Immunoinformatics-guided recombinant polypeptide-based enzyme-linked immunosorbent assay for seromonitoring of laboratory animals for minute virus of mice and Kilham rat virus.

Subclinical infection of laboratory animals with one or more of several pathogens affects the results of experiments on animals. Monitoring the health of laboratory animals encompasses routine surveillance for pathogens, including several viruses. This study aimed to explore the development of an alternative assay to the existing ones for detecting infection of mice and rats with the parvoviruses minute virus of mice (MVM) and Kilham rat virus (KRV), respectively. Full-length VP2 and NS1 proteins of these parvoviruses, besides fragments containing multiple predicted epitopes stitched together, were studied for serological detection. The optimal dilution of full-length proteins and antigenic regions containing predicted epitopes for coating, test sera, and conjugate was determined using a checkerboard titration at each step. The assays were evaluated vis-à-vis commercially available ELISA kits. The results showed that an engineered fusion of fragments containing multiple predicted MVM VP2 and NS1 epitopes was better than either of the full-length proteins for detecting antibodies in 90% of the tested sera samples. For KRV ELISA, full-length VP2 was better compared to other individual recombinant protein fragments or combinations thereof for the detection of antibodies in sera. This report is the first description of an ELISA for KRV and an improved assay for MVM. Importantly, our assays could be exploited with small volumes of sera. The results also demonstrate the utility of immunoinformatics-driven polypeptide engineering in the development of diagnostic assays and the potential to develop better tests for monitoring the health status of laboratory animals.

Advancing proactive crash prediction: A discretized duration approach for predicting crashes and severity.

Driven by advancements in data-driven methods, recent developments in proactive crash prediction models have primarily focused on implementing machine learning and artificial intelligence. However, from a causal perspective, statistical models are preferred for their ability to estimate effect sizes using variable coefficients and elasticity effects. Most statistical framework-based crash prediction models adopt a case-control approach, matching crashes to non-crash events. However, accurately defining the crash-to-non-crash ratio and incorporating crash severities pose challenges. Few studies have ventured beyond the case-control approach to develop proactive crash prediction models, such as the duration-based framework. This study extends the duration-based modeling framework to create a novel framework for predicting crashes and their severity. Addressing the increased computational complexity resulting from incorporating crash severities, we explore a tradeoff between model performance and estimation time. Results indicate that a 15 % sample drawn at the epoch level achieves a balanced approach, reducing data size while maintaining reasonable predictive accuracy. Furthermore, stability analysis of predictor variables across different samples reveals that variables such as Time of day (Early afternoon), Weather condition (Clear), Lighting condition (Daytime), Illumination (Illuminated), and Volume require larger samples for more accurate coefficient estimation. Conversely, Daytime (Early morning, Late morning, Late afternoon), Lighting condition (Dark lighted), Terrain (Flat), Land use (Commercial, Rural), Number of lanes, and Speed converge towards true estimates with small incremental increases in sample size. The validation reveals that the model performs better in highway segments experiencing more frequent crashes (segments where the duration between crashes is less than 100 h, or approximately 4 days).

Effects of CYP3A4 and CYP2C9 genotype on systemic anastrozole and fulvestrant concentrations in SWOG S0226.

Objective & methods: This study tested associations of genotype-predicted activity of CYP3A4, other pharmacogenes, SLC28A7 (rs11648166) and ALPPL2 (rs28845026) with systemic concentrations of the endocrine therapies anastrozole and fulvestrant in SWOG S0226 trial participants. Results: Participants in the anastrozole-only arm with low CYP3A4 activity (i.e. CYP3A4*22 carriers) had higher systemic anastrozole concentrations than patients with high CYP3A4 activity (ß-coefficient = 10.03; 95% CI: 1.42, 18.6; p = 0.025). In an exploratory analysis, participants with low CYP2C9 activity had lower anastrozole concentrations and higher fulvestrant concentrations than participants with high CYP2C9 activity. Conclusion: Inherited genetic variation in CYP3A4 and CYP2C9 may affect concentrations of endocrine therapy and may be useful to personalize dosing and improve treatment outcomes.

Assessment of Immune Responses to Rabies Vaccination in Free-Ranging Dogs in Bengaluru, India.

Rabies is a fatal encephalomyelitis mainly transmitted to humans and other animals by rabid dog bites. Hence, vaccination programs are being instituted for the control of rabies in dogs. Though stray dogs have been vaccinated for years under various programs initiated for control of the disease, the effectiveness of these programs can be ascertained only by assessing the immunity of these dogs. With this in view, a study was conducted to assess the effectiveness of the ongoing mass dog vaccination (MDV) program by the Bengaluru City Municipal Corporation, Bengaluru, India. Whole blood and serum samples (n = 260) from vaccinated stray dogs in 26 wards of 8 corporation zones were tested by rapid fluorescent focus inhibition test (RFFIT) as well as an in-house quantitative indirect enzyme-linked immunosorbent assay (iELISA) for a humoral response and by interferon-gamma (IFN-γ) ELISA for a cellular response. As determined by the cut-off value of 0.5 IU/mL of serum, 71% and 87% of the samples from vaccinated dogs revealed adequate levels of antibodies presumed to confer protection by RFFIT and iELISA, respectively. The sensitivity and specificity of the iELISA were 100% and 63.3%, respectively. The IFN-γ ELISA revealed adequate cellular response in 50% of the samples. The quantitative iELISA was found to be useful in large-scale seromonitoring of MDV programs to aid in the elimination of dog-mediated rabies.

Analysis of speed reductions and crash risk of aggressive drivers during emergent pre-crash scenarios at unsignalized intersections.

Aggressive driver behavior (ADB) is often linked with road crashes, especially during crash imminent situations. Previous studies demonstrated that ADB was positively correlated with collision risk; however, this relationship has not quantified evidently. This study aimed to analyze drivers' collision risk and speed reduction behavior during an emergent pre-crash scenario (such as a conflict encroaching into an unsignalized intersection at different critical time gaps) using a driving simulator. The effect of ADB on crash risk is investigated using the time to collision (TTC). Further, drivers' collision evasive behavior is analyzed using speed reduction time (SRT) survival probabilities. Fifty-eight Indian drivers are identified as aggressive, moderately aggressive, and, non-aggressive based on aggressive indicators such as vehicle kinematics (percentage of the time spent in speeding and rapid accelerations, maximum brake pressure, etc.). Two separate models are built to analyze ADB effects on TTC and SRT using a Generalized Linear Mixed Model (GLMM) and a Weibull Accelerated Failure Time (AFT) model, respectively. From the results, it can be observed that aggressive drivers' TTC and SRT are reduced by 82% and 38%, respectively. Compared to a 7 sec conflict approaching time gap, TTC is reduced by 18%, 39%, 51%, and 58% for 6 sec, 5 sec, 4 sec, and 3 sec conflict approaching time gaps, respectively. The estimated SRT survival probabilities for aggressive, moderately aggressive and non-aggressive drivers are 0%, 3% and 68% at 3 sec of conflict approaching time gap, respectively. SRT survival probability increased by 25% for matured drivers and decreased by 48% for drivers who tend to engage in frequent speeding. Important implications of the study findings are discussed.

Genome sequencing and comparative genomic analysis of bovine mastitis-associated Staphylococcus aureus strains from India.

BACKGROUND: Bovine mastitis accounts for significant economic losses to the dairy industry worldwide. Staphylococcus aureus is the most common causative agent of bovine mastitis. Investigating the prevalence of virulence factors and antimicrobial resistance would provide insight into the molecular epidemiology of mastitis-associated S. aureus strains. The present study is focused on the whole genome sequencing and comparative genomic analysis of 41 mastitis-associated S. aureus strains isolated from India. RESULTS: The results elucidate explicit knowledge of 15 diverse sequence types (STs) and five clonal complexes (CCs). The clonal complexes CC8 and CC97 were found to be the predominant genotypes comprising 21 and 10 isolates, respectively. The mean genome size was 2.7 Mbp with a 32.7% average GC content. The pan-genome of the Indian strains of mastitis-associated S. aureus is almost closed. The genome-wide SNP-based phylogenetic analysis differentiated 41 strains into six major clades. Sixteen different spa types were identified, and eight isolates were untypeable. The cgMLST analysis of all S. aureus genome sequences reported from India revealed that S. aureus strain MUF256, isolated from wound fluids of a diabetic patient, was the common ancestor. Further, we observed that all the Indian mastitis-associated S. aureus isolates belonging to the CC97 are mastitis-associated. We identified 17 different antimicrobial resistance (AMR) genes among these isolates, and all the isolates used in this study were susceptible to methicillin. We also identified 108 virulence-associated genes and discuss their associations with different genotypes. CONCLUSION: This is the first study presenting a comprehensive whole genome analysis of bovine mastitis-associated S. aureus isolates from India. Comparative genomic analysis revealed the genome diversity, major genotypes, antimicrobial resistome, and virulome of clinical and subclinical mastitis-associated S. aureus strains.

First complete genome sequence of lumpy skin disease virus directly from a clinical sample in South India.

Lumpy skin disease (LSD), a notifiable disease listed by the World Organization for Animal Health and a fast fast-moving transboundary viral disease infecting cattle and buffaloes, was reported in India in 2019 and has since rapidly spread across the country. This study reports the first complete genome sequence and analysis of a pathogenic LSD virus (LSDV) from India (LSDV/208/PVNRTVU/2020) obtained by direct sequencing of a suspected clinical sample using Illumina and Nanopore sequencing technologies. The complete genome sequence of LSDV/208/PVNRTVU/2020 is 150445 bp long, codes for 156 putative genes and carries identical 2254 bp inverted terminal repeats at either ends. The unique features reported in the LSDV isolates from the recent outbreaks in Asia, namely, the insertions of 12 nucleotides in the viral G-protein coupled receptor (GPCR) and 27 nucleotides leading to duplication of 9 aminoacids in the extracellular enveloped virus-specific (EEV) genes were also conserved in LSDV/208/PVNRTVU/2020. Phylogenetic analysis of the complete genome sequence of LSDV/208/PVNRTVU/2020 revealed its close relation with Kenyan strains and clustered away from vaccine strains. Further analysis showed evidence of strong purifying selection without any recombination events. The data presented in this study could be useful for designing effective strategies such as developing rapid diagnostics and vaccines to control LSD.

Investigating the role of beliefs in influencing the hand-held and hands-free mobile phone use among pedestrians in India.

Mobile phone distraction is a significant contributor to pedestrian injuries. However, mobile phone engagement among pedestrians has been scarcely explored in a developing country like India. The present study utilized the beliefs-based theory of planned behaviour to examine the association between pedestrian beliefs towards distracted walking (behavioural, normative, and control) and their mobile phone use frequencies. Based on a survey of 560 pedestrians (64.6% males), it was found that the major use of mobile phones was for listening to music (30.7%), followed by receiving a call (25%), making a call (18.9%), texting (9.8%), navigation (8.5%) and internet browsing (7.1%). A series of multivariate ANOVAs and logistic regression models were developed to investigate the relationships between the beliefs and frequencies of mobile phone use in hands-free and hand-held conditions. Significant multivariate differences were found for behavioural and normative beliefs in hands-free conditions and all three types of beliefs in hand-held conditions. The frequency of mobile phone use was significantly predicted by normative beliefs (p < 0.001) in the hands-free condition, and by behavioural (p = 0.041) and normative beliefs (p = 0.004) in the hand-held condition. The findings may assist the road safety countermeasures in addressing the issue of pedestrian distraction.

A comparative assessment of subjective experience in simulator and on-road driving under normal and time pressure driving conditions.

This study conducts a comparative assessment of subjective experience of real-world and simulated world driving for investigating factors leading to simulator sickness. Thirty professional car drivers drove a fixed-base driving simulator in real and simulated worlds under No Time Pressure (NTP) and Time Pressure (TP) driving conditions. Drivers rated their perceptions based on real-world driving and simulator driving experiences after each driving session with respect to three factors: simulator sickness, mental workload, and sense of presence. The structural equation model results revealed that drivers experienced high mental workload due to TP driving conditions (factor loading = 0.90) and repeated exposure to simulated world (factor loading = 0.20) which induced simulator sickness (factor loading = 0.41) and resulted in low sense of presence (factor loading = -0.18). Thus, it can be concluded that lack of experience with virtual reality induced high simulator sickness, increased mental workload, and low sense of presence.

COVID-19: A Veterinary and One Health Perspective.

Interface with animals has been responsible for the occurrence of a major proportion of human diseases for the past several decades. Recent outbreaks of respiratory, haemorrhagic, encephalitic, arthropod-borne and other viral diseases have underlined the role of animals in the transmission of pathogens to humans. The on-going coronavirus disease-2019 (COVID-19) pandemic is one among them and is thought to have originated from bats and jumped to humans through an intermediate animal host. Indeed, the aetiology, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can infect and cause disease in cats, ferrets and minks, as well as be transmitted from one animal to another. The seriousness of the pandemic along with the zoonotic origin of the virus has been a red alert on the critical need for collaboration and cooperation among human and animal health professionals, as well as stakeholders from various other disciplines that study planetary health parameters and the well-being of the biosphere. It is therefore imminent that One Health principles are applied across the board for human infectious diseases so that we can be better prepared for future zoonotic disease outbreaks and pandemics.

A narrative review on yoga: a potential intervention for augmenting immunomodulation and mental health in COVID-19.

BACKGROUND: The ongoing novel coronavirus disease 2019 (COVID-19) pandemic has a significant mortality rate of 3-5%. The principal causes of multiorgan failure and death are cytokine release syndrome and immune dysfunction. Stress, anxiety, and depression has been aggravated by the pandemic and its resultant restrictions in day-to-day life which may contribute to immune dysregulation. Thus, immunity strengthening and the prevention of cytokine release syndrome are important for preventing and minimizing mortality in COVID-19 patients. However, despite a few specific remedies that now exist for the SARS-CoV-2virus, the principal modes of prevention include vaccination, masking, and holistic healing methods, such as yoga. Currently, extensive research is being conducted to better understand the neuroendocrinoimmunological mechanisms by which yoga alleviates stress and inflammation. This review article explores the anti-inflammatory and immune-modulating potentials of yoga, along with its role in reducing risk for immune dysfunction and impaired mental health. METHODS: We conducted this narrative review from published literature in MEDLINE, EMBASE, COCHRANE databases. Screening was performed for titles and abstracts by two independent review authors; potentially eligible citations were retrieved for full-text review. References of included articles and articles of major non-indexed peer reviewed journals were searched for relevance by two independent review authors. A third review author checked the excluded records. All disagreements were resolved through discussion amongst review authors or through adjudication by a fourth review author. Abstracts, editorials, conference proceedings and clinical trial registrations were excluded. OBSERVATIONS: Yoga is a nonpharmacological, cost-effective, and safe intervention associated with several health benefits. Originating in ancient India, this vast discipline consists of postures (asanas), breathing techniques (pranayama), meditation (dhyana/dharana), and relaxation. Studies have demonstrated yoga's ability to bolster innate immunity and to inhibit cytokine release syndrome. As an intervention, yoga has been shown to improve mental health, as it alleviates anxiety, depression, and stress and enhances mindfulness, self-control, and self-regulation. Yoga has been correlated with numerous cardioprotective effects, which also may play a role in COVID-19 by preventing lung and cardiac injury. CONCLUSION AND RELEVANCE: This review paves the path for further research on yoga as a potential intervention for enhancing innate immunity and mental health and thus its role in prevention and adjunctive treatment in COVID-19.

Modelling work- and non-work-based trip patterns during transition to lockdown period of COVID-19 pandemic in India.

COVID-19 pandemic has significantly affected the transportation sector across the world. Implementation of lockdown (that includes restricted travel activities) is a prevention strategy executed by various governments to minimize the spread of COVID-19. India went into complete lockdown from 25th March 2020; however, change in commuter's travel behavior was observed from the third week of March (termed as transition to lockdown) due to pandemic fear. In total 1945 participants participated in the travel behaviour survey and their responses with respect to work-based and non-work-based trips during transition period were analysed to understand their adaptation towards COVID-19. The study also attempted to quantify the effects of influencing factors which can explain change in the commuters' travel behaviour. The findings revealed that one-year increment in traveller's age had 2% reduced probability of no travel during transition than pre-transition. For non-work-related travel, chances of lower travel frequency were significantly greater during the transition period as compared to pre-transition. Compared to the non-essential trips, the chances of reduced travel frequency for the essential trips were found to be lower by 92%. By examining these behavioural changes, the present study aims to assist the policymakers in understanding the dynamics of fluctuating travel demand with respect to trip purpose during pandemic situations like COVID-19.

Complete genome sequencing and assessment of mutation-associated protein dynamics of the first Indian bovine ephemeral fever virus (BEFV) isolate.

BACKGROUND: Bovine ephemeral fever (BEF) is a re-emerging disease caused by bovine ephemeral fever virus (BEFV). Although it poses a huge economic threat to the livestock sector, complete viral genome information from any South Asian country, including India, lacks. AIM: Genome characterization of the first Indian BEFV isolate and to evaluate its genetic diversity by characterizing genomic mutations and their associated protein dynamics. MATERIALS AND METHODS: Of the nineteen positive blood samples collected from BEF symptomatic animals during the 2018-19 outbreaks in India, one random sample was used to amplify the entire viral genome by RT-PCR. Utilizing Sanger sequencing and NGS technology, a complete genome was determined. Genome characterization, genetic diversity and phylogenetic analyses were explored by comparing the results with available global isolates. Additionally, unique genomic mutations within the Indian isolate were investigated, followed by in-silico assessment of non-synonymous (NS) mutations impacts on corresponding proteins' secondary structure, solvent accessibility and dynamics. RESULTS: The complete genome of Indian BEFV has 14,903 nucleotides with 33% GC with considerable genetic diversity. Its sequence comparison and phylogenetic analysis revealed a close relatedness to the Middle Eastern lineage. Genome-wide scanning elucidated 30 unique mutations, including 10 NS mutations in the P, L and GNS proteins. The mutational impact evaluation confirmed alterations in protein structure and dynamics, with minimal effect on solvent accessibility. Additionally, alteration in the interatomic interactions was compared against the wild type. CONCLUSION: These findings extend our understanding of the BEFV epidemiological and pathogenic potential, aiding in developing better therapeutic and preventive interventions.

Are you willing to drink and drive? An investigation in Indian scenario using an extended prototype willingness model.

OBJECTIVE: Driving While Intoxicated (DWI) is a significant threat to traffic safety worldwide, but little is known about the motivational factors behind the willingness to drink and drive especially in low- and middle-income countries (LMICs) such as India. The present study applied an extended version of prototype willingness model (PWM) to investigate the factors predicting the willingness to drink and drive. METHODS: One hundred and forty-three participants (77% males) responded to an online survey in India. The survey included the standard PWM constructs such as attitudes, subjective norms, prototype similarity and prototype favourability. Additionally, the measures of traffic fatalism and risk-perceptions were also incorporated to examine their effects on the willingness to drink and drive. RESULTS: The hierarchical regression model showed that the PWM constructs explained 84% of the variance in willingness to drink and drive. When the additional measures (traffic fatalism and risk perception) were included along with the PWM variables, it resulted in an increase of additional 2% of the variance in explaining the willingness, thereby leading to a total of 86%. In both the steps, the variable "attitudes" was found to be the strongest predictor of willingness to drink and drive. CONCLUSIONS: This study is first of its kind to evaluate the effectiveness of PWM model in Indian scenario for understanding the willingness to engage in the act of drunk driving. The study findings may prove useful to the stakeholders of educational and awareness programs, where the focus is required to be aimed at attitudes, subjective norms, prototype similarity and fatalistic beliefs of the drivers.

Analysis of factors influencing aggressive driver behavior and crash involvement.

OBJECTIVE: Aggressive driver behavior is one of the major contributing factors to road crashes. However, the relationship between aggressive driver behavior and crash risk is scarcely explored. The present study focused on quantifying the effect of aggressive driver behavior on crash probability. METHOD AND DATA SOURCES: A sample of 405 Indian drivers were analyzed to model the aggressive driver behavior using self-reported measures. Generalized linear models were developed to quantify the effects of independent variables such as age, gender, personality traits (e.g., driving anger, physical aggression, hostility), and individual predilections to commit violations (e.g., excessive speeding and frequent risky overtaking) on aggressive driver behavior and crash probabilities. RESULTS: K-means clustering technique was applied to the Aggressive Driving Scale (ADS) scores to cluster the drivers into three groups (aggressive, normal, and cautious). Gender was significantly correlated with aggressive driver behavior. Compared to female drivers, male drivers were 2.57 times more likely to engage in aggressive driving. Driver's age was negatively correlated with aggressive driving. With one-year increment in driver's age, the tendency of a driver to engage in aggressive driving was reduced by 26%. In addition, the likelihood of being engaged in aggressive driving was increased by 2.98 times and 2.15 times for the drivers who engage in excessive speeding and frequent risky overtaking, respectively. Driver's personality traits were significantly correlated with aggressive drivers. The crash involvement model showed that aggressive drivers were 2.79 times more likely to be involved in road crashes than cautious drivers. Further, married drivers were 2.17 times less likely to be involved in crashes, whereas for professional drivers the crash involvement probability was increased by 75%. CONCLUSIONS: The results revealed that in addition to age and gender personality traits were significant predictors of driving aggression. Further, the driver's marital status was negatively correlated with the crash involvement and professional drivers were more likely to be involved in crashes than nonprofessional drivers. The study findings can be used in identifying specific risk-prone drivers to provide safety measures via in-vehicle Advanced Driver Assistance Systems (ADAS).

Molecular fingerprinting of bovine mastitis-associated Staphylococcus aureus isolates from India.

Staphylococcus aureus is a major etiological agent of clinical and subclinical bovine mastitis. Owing to the mostly backyard dairy practices, we hypothesized that genetic diversity among mastitis-associated S. aureus from India would be high, and investigated 166 isolates obtained mostly from the Southern State of Karnataka, but also from a few other states. The results revealed (a) 8 to 13 fragments in pulsed-field gel electrophoresis (PFGE), forming 31 distinct patterns, and (b) 34 spa types, of which three (t17680, t18314, and t18320) were newly identified. Multi-locus sequencing typing (MLST) identified 39 sequence types (STs), with ST2454 (34.4%) and ST2459 (24%) being the most commonly represented, which clustered to clonal complexes (CC) CC9 and CC97, respectively; 12 STs were newly identified. Thirty-four (20.5%) of the 166 isolates displayed oxacillin resistance. On the other hand, whereas none were mecC+, 44 (26.5%) isolates were mecA+, with a predominance of SCCmecIVb (26/32 isolates, others being untypeable); 24 isolates (14.46%) were oxacillin-susceptible methicillin-resistant S. aureus (OS-MRSA; mecA+ but OS). Integrated analysis revealed that CC9-ST2454- and CC97-ST2459-SCCmecIVb were the predominant MRSA, although the distribution of CC9 and CC97 was similar between methicillin-resistant and -susceptible isolates. By PCR, 56.25%, 28.75% and 47.5% of the 166 isolates were positive for hlg, tsst and pvl genes, respectively. Our results, for the first time describe the application of a combination of various molecular methods to bovine mastitis-associated S. aureus isolates from India, corroborate the worldwide distribution of CC97 and CC9, and suggest pathogenic potential of the isolates.

Modelling brake transition time of young alcohol-impaired drivers using hazard-based duration models.

Braking performance of drivers is a crucial factor in evaluating the collision patterns and implementing road safety measures. Further, alcohol is known to impair driving control. The present study aims to examine the influence of a comprehensive range of Blood Alcohol Concentration (BAC) levels (0%, 0.03 %, 0.05 % and 0.08 %) on brake transition times of drivers. As young drivers show significantly higher crash risks compared to the experienced drivers, fifty-four young Indian drivers in the age group of 21-25 years (forty males and fourteen females) participated in the driving simulator experiments. The study adopted the framework of a within-subjects design, where each driver encountered rural and urban driving scenarios in a counterbalanced order, during experimental driving at each of the four BAC levels. Their brake transition times were estimated with respect to sudden pedestrian crossing events. Weibull Accelerated Failure Time (AFT) models with shared frailty were developed for quantifying the effects of BAC levels along with driver attributes on brake transition time. Preliminary analysis showed significant main effects of BAC (p < 0.001) and driving environment (p = 0.002) on brake transition time; however, their interaction effect was not significant (p = 0.485). The models revealed that 0.03 %, 0.05 % and 0.08 % BACs significantly reduced the brake transition times by 16 %, 28 % and 52 % in rural driving environment, and by 23 %, 37 % and 53 % in urban driving environment, compared to 0% BAC. The study outcomes may find application in assisting collision warning systems which take into account the braking behaviour of drivers.

Modeling the influence of 0.03%, 0.05% and 0.08% blood alcohol concentrations on lane positioning and steering control of Indian drivers.

OBJECTIVE: Alcohol-impairment of drivers has significant influence on road traffic safety; however, no experimental research has been conducted on the lateral driving control of Indian drivers. Therefore, the present study investigates the effects of different Blood Alcohol Concentration (BAC) levels (0%, 0.03%, 0.05% and 0.08%) and driver attributes on the lane positioning and steering control of Indian drivers. METHOD: A driving simulator experiment was designed where 82 adult licensed drivers (62 males, 20 females) completed driving on the simulated urban arterial road environment under the influence of varying BACs. Lateral driving performance measures associated with lane positioning (lane position variability) and steering control of drivers (steering angle variability and steering reversal rates (SRR)) were analyzed. RESULTS: The findings reported that lane position variability was significantly influenced only at 0.08% BAC (5.8% increment); no significant influence was observed at 0.03% and 0.05% BACs. Compared to 0% BAC, steering angle variability increased by 0.105 degrees or 15.7%, 0.142 degrees or 21.4%, and 0.176 degrees or 25.7% at 0.03%, 0.05% and 0.08% BACs respectively. No significant differences were observed between the sober-state driving and alcohol-impaired driving at 1ο and 5ο SRR. However, 10ο SRR was found to be higher by 36%, 65% and 92% at 0.03%, 0.05% and 0.08% BACs compared to 0% BAC. Among the driver attributes, male drivers displayed higher impairment in lane positioning behavior as compared to female drivers. One-year increase in drivers' age reduced the lane position variability by 0.6%. Drivers who performed regular physical exercise were found to have better lateral control on the vehicle, as observed in their steering angle variability. Prior crash history had negative association with the steering reversal rates, indicating that drivers who have previous experience of crash involvement show better steering control than the drivers without any crash experience. CONCLUSION: In conclusion, the outcomes of this study provide novel insights into the alcohol-impaired lateral vehicle control of Indian drivers which can assist in policy interventions aiming to reduce crashes with alcohol as a major crash causation factor.

Sleep-deprived car-following: Indicators of rear-end crash potential.

Safety assessment among sleep-deprived drivers is a challenging research area with only a few sleep-related studies investigating safety performance during car-following. Therefore, this study aimed to measure the effects of partial sleep deprivation on driver safety during car-following. Fifty healthy male drivers with no prior history of any sleep-related disorders, drove the driving simulator in three conditions of varying sleep duration: a baseline (no sleep deprivation), test session (TS1) after one night of PSD (sleep ≤4.5 h/night) and TS2 after two consecutive nights of PSD. The reduced sleep in PSD sessions was monitored using an Actiwatch. Karolinska Sleepiness Scale was used to indicate loss of alertness among drivers. Each drive included a car-following task to measure longitudinal safety indicators based on speed and headway management: normalized time exposed to critical gap (TECG'), safety critical time headway and speed variability with respect to leading vehicle's speed (SPV). Crash potential index (CPI) was also determined from deceleration rate of drivers during car-following and was found correlated with other indicators. Therefore, to determine the aggregate influence of PSD on safety during car-following, CPI was modelled in terms of TECG, SPV, THW and other covariates. All safety metrics were modelled using generalized mixed effects regression models. The results showed that compared to the baseline drive, critical time headway decreased by 0.65 and 1.08 times whereas speed variability increased by 1.34 and 1.28 times during the TS1 and TS2, respectively, both indicating higher crash risk. However, decrease in TECG' by 64 % and 56 % during TS1 and TS2, respectively indicate compensatory measures to avoid risks due to sleep loss. A fractional regression model of crash potential revealed that low time-headway and higher speed variability and high time exposed to critical gap (TECG') significantly contribute to higher CPI values indicating higher safety risk. Other covariates such as sleep duration, professional driving experience and history of traffic violations were also associated with safety indicators and CPI, however no significant effects of age were noticed in the study. The study findings present the safety indicators sensitive to rear-end crashes specifically under PSD conditions, which can be used in designing collisions avoidance systems and strategies to improve overall traffic safety.

Exploring the effectiveness of a digital voice assistant to maintain driver alertness in partially automated vehicles.

OBJECTIVE: Vehicle automation shifts the driver's role from active operator to passive observer at the potential cost of degrading their alertness. This study investigated the role of an in-vehicle voice-based assistant (VA; conversing about traffic/road environment) to counter the disengaging and fatiguing effects of automation. METHOD: Twenty-four participants undertook two drives- with and without VA in a partially automated vehicle. Participants were subsequently categorized into high and low participation groups (based on their proportion of vocal exchanges with VA). The effectiveness of VA was assessed based on driver alertness measured using Karolinska Sleepiness Scale (KSS), eye-based sleepiness indicators and glance behavior, NASA-TLX workload rating and time to gain motor readiness in response to take-over request and performance rating made by the drivers. RESULTS: Paired samples t-tests comparison of alertness measures across the two drives were conducted. Lower KSS rating, larger pupil diameter, higher glances (rear-mirror, roadside vehicles and signals in the drive with VA) and higher feedback ratings of VA indicated the efficiency of VA in improving driver alertness during automation. However, there was no significant difference in alertness or glance behavior between the driver groups (high and low-PR), although the time to resume steering control was significantly lower in the higher engagement group. CONCLUSION: The study successfully demonstrated the advantages of using a voice assistant (VA) to counter these effects of passive fatigue, for example, by reducing the time to gain motor-readiness following a TOR. The findings show that despite the low engagement in spoken conversation, active listening also positively influenced driver alertness and awareness during the drive in an automated vehicle.