Encoding of self-initiated actions in axon terminals of the mesocortical pathway.

Significance: The initiation of goal-directed actions is a complex process involving the medial prefrontal cortex and dopaminergic inputs through the mesocortical pathway. However, it is unclear what information the mesocortical pathway conveys and how it impacts action initiation. In this study, we unveiled the indispensable role of mesocortical axon terminals in encoding the execution of movements in self-initiated actions. Aim: To investigate the role of mesocortical axon terminals in encoding the execution of movements in self-initiated actions. Approach: We designed a lever-press task in which mice internally determine the timing of the press, receiving a larger reward for longer waiting periods. Results: Our study revealed that self-initiated actions depend on dopaminergic signaling mediated by D2 receptors, whereas sensory-triggered lever-press actions do not involve D2 signaling. Microprism-mediated two-photon calcium imaging further demonstrated ramping activity in mesocortical axon terminals approximately 0.5 s before the self-initiated lever press. Remarkably, the ramping patterns remained consistent whether the mice responded to cues immediately for a smaller reward or held their response for a larger reward. Conclusions: We conclude that mesocortical dopamine axon terminals encode the timing of self-initiated actions, shedding light on a crucial aspect of the intricate neural mechanisms governing goal-directed behavior.

A surrogate approach for estimating vehicle-related emissions under heterogenous traffic conditions.

Vehicle emission analysis currently faces a trade-off between easy-to-use, low-accuracy macroscopic models, and computationally intensive, high-accuracy microscopic models. In this study, we develop a surrogate model that leverages microscopic traffic and emission simulations to predict link-level emission rates. The input variables are obtained by aggregating 1 Hz simulated vehicle trajectories into hourly traffic condition factors (e.g., link average/variation of speed, truck fleet percentage, road grade, etc.). The emission ground truth data are generated using the Motor Vehicle Emission Simulator (MOVES) opmode-based analysis module. We explore different parameter and machine learning model structures to establish the statistical relationship of the input variables and the link-level emission rates. We demonstrate the ability of our model to accurately estimate vehicle-related emissions by using the Columbia, South Carolina road network as an example. This model can serve as a high-level planning tool to assess the impacts of emissions from transportation projects.Implications: Vehicle emission analysis is facing trade-offs between easy-to-use macroscopic emission models with low accuracy and computationally intensive microscopic models with high accuracy. Existing studies attempted to cope with the trade-off by pre-selecting representative emission rates but are still subject to the risk of not considering differentiated traffic patterns by using single emission rate. To fill in the knowledge gap in the literature, we develop a surrogate approach that fully integrates driving trajectories of heterogenous traffic patterns into a link-level emissions estimation model considering road characteristics. The model can achieve high accuracy and utilize publicly available traffic data in vehicle emission prediction. We apply the proposed model in a middle size city road network and demonstrate its capability to capture and quantify the impacts of traffic patterns on link-level vehicle-related emissions. Additionally, the proposed model can serve as a sketch planning tool for researchers and transportation air quality practitioners to quickly assess bounds of emissions benefits due to traffic operational and transportation strategies.

Assessing the likelihood of secondary crashes on freeways with Adaptive Signal Control System deployed on alternate routes.

INTRODUCTION: Reducing the likelihood of freeway secondary crashes will provide significant safety, operational and environmental benefits. This paper presents a method for assessing the likelihood of freeway secondary crashes with Adaptive Signal Control Systems (ASCS) deployed on alternate routes that are typically used by diverted freeway traffic to avoid any delay or congestion due to a freeway primary crash. METHOD: The method includes four steps: (1) identification of secondary crashes, (2) verification of alternate routes, (3) assessment of the likelihood of secondary crashes for freeways with ASCS deployed on alternate routes and non-ASCS (i.e. pre-timed, semi- or fully-actuated) alternate routes, and (4) investigation of unobserved heterogeneity of the likelihood of freeway secondary crashes. Four freeway sections (i.e., two with ASCS deployed on alternate routes and two non-ASCS alternate routes) in South Carolina are considered. RESULTS AND CONCLUSIONS: Findings from the logistic regression modeling reveal significant reduction in the likelihood of secondary crashes for one freeway section (i.e., Charleston I-26 E) with ASCS deployed on alternate route. Other factors such as rear-end crash, dark or limited light, peak period, and annual average daily traffic contribute to the likelihood of freeway secondary crashes. Furthermore, random-parameter logistic regression model results for Charleston I-26 E reveal that unobserved heterogeneity of ASCS effect exists across the observations and ASCS are associated with the reduction of the likelihood of freeway secondary crashes for 84% of the observations (i.e., primary crashes). Location of the primary crash on the freeway is observed to affect the benefit of ASCS toward freeway secondary crash reduction as the primary crash's location determines how many upstream freeway vehicles will be able to take the alternate route. Practical Applications: Based on the findings, it is recommended that the South Carolina Department of Transportation (SCDOT) considers deploying ASCS on alternate routes parallel to freeway sections where high percentages of secondary crashes are found.

Injury severity analysis of truck-involved crashes under different weather conditions.

This paper investigates truck-involved crashes to determine the statistically significant factors that contribute to injury severity under different weather conditions. The analysis uses crash data from the state of Ohio between 2011 and 2015 available from the Highway Safety Information System. To determine if weather conditions should be considered separately for truck safety analyses, parameter transferability tests are conducted; the results suggest that weather conditions should be modeled separately with a high level of statistical confidence. To this end, three separate mixed logit models are estimated for three different weather conditions: normal, rain and snow. The estimated models identify a variety of statistically significant factors influencing the injury severity. Different weather conditions are found to have different contributing effects on injury severity in truck-involved crashes. Rural, rear-end and sideswipe crash parameters were found to have significantly different levels of impact on injury severity. Based on the findings of this study, several countermeasures are suggested: 1) safety and enforcement programs should focus on female truck drivers, 2) a variable speed limit sign should be used to lower speeds of trucks during rainy condition, and 3) trucks should be restricted or prohibited on non-interstates during rainy and snowy conditions. These countermeasures could reduce the number and severity of truck-involved crashes under different weather conditions.

Assessing operating room turnover time via the use of mobile application.

BACKGROUND: Improving operating room (OR) utilization is crucial to hospitals. This study examines the effectiveness of a mobile application co-developed with hospital staff to track OR turnover time (TOT). METHODS: An Android-based app, named ORTimer, was used by staff in two OR units (GI-Lab and D-Core) of Greenville Memorial Hospital (GMH) in South Carolina. The staff used the app to record milestones and note delay reasons (if applicable). A total of 1,782 turnover observations from the GI-Lab and 694 turnover observations from the D-Core were collected for the study. Using data collected from the app and additional information from GMH's electronic medical record system, a two-sample proportionality test was conducted to test the hypothesis that the use of the app improved OR turnover performance (i.e., the TOT is equal to or less than the allotted time). RESULTS: The result of the hypothesis test indicates that a higher percentage of observations in the GI-Lab and D-Core met their turnover target time when the ORTimer app was used. Additionally, multiple regression analysis was used to identify significant factors that contribute to prolonged OR TOT and to estimate their impacts. CONCLUSIONS: The app serves as both a visual management tool as well as a TOT data collection tool. By identifying barriers to the on-time completion of the turnaround, the app allows for continuous improvement of the turnover process.

Truck-involved crashes injury severity analysis for different lighting conditions on rural and urban roadways.

This paper investigates factors affecting injury severity of crashes involving trucks for different lighting conditions on rural and urban roadways. It uses 2009-2013 Ohio crash data from the Highway Safety Information System. The explanatory factors include the occupant, vehicle, collision, roadway, temporal and environmental characteristics. Six separate mixed logit models were developed considering three lighting conditions (daylight, dark, and dark-lighted) on two area types (rural and urban). A series of log-likelihood ratio tests were conducted to validate that these six separate models by lighting conditions and area types are warranted. The model results suggest major differences in both the combination and the magnitude of impact of variables included in each model. Some variables were significant only in one lighting condition but not in other conditions. Similarly, some variables were found to be significant in one area type but not in other area type. These differences show that the different lighting conditions and area types do in fact have different contributing effects on injury severity in truck-involved crashes, further highlighting the importance of examining crashes based on lighting conditions on rural and urban roadways. Age and gender of occupant (who is the most severely injured in a crash), truck types, AADT, speed, and weather condition were found to be factors that have significantly different levels of impact on injury severity in truck-involved crashes.

Assessment of the Nurse Medication Administration Workflow Process.

This paper presents findings of an observational study of the Registered Nurse (RN) Medication Administration Process (MAP) conducted on two comparable medical units in a large urban tertiary care medical center in Columbia, South Carolina. A total of 305 individual MAP observations were recorded over a 6-week period with an average of 5 MAP observations per RN participant for both clinical units. A key MAP variation was identified in terms of unbundled versus bundled MAP performance. In the unbundled workflow, an RN engages in the MAP by performing only MAP tasks during a care episode. In the bundled workflow, an RN completes medication administration along with other patient care responsibilities during the care episode. Using a discrete-event simulation model, this paper addresses the difference between unbundled and bundled workflow and their effects on simulated redesign interventions.

Assessment of the Nurse Medication Administration Workflow Process

This paper presents findings of an observational study of the Registered Nurse (RN) Medication Administration Process (MAP) conducted on two comparable medical units in a large urban tertiary care medical center in Columbia, South Carolina. A total of 305 individual MAP observations were recorded over a 6-week period with an average of 5 MAP observations per RN participant for both clinical units. A key MAP variation was identified in terms of unbundled versus bundled MAP performance. In the unbundled workflow, an RN engages in the MAP by performing only MAP tasks during a care episode. In the bundled workflow, an RN completes medication administration along with other patient care responsibilities during the care episode. Using a discrete-event simulation model, this paper addresses the difference between unbundled and bundled workflow and their effects on simulated redesign interventions.

Computer Simulation Shows the Effect of Communication on Day of Surgery Patient Flow.

To improve patient flow in a surgical environment, practitioners and academicians often use process mapping and simulation as tools to evaluate and recommend changes. We used simulations to help staff visualize the effect of communication and coordination delays that occur on the day of surgery. Perioperative services staff participated in tabletop exercises in which they chose the delays that were most important to eliminate. Using a day-of-surgery computer simulation model, the elimination of delays was tested and the results were shared with the group. This exercise, repeated for multiple groups of staff, provided an understanding of not only the dynamic events taking place, but also how small communication delays can contribute to a significant loss in efficiency and the ability to provide timely care. Survey results confirmed these understandings.

Translation of small open reading frames within unannotated RNA transcripts in Saccharomyces cerevisiae.

High-throughput gene expression analysis has revealed a plethora of previously undetected transcripts in eukaryotic cells. In this study, we investigate >1,100 unannotated transcripts in yeast predicted to lack protein-coding capacity. We show that a majority of these RNAs are enriched on polyribosomes akin to mRNAs. Ribosome profiling demonstrates that many bind translocating ribosomes within predicted open reading frames 10-96 codons in size. We validate expression of peptides encoded within a subset of these RNAs and provide evidence for conservation among yeast species. Consistent with their translation, many of these transcripts are targeted for degradation by the translation-dependent nonsense-mediated RNA decay (NMD) pathway. We identify lncRNAs that are also sensitive to NMD, indicating that translation of noncoding transcripts also occurs in mammals. These data demonstrate transcripts considered to lack coding potential are bona fide protein coding and expand the proteome of yeast and possibly other eukaryotes.

Effective healthcare process redesign through an interdisciplinary team approach.

Healthcare process redesign is a complex and often high risk undertaking. Typically, there is a limited understanding of the baseline process and often inadequate tools by which to assess it. This can be confounded by narrow redesign team expertise that can result in unanticipated and/or unintended redesign consequences. Interdisciplinary research teams of healthcare, biostatistics, engineering and computer science experts provide broad support for a more effective and safer approach to healthcare process redesign. We describe an interdisciplinary research team focused on medication administration process (MAP)redesign and its achievements and challenges.

Analysis of driver injury severity in rural single-vehicle crashes.

Rural roads carry less than fifty percent of the traffic in the United States. However, more than half of the traffic accident fatalities occurred on rural roads. This research focuses on analyzing injury severities involving single-vehicle crashes on rural roads, utilizing a latent class logit (LCL) model. Similar to multinomial logit (MNL) models, the LCL model has the advantage of not restricting the coefficients of each explanatory variable in different severity functions to be the same, making it possible to identify the impacts of the same explanatory variable on different injury outcomes. In addition, its unique model structure allows the LCL model to better address issues pertinent to the independence from irrelevant alternatives (IIA) property. A MNL model is also included as the benchmark simply because of its popularity in injury severity modeling. The model fitting results of the MNL and LCL models are presented and discussed. Key injury severity impact factors are identified for rural single-vehicle crashes. Also, a comparison of the model fitting, analysis marginal effects, and prediction performance of the MNL and LCL models are conducted, suggesting that the LCL model may be another viable modeling alternative for crash-severity analysis.

Sulfonylurea receptor as a target for molecular imaging of pancreas beta cells with (99m)Tc-DTPA-glipizide.

OBJECTIVE: This study was aimed to assess pancreas beta cell activity using (99m)Tc-diethyleneaminepentaacetic acid-glipizide (DTPA-GLP), a sulfonylurea receptor agent. The effect of DTPA-GLP on the blood glucose level in rats was also evaluated. METHODS: DTPA dianhydride was conjugated with GLP in the presence of sodium amide, yielding 60%. Biodistribution and planar images were obtained at 30-120 min after injection of (99m)Tc-DTPA-GLP (1 mg/rat, 0.74 and 11.1 MBq per rat, respectively) in normal female Fischer 344 rats. The control group was given (99m)Tc-DTPA. To demonstrate pancreas beta cell uptake of (99m)Tc-DTPA-GLP via a receptor-mediated process, a group of rats was pretreated with streptozotocin (a beta cell toxin, 55 mg/kg, i.v.) and the images were acquired at immediately-65 min on day 5 post-treatment. The effect on the glucose levels after a single administration (ip) of DTPA-GLP was compared to glipizide (GLP) for up to 6 h. RESULTS: The structure of DTPA-GLP was confirmed by NMR, mass spectrometry and HPLC. Radiochemical purity assessed by ITLC was >96%. (99m)Tc-DTPA-GLP showed increased pancreas-to-muscle ratios, whereas (99m)Tc-DTPA showed decreased ratios at various time points. Pancreas could be visualized with (99m)Tc-DTPA-GLP in normal rat, however, (99m)Tc-DTPA has poor uptake suggesting the specificity of (99m)Tc-DTPA-GLP. Pancreas beta cell uptake could be blocked by pre-treatment with streptozotocin. DTPA-GLP showed an equal or better response in lowering the glucose levels compared to the existing GLP drug. CONCLUSIONS: It is feasible to use (99m)Tc-DTPA-GLP to assess pancreas beta cell receptor recognition. (99m)Tc-DTPA-GLP may be helpful in evaluating patients with diabetes, pancreatitis and pancreatic tumors.

The use of a mobile application to track process workflow in perioperative services.

This article discusses the data collection tool developed to investigate how patient flow is affected by the delivery of different types of care within Perioperative Services. To better understand the Perioperative Services processes, this study tracked staff members as they perform their activities. A challenging aspect of documenting the processes observed while tracking the Perioperative Services staff is to record the specific times and order in which the activities took place. The Perioperative Services is a fast-paced, dynamic environment where the staff members often perform multiple tasks that may also be interrupted, and each staff member may perform these tasks in their own sequence. To meet the needs of accurate data gathering, an iPhone/iPod Touch application was developed. It provides several advantages over the traditional paper/pencil method: (1) time stamps are instantaneous and consistent among the data collectors, (2) activities are entered via swipe-and-click capability, (3) multiple active tasks and interruptions can be tracked, and (4) collected data can be output to Microsoft Excel or Access for analysis. The "app" has proven to be useful in capturing data for our study. This technology can be customized and applied to similar settings at other hospitals.