Negative mood induction increases choice of heroin versus food pictures in opiate-dependent individuals: Correlation with self-medication coping motives and subjective reactivity

This is the final version. Available from Frontiers Media via the DOI in this record.Acute growth in negative affect is thought to play a major role in triggering relapse in opiate-dependent individuals. Consistent with this view, three lab studies have demonstrated that negative mood induction increases opiate craving in opiate-dependent individuals. The current study sought to confirm these effects with a behavioral measure of heroin seeking, and test whether the effect is associated with self-reported opiate use to cope with negative affect and subjective reactivity to mood induction. Participants were heroin-dependent individuals engaged with treatment services (n = 47) and control participants (n = 25). Heroin users completed a questionnaire assessing reasons for using heroin: negative affect, social pressure, and cued craving. Baseline heroin choice was measured by preference to enlarge heroin versus food thumbnail pictures in two-alternative forced-choice trials. Negative mood was then induced by depressive statements and music before heroin choice was tested again. Subjective reactivity was indexed by negative and positive mood reported at the pre-induction to post-test timepoints. Heroin users chose heroin images more frequently than controls overall (p = .001) and showed a negative mood-induced increase in heroin choice compared to control participants (interaction p < .05). Mood-induced heroin choice was associated with self-reported heroin use to cope with negative affect (p < .05), but not social pressure (p = .39) or cued craving (p = .52), and with subjective mood reactivity (p = .007). These data suggest that acute negative mood is a trigger for heroin seeking in heroin-dependent individuals, and this effect is pronounced in those who report using heroin to cope with negative affect, and those who show greater subjective reactivity to negative triggers. Interventions should seek to target negative coping motives to build resilience to affective triggers for relapse.Alcohol Research U

Crowdsourcing/Winter Operations Dashboard Upgrade

INDOT has recently completed the deployment of Parsons telematics-based dash-cameras, automatic vehicle locator (AVL) positions, and spreader rate monitoring across their winter operations fleet. The motivation of this study was to develop dashboards that integrate connected vehicle data into the real-time monitoring and after-action review of winter storms. Each month approximately 13 billion connected vehicle records are ingested for the state of Indiana and almost 99 billion weather data records are ingested nationwide in 15-minute intervals. This study developed techniques to utilize this connected vehicle data and weather data to monitor real-time mobility of interstates and post storm after-action assessments to identify improvement opportunities of winter operations activities. In multiple instances, these agile reviews have influenced operational changes in snow removal and maintenance around the state, leading to a marked improvement in observed mobility and safety

Salt Monitoring and Reporting Technology (SMART) for Salt Stockpile Inventory Reporting

Transportation agencies in northern environments spend a considerable amount of their budget on salt for winter operations. For example, in the state of Indiana, there are approximately 120 salt storage facilities distributed throughout the state and the state expends between 30 M USD and 60 M USD on inventory and delivery each year. Historical techniques of relying on visual estimates of salt stockpiles can be inaccurate and unhelpful for managing the supply chain during the winter or planning for re-supply during the summer months. This project report describes the implementation of a portable and permanent LiDAR system that can be used to inventory indoor stockpiles of salt in under 15 min and describes how this system has been deployed over 300 times at over 120 facilities. A quick and easy accuracy test, based on the conservation of volume, was used to provide an independent check on the system performance by repositioning portions of the salt pile. Those tests indicated stockpile volumes can be estimated with an accuracy of 1%–3% of indicated stockpile volumes. The report concludes by discussing how this technology can be permanently installed for systematic monitoring throughout the year

Prioritizing Roadway Pavement Marking Maintenance Using Lane Keep Assist Sensor Data

There are over four million miles of roads in the United States, and the prioritization of locations to perform maintenance activities typically relies on human inspection or semi-automated dedicated vehicles. Pavement markings are used to delineate the boundaries of the lane the vehicle is driving within. These markings are also used by original equipment manufacturers (OEM) for implementing advanced safety features such as lane keep assist (LKA) and eventually autonomous operation. However, pavement markings deteriorate over time due to the fact of weather and wear from tires and snowplow operations. Furthermore, their performance varies depending upon lighting (day/night) as well as surface conditions (wet/dry). This paper presents a case study in Indiana where over 5000 miles of interstate were driven and LKA was used to classify pavement markings. Longitudinal comparisons between 2020 and 2021 showed that the percentage of lanes with both lines detected increased from 80.2% to 92.3%. This information can be used for various applications such as developing or updating standards for pavement marking materials (infrastructure), quantifying performance measures that can be used by automotive OEMs to warn drivers of potential problems with identifying pavement markings, and prioritizing agency pavement marking maintenance activities

Leveraging LiDAR Intensity to Evaluate Roadway Pavement Markings

The United States has over 8.8 million lane miles nationwide, which require regular maintenance and evaluations of sign retroreflectivity, pavement markings, and other pavement information. Pavement markings convey crucial information to drivers as well as connected and autonomous vehicles for lane delineations. Current means of evaluation are by human inspection or semi-automated dedicated vehicles, which often capture one to two pavement lines at a time. Mobile LiDAR is also frequently used by agencies to map signs and infrastructure as well as assess pavement conditions and drainage profiles. This paper presents a case study where over 70 miles of US-52 and US-41 in Indiana were assessed, utilizing both a mobile retroreflectometer and a LiDAR mobile mapping system. Comparing the intensity data from LiDAR data and the retroreflective readings, there was a linear correlation for right edge pavement markings with an R2 of 0.87 and for the center skip line a linear correlation with an R2 of 0.63. The p-values were 0.000 and 0.000, respectively. Although there are no published standards for using LiDAR to evaluate pavement marking retroreflectivity, these results suggest that mobile LiDAR is a viable tool for network level monitoring of retroreflectivity

Leveraging LiDAR Intensity to Evaluate Roadway Pavement Markings

The United States has over 8.8 million lane miles nationwide, which require regular maintenance and evaluations of sign retroreflectivity, pavement markings, and other pavement information. Pavement markings convey crucial information to drivers as well as connected and autonomous vehicles for lane delineations. Current means of evaluation are by human inspection or semi-automated dedicated vehicles, which often capture one to two pavement lines at a time. Mobile LiDAR is also frequently used by agencies to map signs and infrastructure as well as assess pavement conditions and drainage profiles. This paper presents a case study where over 70 miles of US-52 and US-41 in Indiana were assessed, utilizing both a mobile retroreflectometer and a LiDAR mobile mapping system. Comparing the intensity data from LiDAR data and the retroreflective readings, there was a linear correlation for right edge pavement markings with an R2 of 0.87 and for the center skip line a linear correlation with an R2 of 0.63. The p-values were 0.000 and 0.000, respectively. Although there are no published standards for using LiDAR to evaluate pavement marking retroreflectivity, these results suggest that mobile LiDAR is a viable tool for network level monitoring of retroreflectivity

Cue-elicited craving and human Pavlovian-to-instrumental transfer

Background: Drug cue-reactivity can be measured by the well-established cue-elicited craving model, or by the more recently developed Pavlovian to instrumental transfer (PIT) procedure, which quantifies the impact of drug cues on drug-seeking behaviour. It remains unclear whether these two models produce similar cue reactive effects. Method: To test this, 38 young adult beer drinkers completed an alcohol cue-elicited craving procedure followed by a specific PIT procedure with alcohol cues. Results: There was a significant effect of alcohol cues on craving (p =.007) and on alcohol-seeking behaviour in the PIT procedure (p <.001). Contrary to expectations, these two indices of cue-reactivity were not correlated (r = −.08, p =.66). However, analysis indicated that the alcohol PIT effect was correlated with the self-reported belief that alcohol cues signalled greater effectiveness of the alcohol-seeking response (r =.44, p =.008). Conclusions: These findings suggest that different measures of cue-reactivity might tap into different responses within an individual. Future research is necessary to consider whether this variance is due to which aspect of cue reactivity is being assessed and whether different types of cue-reactivity are differentially influenced by variables such as outcome expectancy

Measuring Roadway Lane Widths Using Connected Vehicle Sensor Data

The United States has over three trillion vehicle miles of travel annually on over four million miles of public roadways, which require regular maintenance. To maintain and improve these facilities, agencies often temporarily close lanes, reconfigure lane geometry, or completely close the road depending on the scope of the construction project. Lane widths of less than 11 feet in construction zones can impact highway capacity and crash rates. Crash data can be used to identify locations where the road geometry could be improved. However, this is a manual process that does not scale well. This paper describes findings for using data from onboard sensors in production vehicles for measuring lane widths. Over 200 miles of roadway on US-52, US-41, and I-65 in Indiana were measured using vehicle sensor data and compared with mobile LiDAR point clouds as ground truth and had a root mean square error of approximately 0.24 feet. The novelty of these results is that vehicle sensors can identify when work zones use lane widths substantially narrower than the 11 foot standard at a network level and can be used to aid in the inspection and verification of construction specification conformity. This information would contribute to the construction inspection performed by agencies in a safer, more efficient way

Pavement Quality Evaluation Using Connected Vehicle Data

Modern vehicles have extensive instrumentation that can be used to actively assess the condition of infrastructure such as pavement markings, signs, and pavement smoothness. Currently, pavement condition evaluations are performed by state and federal officials typically using the industry standard of the International Roughness Index (IRI) or visual inspections. This paper looks at the use of on-board sensors integrated in Original Equipment Manufacturer (OEM) connected vehicles to obtain crowdsource estimates of ride quality using the International Rough Index (IRI). This paper presents a case study where over 112 km (70 mi) of Interstate-65 in Indiana were assessed, utilizing both an inertial profiler and connected production vehicle data. By comparing the inertial profiler to crowdsourced connected vehicle data, there was a linear correlation with an R2 of 0.79 and a p-value of <0.001. Although there are no published standards for using connected vehicle roughness data to evaluate pavement quality, these results suggest that connected vehicle roughness data is a viable tool for network level monitoring of pavement quality

Cognitive function in schizophrenia: conflicting findings and future directions

INTRODUCTION: Schizophrenia is a severe mental disorder with multiple psychopathological domains being affected. Several lines of evidence indicate that cognitive impairment serves as the key component of schizophrenia psychopathology. Although there have been a multitude of cognitive studies in schizophrenia, there are many conflicting results. We reasoned that this could be due to individual differences among the patients (i.e. variation in the severity of positive vs. negative symptoms), different task designs, and/or the administration of different antipsychotics. METHODS: We thus review existing data concentrating on these dimensions, specifically in relation to dopamine function. We focus on most commonly used cognitive domains: learning, working memory, and attention. RESULTS: We found that the type of cognitive domain under investigation, medication state and type, and severity of positive and negative symptoms can explain the conflicting results in the literature. CONCLUSIONS: This review points to future studies investigating individual differences among schizophrenia patients in order to reveal the exact relationship between cognitive function, clinical features, and antipsychotic treatment