41 research outputs found

    Methodological evolution and frontiers of identifying, modeling and preventing secondary crashes on highways

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    © 2018 Elsevier Ltd Secondary crashes (SCs) or crashes that occur within the boundaries of the impact area of prior, primary crashes are one of the incident types that frequently affect highway traffic operations and safety. Existing studies have made great efforts to explore the underlying mechanisms of SCs and relevant methodologies have been e volving over the last two decades concerning the identification, modeling, and prevention of these crashes. So far there is a lack of a detailed examination on the progress, lessons, and potential opportunities regarding existing achievements in SC-related studies. This paper provides a comprehensive investigation of the state-of-the-art approaches; examines their strengths and weaknesses; and provides guidance in exploiting new directions in SC-related research. It aims to support researchers and practitioners in understanding well-established approaches so as to further explore the frontiers. Published studies focused on SCs since 1997 have been identified, reviewed, and summarized. Key issues concentrated on the following aspects are discussed: (i) static/dynamic approaches to identify SCs; (ii) parametric/non-parametric models to analyze SC risk, and (iii) deployable countermeasures to prevent SCs. Based on the examined issues, needs, and challenges, this paper further provides insights into potential opportunities such as: (a) fusing data from multiple sources for SC identification, (b) using advanced learning algorithms for real-time SC analysis, and (c) deploying connected vehicles for SC prevention in future research. This paper contributes to the research community by providing a one-stop reference for research on secondary crashes

    Secondary collisions and injury severity: A joint analysis using structural equation models

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    <p><b>Objective</b>: This study aims to investigate the contributing factors to secondary collisions and the effects of secondary collisions on injury severity levels. Manhattan, which is the most densely populated urban area of New York City, is used as a case study. In Manhattan, about 7.5% of crash events become involved with secondary collisions and as high as 9.3% of those secondary collisions lead to incapacitating and fatal injuries.</p> <p><b>Methods</b>: Structural equation models (SEMs) are proposed to jointly model the presence of secondary collisions and injury severity levels and adjust for the endogeneity effects. The structural relationship among secondary collisions, injury severity, and contributing factors such as speeding, alcohol, fatigue, brake defects, limited view, and rain are fully explored using SEMs. In addition, to assess the temporal effects, we use time as a moderator in the proposed SEM framework.</p> <p><b>Results</b>: Due to its better performance compared with other models, the SEM with no constraint is used to investigate the contributing factors to secondary collisions. Thirteen explanatory variables are found to contribute to the presence of secondary collisions, including alcohol, drugs, inattention, inexperience, sleep, control disregarded, speeding, fatigue, defective brakes, pedestrian involved, defective pavement, limited view, and rain. Regarding the temporal effects, results indicate that it is more likely to sustain secondary collisions and severe injuries at night.</p> <p><b>Conclusions</b>: This study fully investigates the contributing factors to secondary collisions and estimates the safety effects of secondary collisions after adjusting for the endogeneity effects and shows the advantage of using SEMs in exploring the structural relationship between risk factors and safety indicators. Understanding the causes and impacts of secondary collisions can help transportation agencies and automobile manufacturers develop effective injury prevention countermeasures.</p

    Mild Blast Events Alter Anxiety, Memory, and Neural Activity Patterns in the Anterior Cingulate Cortex

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    <div><p>There is a general interest in understanding of whether and how exposure to emotionally traumatizing events can alter memory function and anxiety behaviors. Here we have developed a novel laboratory-version of mild blast exposure comprised of high decibel bomb explosion sound coupled with strong air blast to mice. This model allows us to isolate the effects of emotionally fearful components from those of traumatic brain injury or bodily injury typical associated with bomb blasts. We demonstrate that this mild blast exposure is capable of impairing object recognition memory, increasing anxiety in elevated O-maze test, and resulting contextual generalization. Our <i>in vivo</i> neural ensemble recording reveal that such mild blast exposures produced diverse firing changes in the anterior cingulate cortex, a region processing emotional memory and inhibitory control. Moreover, we show that these real-time neural ensemble patterns underwent post-event reverberations, indicating rapid consolidation of those fearful experiences. Identification of blast-induced neural activity changes in the frontal brain may allow us to better understand how mild blast experiences result in abnormal changes in memory functions and excessive fear generalization related to post-traumatic stress disorder.</p></div

    Mice experienced mild blast tend to stay away from the edge of the Open field.

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    <p>(A) Schematic illustration of the open field protocol. A single session of mild blast (1-min in duration) was introduced to the mice 1 h or 24 h before the open field test. Representative trajectories of a control and blasted mouse on the open field are shown in the middle panels. A total of 5 min was used for the measurement. (B) Time percentage in perphery is different between the groups of control and blast. (C) No difference was observed between blast and control mice in traveled distance. (n = 15 mice each group, student <i>t</i>-test, *<i>p</i><0.05, **<i>p</i><0.01).</p

    Classification and dynamic decoding of ACC real-time ensemble representations of blast events.

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    <p>(A) Firing patterns during rest (dots, black ellipsoid), blast (squares, red ellipsoid), tone (plus, blue ellipsoid), and air-blast (circle, green ellipsoid) epochs are shown after being projected to a three-dimensional space obtained by using MDA. MDA1–3 denote the discriminant axes. Both training and test (grey symbols) data are shown. The trajectories in this three-dimensional space indicate dynamical monitoring of ensemble activity from 1sec before to 2 sec after the actual startling events respectively. The black arrows indicate the direction of the trajectories. Note the trajectories start in the rest cluster 1 sec before the startling events occurred, and move toward the respective event cluster, then returned to the rest. (B) The post-blast reverberation trajectories are shown in red, and directionality indicated by arrows. (C) 30 sec of post-blast ensemble pattern of simultaneously recorded ACC units is shown. Units are grouped by the different types of responses to mild blast stimuli (transient on in orange, transient off in green, prolonged on in red and off-type in blue). Time zero indicates the time point when the stimuli were presented. Red triangles below the x-axis indicate the time point at which the post-blast reverberations took place.</p

    Diverse responses of ACC units to mild blast.

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    <p>(A) Spike raster of simultaneously recorded ACC units in response to blast stimulation is shown. For illustration, only 16 units were listed here. (B) ACC units display a variety type of responses to mild blast, such as transient on (orange), transient off (green), prolonged on (red) and off type (blue). Peri-event rasters and histograms of four representative units are shown. Each short vertical tick indicates a single spike. And spike activities are aligned to the time when mild blasts were delivered. (C) Histograms of averaged responses ratio of four unit types. (D) Pie chart illustrates the portions of different types of responses in the recorded ACC units from six mice. Units with different response types were demonstrated in according colors in (A). Time zero indicates the time point when the stimuli were presented.</p

    General-to-specific arrangement of ACC neuronal responsiveness.

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    <p>(<b>A</b>) ACC units display the transient on and prolonged responses to Air-blow. (B) ACC units display the prolonged responses to Tone. (C)Hierarchical clustering analysis of simultaneously recorded neurons from the ACC of five mice reveals the general-to-specific response selectivity to emotionally charged stimuli, ranging from general (responsive to all types of events, 15 units), to sub-general (responses to a subset of two types of events, 104 units), highly specific responsive units (responsive to one type of events, 184 units). Approximately 55.6% of cell did not respond to blast, air blow, or acoustic startle (bottom of the figure, in blue, 379 nonresponsive units). The color scale bar indicates the normalized response magnitude.</p

    Mild blasts immediately after learning impaired retention of novel object recognition memories.

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    <p>(A) Schematic illustration for the experimental regimen. (B) Representative exploration trajectories of two mice duration retention test (control mouse is in the left panel, blast mouse is on the right panel). The blast mice (in red) explored equally the novel object (circle on the left side) and familiar object (right circle). (C) The percentages of time in exploring novel object showed significant group difference between control and blast group in both 1 hr and 24 hr retention tests. (D) Preference scores also showed that blast group had reduced performances. (E) Numbers of contacts for novel vs. familiar objects. (F) The time on contacting novel or familiar object. The group data shows the blast mice did not exhibit any preference for the novel object whereas the control group formed significant novel object recognition memory at both 1-hr short-term memory test and 24-hr long-term memory test. (<i>n</i> = 15 for each group, repeated measures ANOVA with Bonferroni’s post hoc test, ***<i>p</i><0.001, **<i>p</i><0.01.).</p

    Mice exposed to chronic mild blast were impaired at novel object recognition test.

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    <p>(A) Schematic illustration of experiment regimen. A group of 15 mice were exposed to chronic mild blast each day for ten days. The control group (15 mice) were housed at home cages without exposure to mild blast. (B) Representative exploration trajectories of a control mouse (Left) and blast mouse (right). The control mouse spent more time in exploring the new object during the retention test in comparison to the blasted mice. (C) The percentages of time in exploring novel object showed significant group difference between control and blast group in 1 hr retention test. (D) Preference scores also showed that blast group had reduced performances. (E) Numbers of contacts for novel vs. familiar objects. (F) The time on contacting novel or familiar object. Blasted mice exhibited statistical difference from control group in the novel object recognition test; their performance is close to random level. (<i>n</i> = 15 for each group, repeated measures ANOVA with Bonferroni’s post hoc test, ***<i>p</i><0.001, **<i>p</i><0.01.).</p

    Laboratory-version of mild blast.

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    <p>(A) Schematic drawing shows that mild blast contains the bomb explosion sound and air blast. (B) Loud explosion acoustics (100 <i>dB,</i> 1<i>s</i>) coupled with mild directional air blast (2 <i>psi,</i> 0.5<b> </b><i>ms</i>). (C) A single session of mild blast is consists of 60 combined explosion sound and air-blow, with a total of 1-minute in time duration.</p
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