years (aRR, 0.81; 95%, CI, 0.77-0.85) and 6% lower at age 17 years (aRR, 0.94; 95% CI, 0.89-0.99) compared with individuals aged 18 years. Conclusions and Relevance: In Ohio, drivers younger than 18 years who are subject to graduated driver licensing and driver education, including BTW training requirements, had lower crash rates in the first year postlicensure compared with those aged 18 years, with controls applied. These findings suggest that it may be fruitful for future work to reconsider the value of mandated driver license policies, including BTW training, and to examine reasons for delayed licensure and barriers to accessing training.

DAF-16/FOXO regulates homeostasis of essential sleep-like behavior during larval transitions in C. elegans.

Sleep homeostasis, which refers to the maintenance of sleep amount or depth following sleep deprivation, indicates that sleep and sleep-like states serve fundamental functions that cannot be bypassed [1]. Homeostasis of sleep-like behavior is observed during C. elegans lethargus, a 2-3 hr behavioral quiescent period that occurs during larval state transitions [2]. Here, we report a role for DAF-16/FOXO, a transcription factor that is active under conditions of stress [3], in the response to deprivation of lethargus quiescence. Forced locomotion during lethargus results in nuclear translocation of DAF-16. The formation of dauer larvae, a developmental state promoted by daf-16, is increased in response to quiescence deprivation. daf-16 mutants show an impaired homeostatic response to deprivation of lethargus quiescence and are hypersensitive to the lethal effects of forced locomotion during lethargus. DAF-16 expression in muscle cells, but not in neurons, is sufficient to restore a homeostatic response to deprivation of quiescence, pointing to a role for muscle in sleep homeostasis. These findings are relevant to clinical observations of altered metabolic signaling in response to sleep deprivation and suggest that these signaling pathways may act in nonneuronal tissue to regulate sleep behaviors.

Statistical Learning with Time Series Dependence: An Application to Scoring Sleep in Mice.

We develop methodology which combines statistical learning methods with generalized Markov models, thereby enhancing the former to account for time series dependence. Our methodology can accommodate very general and very long-term time dependence structures in an easily estimable and computationally tractable fashion. We apply our methodology to the scoring of sleep behavior in mice. As currently used methods are expensive, invasive, and labor intensive, there is considerable interest in high-throughput automated systems which would allow many mice to be scored cheaply and quickly. Previous efforts have been able to differentiate sleep from wakefulness, but they are unable to differentiate the rare and important state of REM sleep from non-REM sleep. Key difficulties in detecting REM are that (i) REM is much rarer than non-REM and wakefulness, (ii) REM looks similar to non-REM in terms of the observed covariates, (iii) the data are noisy, and (iv) the data contain strong time dependence structures crucial for differentiating REM from non-REM. Our new approach (i) shows improved differentiation of REM from non-REM sleep and (ii) accurately estimates aggregate quantities of sleep in our application to video-based sleep scoring of mice.

Hypnotic hypersensitivity to volatile anesthetics and dexmedetomidine in dopamine ß-hydroxylase knockout mice.

BACKGROUND: Multiple lines of evidence suggest that the adrenergic system can modulate sensitivity to anesthetic-induced immobility and anesthetic-induced hypnosis as well. However, several considerations prevent the conclusion that the endogenous adrenergic ligands norepinephrine and epinephrine alter anesthetic sensitivity. METHODS: Using dopamine ß-hydroxylase knockout (Dbh) mice genetically engineered to lack the adrenergic ligands and their siblings with normal adrenergic levels, we test the contribution of the adrenergic ligands upon volatile anesthetic induction and emergence. Moreover, we investigate the effects of intravenous dexmedetomidine in adrenergic-deficient mice and their siblings using both righting reflex and processed electroencephalographic measures of anesthetic hypnosis. RESULTS: We demonstrate that the loss of norepinephrine and epinephrine and not other neuromodulators co-packaged in adrenergic neurons is sufficient to cause hypersensitivity to induction of volatile anesthesia. However, the most profound effect of adrenergic deficiency is retarding emergence from anesthesia, which takes two to three times as long in Dbh mice for sevoflurane, isoflurane, and halothane. Having shown that Dbh mice are hypersensitive to volatile anesthetics, we further demonstrate that their hypnotic hypersensitivity persists at multiple doses of dexmedetomidine. Dbh mice exhibit up to 67% shorter latencies to loss of righting reflex and up to 545% longer durations of dexmedetomidine-induced general anesthesia. Central rescue of adrenergic signaling restores control-like dexmedetomidine sensitivity. A novel continuous electroencephalographic analysis illustrates that the longer duration of dexmedetomidine-induced hypnosis is not due to a motor confound, but occurs because of impaired anesthetic emergence. CONCLUSIONS: Adrenergic signaling is essential for normal emergence from general anesthesia. Dexmedetomidine-induced general anesthesia does not depend on inhibition of adrenergic neurotransmission.

Assessing REM sleep in mice using video data.

STUDY OBJECTIVES: Assessment of sleep and its substages in mice currently requires implantation of chronic electrodes for measurement of electroencephalogram (EEG) and electromyogram (EMG). This is not ideal for high-throughput screening. To address this deficiency, we present a novel method based on digital video analysis. This methodology extends previous approaches that estimate sleep and wakefulness without EEG/EMG in order to now discriminate rapid eye movement (REM) from non-REM (NREM) sleep. DESIGN: Studies were conducted in 8 male C57BL/6J mice. EEG/EMG were recorded for 24 hours and manually scored in 10-second epochs. Mouse behavior was continuously recorded by digital video at 10 frames/second. Six variables were extracted from the video for each 10-second epoch (i.e., intraepoch mean of velocity, aspect ratio, and area of the mouse and intraepoch standard deviation of the same variables) and used as inputs for our model. MEASUREMENTS AND RESULTS: We focus on estimating features of REM (i.e., time spent in REM, number of bouts, and median bout length) as well as time spent in NREM and WAKE. We also consider the model's epoch-by-epoch scoring performance relative to several alternative approaches. Our model provides good estimates of these features across the day both when averaged across mice and in individual mice, but the epoch-by-epoch agreement is not as good. CONCLUSIONS: There are subtle changes in the area and shape (i.e., aspect ratio) of the mouse as it transitions from NREM to REM, likely due to the atonia of REM, thus allowing our methodology to discriminate these two states. Although REM is relatively rare, our methodology can detect it and assess the amount of REM sleep.

Rapid eye movement sleep debt accrues in mice exposed to volatile anesthetics.

BACKGROUND: General anesthesia has been likened to a state in which anesthetized subjects are locked out of access to both rapid eye movement (REM) sleep and wakefulness. Were this true for all anesthetics, a significant REM rebound after anesthetic exposure might be expected. However, for the intravenous anesthetic propofol, studies demonstrate that no sleep debt accrues. Moreover, preexisting sleep debts dissipate during propofol anesthesia. To determine whether these effects are specific to propofol or are typical of volatile anesthetics, the authors tested the hypothesis that REM sleep debt would accrue in rodents anesthetized with volatile anesthetics. METHODS: Electroencephalographic and electromyographic electrodes were implanted in 10 mice. After 9-11 days of recovery and habituation to a 12 h:12 h light-dark cycle, baseline states of wakefulness, nonrapid eye movement sleep, and REM sleep were recorded in mice exposed to 6 h of an oxygen control and on separate days to 6 h of isoflurane, sevoflurane, or halothane in oxygen. All exposures were conducted at the onset of light. RESULTS: Mice in all three anesthetized groups exhibited a significant doubling of REM sleep during the first 6 h of the dark phase of the circadian schedule, whereas only mice exposed to halothane displayed a significant increase in nonrapid eye movement sleep that peaked at 152% of baseline. CONCLUSION: REM sleep rebound after exposure to volatile anesthetics suggests that these volatile anesthetics do not fully substitute for natural sleep. This result contrasts with the published actions of propofol for which no REM sleep rebound occurred.