This work is funded by NIH grant 1R03AI090465.

1. Getting Data
<ol>
 <li>Trajectory data can be collected with handheld GPS units, GPS-enabled smart phone tracking applications, as well as A-GPS (assisted GPS) devices such as the one employed in our study, a commercial child tracker device.</li>
 <li>Trajectory data is usually saved in terms of time-latitude-longitude records. A desired time interval should be set based on application needs. Often the most frequent interval is desired for space-time activity studies.</li>
 <li>Convert the data to comma-separated values, or .csv files with separate columns for record id, latitude, longitude, and time, respectively. Then convert the .csv files into commonly-used Geographic Information Systems (GIS) file format (i.e. ESRI shapefile8).</li>
 <li>Load in a shapefile of building polygons and another of the boundary of the study area with the trajectory analyzer. Set the "extrusion" of the buildings properly for a 3D display and set the "extrusion" and "transparency" of the boundary layer properly to display a space-time cube6, 9 with the x,y dimensions representing space and the z dimension representing time.</li>
</ol>
2. Pre-processing
<ol>
 <li>Two options are available for pre-processing the noisy raw trajectory data. One may choose from the drop down list of the pre-processing menu.</li>
 <li>If 'Interactive' is chosen, a 2D projection of the 3D trajectory is created for easy viewing and selection. Manipulate the 3D display to examine the raw trajectory in space and time. Identify errors in the data based on the shape, speed and/or topology of track segments. Usually track points (vertices) with unrealistic high speed or abrupt direction change signify errors. Select and remove them from the original trajectories. Select and remove them from either the 3D trajectory or its 2D projection.</li>
 <li>A cluster of track points with spiky shapes (Figure 1) spatially and a long duration temporally signify errors that are most possibly caused by indoor locations where GPS signal is weak. If a group of these points is selected, the program can calculate the spatiotemporal centroid of the selected points and adjust the track to go through the centroid.</li>
 <li>Alternatively, if 'Automatic' is chosen from the pre-processing menu, set the input and output locations as well as empirical parameters that determine the abnormal high speed and abrupt turning of points. The program searches through the loaded trajectory data and runs automatically based on an algorithm that mimics the visual error detection approach.</li>
</ol>
3. Trajectory Segmentation &amp; Activity Space Characterization
<ol>
 <li>Trajectory segmentation requires the building layer, so ensure the building shape file is loaded.</li>
 <li>Click the segmentation tool in the toolbar to start the function. Set the input and output and located the building shape file as the reference layer. Use the building names to label the segmented trajectory. The algorithm identifies indoor segments based on set or default criteria such as speed, duration, etc. of track points, as well as the spatial topology with relation to buildings.</li>
 <li>Click the activity space summarization tool to load in segmented trajectories and calculate selected summary attributes to characterize one's activity space, such as total activity radius, radius at a certain time period, ratio of total time spent indoors vs. outdoors, and so on. </li>
 <li>The attributes can be exported to a spreadsheet for quantitative modeling uses.</li>
</ol>
4. Density Surface Mapping
<ol>
 <li>Density surface shows the density of activities in space with the temporal dimension collapsed. Three options are available from the drop-down list of the density surface mapping menu.</li>
 <li>If the 'Track point density' option is selected, fill in the dialog box with input and output information and choose to display in either 3D or 2D. All vertices from the trajectory data are used to calculate kernel densities of the points. Figure 2 shows a density surface.</li>
 <li>If 'Track path density' is selected, the algorithm calculates and displays density of individual paths traveled (Figure 3).</li>
 <li>If the 'Re-sampled point density' option is selected, the algorithm re-samples the trajectory data using a set time interval and maps the densities of points spread evenly in time. This option is designed for tracking devices that collect tracking points in irregular time intervals due to varying sensitivity of the devices under various physical conditions or segmented trajectories. Figure 4 shows the 2D and 3D density surfaces of segmented trajectories.</li>
 <li>If 'Temporal focusing' is selected for any of the above options, temporal focusing10 can be performed to examine activity patterns at different time periods. For example, activity density surfaces at different times in a day may be visualized for easy identification of hot spots across time (Figure 5).</li>
</ol>
5. Density Volume Estimation and Volume Rendering
<ol>
 <li>Density volume visualization uses the notion of a space-time cube as in the visualization of trajectories. The core of such visualization is the disaggregation of space into voxels11. Our approach to visualizing density volume first estimates density volume in individual voxels by counting the number of space-time tracks that intersect with the voxels. One may click 'Density volume calculation' under the density volume visualization menu for this step.</li>
 <li>The same three options are available for density volume visualization as for density surface visualization. </li>
 <li>Next click 'Volume rendering' to launch the 3D volume visualization interface for interactive volume rendering12. By setting the number of divisions along each axis, one may examine clusters at different scales. A z-factor is used to set the vertical exaggeration for better visualization. A reference layer such as the buildings can be loaded to aid visualization as well. The results of volume rendering can be interactively adjusted by manipulating the transfer function that controls the mapping from density to color. (Figure 6).</li>
</ol>
6. Other Exploratory Data Analyses (EDA) and Visualizations
<ol>
 <li>A procedure is available to create animated series to be displayed in Google Earth. Under 'Other', click 'Export to KML for EDA' to access this procedure. It creates a kml13 file that opens in Google Earth for interactive animation of the trajectory.</li>
 <li>One may follow the trajectory to travel the environment in time by scrolling along the timeline in Google Earth.</li>
 <li>A procedure is available to visualize connections among places of interest through 'Connection analysis'. For example, connections among different buildings on a University campus are derived from segmented trajectory data that were collected by students (Figure 7).</li>
 <li>Based on the derived connections, hotspots such as those buildings with the most outbound or inbound traffic and hubs that connect the most trafficked places may be identified.</li>
</ol>

<ol>
	<li>Stoddard, S. T., Morrison, A. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+human+movement+in+the+transmission+of+vector-borne+pathogens.">The role of human movement in the transmission of vector-borne pathogens.</a> PLoS Negl. Trop. Dis. 3 (7), e10 (2009).</li><li>Morens, D. M., Folkers, G. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+challenge+of+emerging+and+re-emerging+infectious+diseases.">The challenge of emerging and re-emerging infectious diseases.</a> Nature. 430, 242-249 (2004).</li><li>Viboud, C., Bjornstad, O. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synchrony,+waves,+and+spatial+hierarchies+in+the+spread+of+influenza.">Synchrony, waves, and spatial hierarchies in the spread of influenza.</a> Science. 312, 447-451 (2006).</li><li>Shoval, N., Isaacson, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Application+of+tracking+technologies+to+the+study+of+pedestrian+spatial+behaviour.">The Application of tracking technologies to the study of pedestrian spatial behaviour.</a> The Professional Geographer. 58 (2), 172-183 (2006).</li><li>Yu, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatio-temporal+GIS+design+for+exploring+interactions+of+human+activities.">Spatio-temporal GIS design for exploring interactions of human activities.</a> Cartography and Geographic Information Science. 33 (1), 3-19 (2006).</li><li>Kwan, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interactive+geovisualization+of+activity-travel+patterns+using+three-dimensional+geographical+information+systems:+a+methodological+exploration+with+a+large+data+set.">Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set.</a> Transportation Research Part C. 8, 185-203 (2000).</li><li>Dem&#353;ar, U., Virrantaus, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Space-time+density+of+trajectories:+exploring+spatio-temporal+patterns+in+movement+data.">Space-time density of trajectories: exploring spatio-temporal patterns in movement data.</a> International Journal of Geographical Information Science. 24 (10), 1527-1542 (2010).</li><li>Kraak, M., Koussoulakous, A., Fisher, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> A visualization environment for the space-time cube. , 189-200 (2004).</li><li>MacEachren, A. M., Polsky, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visualizing+spatial+relationships+among+health,+environmental,+and+demographic+statistics:+interface+design+issues.">Visualizing spatial relationships among health, environmental, and demographic statistics: interface design issues.</a> , 880-887 (1997).</li><li>Levory, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Display+of+surfaces+from+volume+data.">Display of surfaces from volume data.</a> IEEE Computer Graphics and Application. 8 (5), 29-37 (1998).</li><li>Drebin, R. A., Carpenter, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Volume+Rendering.">Volume Rendering.</a> Computer Graphics. , (1998).</li><li>Lee, W., Krumm, J., Zheng, Y., Zhou, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Trajectory+preprocessing.">Trajectory preprocessing.</a> Computing with Spatial Trajectories. , 3-34 (2011).</li><li>Han, B., Comaniciu, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sequential+kernel+density+approximation+and+its+application+to+real-time+visual+tracking.">Sequential kernel density approximation and its application to real-time visual tracking.</a> IEEE Transactions on Pattern Analysis and Machine Intelligence. , (2007).</li></ol>

No conflicts of interest declared.

We used add-in mechanism of ArcGIS to develop the interface. All the interactive operations were implemented using C++. All the automatic processing and analysis functions were developed using Python.
AGPS data, or GPS data collected by pedestrian presents unique challenge in preprocessing as the errors can be massive due to adjacency to buildings and frequent indoor stops. Moreover, the focus of preprocessing should not be data reduction as what is usually done for vehicle GPS trajectory data due to the already obvious scarcity of tracking points. The obvious error patterns in pedestrian trajectory data, however, provide unique solution to preprocessing. Instead of using standard preprocessing algorithms14, we developed the heuristic method (2.3) that mimics a manual visual error detection approach mentioned in 2.2) and cleans up errors in the trajectory data. Specifically, it calculates attributes (speed and direction change) for each track point in a trajectory first. Track points with unrealistic high speeds and/or direction changes are removed. It then re-calculates attributes (duration and direction change) for each remaining track point and detects clusters of track points with spiky shapes (a series of track points with abrupt direction changes). Finally the spatiotemporal centroid of each cluster is calculated and the trajectory is reduced and adjusted to go through the centroid.
The automatic pre-processing and trajectory segmentation algorithms have been evaluated using traditional activity diary data. Ten students were recruited to each carry an AGPS to collect the trajectory data and at the same time were asked to actively record their stops and movements. Portion of a typical activity diary is illustrated in Figure 8. A three-day experiment generated 30 trajectories. Pre-processed and segmented trajectories were compared to the diary data. The results indicated that 1) the processed trajectories captured majority of indoor activities; 2) time recorded in the trajectory data is more accurate as diary takers often write down a rough estimate of time; 3) the trajectory data captured all details of walkway paths while only straight line connections could be obtained from diary data; and 4) some activities are missing from the diary data as participants often skip records due to the burden. But one limitation of our approach is that the segmented trajectory data sometimes mislabel an indoor segment in a wrong building, especially when two buildings are connected to each other, which is the case with some buildings in our experiment. Improvement on this aspect of the algorithm is needed.
Density surface mapping is an effective tool to explore activity patterns, especially when large amount of trajectory data is involved. Figure 4 shows that a large number of trajectories leads to apparent visual clutter if displayed in its original form while density mapping reveals interesting patterns. A simple application of this was conducted using the data set collected on Kean university campus during the 2010 flu season. Students who caught the flu and students who didn't generated two sets of trajectories. Students were also interviewed regarding the severity of their symptoms. Figure 9 illustrates the activity density patterns of two groups of students, one showing only mild symptoms (Figure 9A) and another showing more notable ones (Figure 9B). It is noted that the real sick students' activity space tends to cluster around a particular building. Further investigation could thus be conducted to determine the causes of such clustering. This experiment indicates that the method has the potential to reveal hidden patterns in trajectory data.
The above density surface maps, however, collapses the temporal dimension. Density volume visualization uses the notion of a space-time cube and represents both spatial and temporal dimensions. Figure 6 indicates that such visualization is effective in dealing with visual cluttering problems. Once made interactive, it allows one to manipulate the rendering to highlight different frequency ranges in the data to detect patterns. One limitation of our current approach, however, is that the rendered volume does not appear to be completely smooth. We are in the process of improving the density estimation algorithm to deal with the issue. One consideration is that Kernel density estimation may improve the visual effect, but the computation time would become much longer. Sequential kernel density estimation15 could be another option that we would investigate.
In addition to detecting activity clusters (hot spots) in time and space, the method we introduced in 7.2 detects another kind of hot spots that is related to connections among places. Again with our data set collected by students during a flu season, connection analysis was conducted to identify strong connections among campus buildings based on all trajectory data (Figure 7) as well as those representing only students who demonstrated notable flu symptoms (Figure 10). Comparing the two figures, we see that one connection (between a building called the University Center and another called the CAS building) appears to be a strong connection in general (Figure 7) but is missing from the set of strong connections identified for sick students (Figure 10). The two strong connections that remain in the latter are one between the University Center and the Science Building and another among Science Building, Hennings Hall and Hutchinson Hall. Knowledge regarding these campus buildings indicates that the University Center is the most heavily trafficked stop on campus with a cafeteria and recreation rooms inside. It could be a potential high risk hub during flu season when students interact with each for a long period of time in a crowded space. It is also learned that the three buildings involved in the second connection are all attached to each other with indoor pathways. These buildings have classrooms where students may spend many hours indoors taking classes without having to go outside of a building. These buildings are also relatively old constructions with aged ventilation systems that could increase risks of respiratory disease transmission. The CAS building that appears in the connection in Figure 7 but not in Figure 10, on the other hand, is a brand new building and stands by itself in large open space. Good ventilation and the fact that student activity has to involve outdoor time period when taking classes elsewhere both could lead to lower risks. These, are of course speculations but proves that such analysis, like other methods presented in this paper can be a useful exploratory analysis tool to reveal hidden patterns. This package, however, by no means includes all possible methods useful for trajectory data analyses. We are keeping our efforts going to develop and incorporate more analytical as well as visual functions into our system.

<table border="1">
 <tbody>
 <tr>
 <td>Name of the reagent</td>
 <td>Company</td>
 <td>Catalogue number</td>
 <td>Comments (optional)</td>
 </tr>
 <tr>
 <td>WorldTracker GPRS</td>
 <td>Tracking The World </td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>A personal computer for running the analysis</td>
 <td></td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>ArcGIS software</td>
 <td>ESRI</td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>Trajectory Analyzer Extension</td>
 <td></td>
 <td></td>
 <td></td>
 </tr>
 </tbody>
</table>

trajectory data analyses for pedestrian space-time activity study

It is well recognized that human movement in the spatial and temporal dimensions has direct influence on disease transmission1-3. An infectious disease typically spreads via contact between infected and susceptible individuals in their overlapped activity spaces. Therefore, daily mobility-activity information can be used as an indicator to measure exposures to risk factors of infection. However, a major difficulty and thus the reason for paucity of studies of infectious disease transmission at the micro scale arise from the lack of detailed individual mobility data. Previously in transportation and tourism research detailed space-time activity data often relied on the time-space diary technique, which requires subjects to actively record their activities in time and space. This is highly demanding for the participants and collaboration from the participants greatly affects the quality of data4.
Modern technologies such as GPS and mobile communications have made possible the automatic collection of trajectory data. The data collected, however, is not ideal for modeling human space-time activities, limited by the accuracies of existing devices. There is also no readily available tool for efficient processing of the data for human behavior study. We present here a suite of methods and an integrated ArcGIS desktop-based visual interface for the pre-processing and spatiotemporal analyses of trajectory data. We provide examples of how such processing may be used to model human space-time activities, especially with error-rich pedestrian trajectory data, that could be useful in public health studies such as infectious disease transmission modeling.
The procedure presented includes pre-processing, trajectory segmentation, activity space characterization, density estimation and visualization, and a few other exploratory analysis methods. Pre-processing is the cleaning of noisy raw trajectory data. We introduce an interactive visual pre-processing interface as well as an automatic module. Trajectory segmentation5 involves the identification of indoor and outdoor parts from pre-processed space-time tracks. Again, both interactive visual segmentation and automatic segmentation are supported. Segmented space-time tracks are then analyzed to derive characteristics of one's activity space such as activity radius etc. Density estimation and visualization are used to examine large amount of trajectory data to model hot spots and interactions. We demonstrate both density surface mapping6 and density volume rendering7. We also include a couple of other exploratory data analyses (EDA) and visualizations tools, such as Google Earth animation support and connection analysis. The suite of analytical as well as visual methods presented in this paper may be applied to any trajectory data for space-time activity studies.

Trajectory data was collected by volunteering undergraduate students from Kean University (NJ, USA) in spring 2010. The purpose was to study activity patterns of students who caught influenza (diagnosed by doctor or self-diagnosed) in comparison to those who did not. In order to illustrate the methods and procedure presented in this paper we took the trajectories collected within the suburban campus area to generate representative results. Trajectories within the campus area are mostly pedestrian trajectories, with only a small portion resulting from driving between the various parking lots and outside of the campus.
The space-time cube representation of trajectories with reference to buildings on the university campus is shown in Figure 1. Figure 1A is the raw data collected by a student recording one day of his activity on campus using an AGPS device (a commercial child tracker). It is obvious that some long duration of indoor stays has resulted in noisy data (indicated by the spiky portion of the track). This is very common in pedestrian trajectory data. Figure 1B shows the pre-processed and segmented trajectory. Figure 1C shows the pre-processed and segmented trajectory with color-coded indoor and outdoor segments in the space-time cube.
Figures 2 to 4 illustrate density surface mapping of a set of trajectories. Figure 2 shows the raw tracking points involved to perform a 'Track point density' mapping option (Figure 2A) and the resulting density map (Figure 2B). Instead of mapping densities of tracking points, Figure 3 maps the densities of traveled paths. Density mapping is particular useful when analyzing large amount of trajectories. Figure 4A displays a total of 470 trajectories. Figures 4B and 4C show the density surface in a 2D (left) and 3D representations (right) using re-sampled points from these trajectories.
In addition to the interactive display of the temporal dimension in a space-time cube, the time variable can be processed through temporal focusing to examine spatial patterns at different time periods. Figure 5 shows examples of such analysis using the sample data set that contains trajectory data collected by students during the flu season. It is obvious that their activities are centered around different locations throughout the day to lead eventually to the composite activity density map on the bottom.
An example of density volume rendering is illustrated in Figure 6. Figure 6A shows that it is hard to detect patterns if all the space-time tracks are visualized in space-time cube because of visual clutters. Figure 6B shows the corresponding density volume rendering results. The four illustrations represent different settings of the transfer function of our density rendering program, thus highlight density volumes at different frequency ranges.
Another way of finding hotspots is through connection analysis. Figure 7 illustrates the result of such analysis with our sample data set. Figure 7A shows the straight line connections among all buildings on campus. The highlighted buildings are those with the highest outbound traffic volume. Figure 7B shows the same connections, with the most trafficked connections highlighted.
<img src="/files/ftp_upload/50130/50130fig1.jpg" alt="Figure 1" fo:content-width="3in" fo:src="/files/ftp_upload/50130/50130fig1highres.jpg"/> 
 Figure 1. Pre-processing and segmentation of trajectory data. A: 2D view of a raw trajectory on the background of campus buildings; B: pre-processed trajectory; C: space-time cube representation of segmented trajectory. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50130/50130fig1large.jpg" target="_blank"> Click here to view larger figure</a>.
<img src="/files/ftp_upload/50130/50130fig2.jpg" alt="Figure 2" /> 
 Figure 2. Density surface mapping. A: raw track points of a trajectory data set; B: density surface derived from track points.
<img src="/files/ftp_upload/50130/50130fig3.jpg" alt="Figure 3"/> 
 Figure 3. Density surface of trajectory paths.
<img src="/files/ftp_upload/50130/50130fig4.jpg" alt="Figure 4" fo:content-width="4in" fo:src="/files/ftp_upload/50130/50130fig4highres.jpg"/> 
 Figure 4. Colored density surface mapping. A: a total of 470 trajectories; B: colored density surface in 2D; C: colored density surface in 3D. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50130/50130fig4large.jpg" target="_blank"> Click here to view larger figure</a>.
<img src="/files/ftp_upload/50130/50130fig5.jpg" alt="Figure 5"/> 
 Figure 5. Temporal focusing for density mapping: student activity densities on campus at different time periods.
<img src="/files/ftp_upload/50130/50130fig6.jpg" alt="Figure 6" fo:content-width="4.5in" fo:src="/files/ftp_upload/50130/50130fig6highres.jpg"/> 
 Figure 6. Density volume rendering and visualization. A: visual clutters resulting from raw trajectories; B: spatiotemporal clusters highlighted by visualizing density volumes at different frequency ranges. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50130/50130fig6large.jpg" target="_blank"> Click here to view larger figure</a>.
<img src="/files/ftp_upload/50130/50130fig7.jpg" alt="Figure 7" fo:content-width="4in" fo:src="/files/ftp_upload/50130/50130fig7highres.jpg"/> 
 Figure 7. Connection analysis results. A: straight line connections among all buildings on a university campus derived from trajectory data, with the most trafficked buildings highlighted; B: the most trafficked connections among buildings on campus. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50130/50130fig7large.jpg" target="_blank"> Click here to view larger figure</a>.
<img src="/files/ftp_upload/50130/50130fig8.jpg" alt="Figure 8" fo:content-width="4in" fo:src="/files/ftp_upload/50130/50130fig8highres.jpg"/> 
 Figure 8. Portion of an activity diary recorded by a student.
<img src="/files/ftp_upload/50130/50130fig9.jpg" alt="Figure 9"/> 
 Figure 9. Activity density patterns of two groups of students. A: activity density patterns of students with mild flu symptoms during a flu season; B: activity density patterns of students with more notable flu symptoms.
<img src="/files/ftp_upload/50130/50130fig10.jpg" alt="Figure 10" fo:content-width="4in" fo:src="/files/ftp_upload/50130/50130fig10highres.jpg"/> 
 Figure 10. Connection analysis results based on trajectory data of students who had notable flu symptoms during a flu season. A: straight line connections among buildings with the most trafficked buildings highlighted; B: the most trafficked connections taken by students with flu. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50130/50130fig10large.jpg" target="_blank"> Click here to view larger figure</a>.

Watch this Scientific Journal Video about Trajectory Data Analyses for Pedestrian Space-time Activity Study at JoVE.com

Trajectory Data Analyses for Pedestrian Space-time Activity Study

A suite of spatiotemporal processing methods are presented to analyze human trajectory data, such as that collected using a GPS device, for the purpose of modeling pedestrian space-time activities.

It is well recognized that human movement in the  spatial and temporal dimensions has direct influence on disease transmission1-3.  An infectious disease ...

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13.4K Views.  Kean University. A suite of spatiotemporal processing methods are presented to analyze human trajectory data, such as that collected using a GPS device, for the purpose of modeling pedestrian space-time activities.

Video: Trajectory Data Analyses for Pedestrian Space-time Activity Study

Testing Visual Sensitivity to the Speed and Direction of Motion in Lizards

Testing visual sensitivity in any species provides basic information regarding behaviour, evolution, and ecology. However, testing specific features of the visual system provide more empirical evidence for functional applications. Investigation into the sensory system provides information about the sensory capacity, learning and memory ability, and establishes known baseline behaviour in which to gauge deviations (Burghardt, 1977). However, unlike mammalian or avian systems, testing for learning and memory in a reptile species is difficult. Furthermore, using an operant paradigm as a psychophysical measure of sensory ability is likewise as difficult. Historically, reptilian species have responded poorly to conditioning trials because of issues related to motivation, physiology, metabolism, and basic biological characteristics. Here, I demonstrate an operant paradigm used a novel model lizard species, the Jacky dragon (Amphibolurus muricatus) and describe how to test peripheral sensitivity to salient speed and motion characteristics. This method uses an innovative approach to assessing learning and sensory capacity in lizards. I employ the use of random-dot kinematograms (RDKs) to measure sensitivity to speed, and manipulate the level of signal strength by changing the proportion of dots moving in a coherent direction. RDKs do not represent a biologically meaningful stimulus, engages the visual system, and is a classic psychophysical tool used to measure sensitivity in humans and other animals. Here, RDKs are displayed to lizards using three video playback systems. Lizards are to select the direction (left or right) in which they perceive dots to be moving. Selection of the appropriate direction is reinforced by biologically important prey stimuli, simulated by computer-animated invertebrates.

Testing visual sensitivity in lizards using an operant conditioning paradigm that employs video playback of random-dot kinematograms and computer-generated invertebrates.

Testing visual sensitivity in any species provides basic information regarding behaviour, evolution, and ecology. However, testing specific features of ...

Computer-Generated Animal Model Stimuli

Communication between animals is diverse and complex. Animals may communicate using auditory, seismic, chemosensory, electrical, or visual signals. In particular, understanding the constraints on visual signal design for communication has been of great interest. Traditional methods for investigating animal interactions have used basic observational techniques, staged encounters, or physical manipulation of morphology. Less intrusive methods have tried to simulate conspecifics using crude playback tools, such as mirrors, still images, or models. As technology has become more advanced, video playback has emerged as another tool in which to examine visual communication (Rosenthal, 2000). However, to move one step further, the application of computer-animation now allows researchers to specifically isolate critical components necessary to elicit social responses from conspecifics, and manipulate these features to control interactions. Here, I provide detail on how to create an animation using the Jacky dragon as a model, but this process may be adaptable for other species. In building the animation, I elected to use Lightwave  3D to alter object morphology, add texture, install bones, and provide comparable weight shading that prevents exaggerated movement. The animation is then matched to select motor patterns to replicate critical movement features. Finally, the sequence must rendered into an individual clip for presentation. Although there are other adaptable techniques, this particular method had been demonstrated to be effective in eliciting both conspicuous and social responses in staged interactions.

Computer-generated stimuli using the Jacky dragon as a model.

Communication between animals is diverse and complex. Animals may communicate using auditory, seismic, chemosensory, electrical, or visual signals. In ...

Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees

The projected number of American amputees is expected to rise to 3.6 million by 2050.  Many of these individuals depend on artificial limbs to perform routine activities, but prosthetic suspensions using traditional socket technology can prove to be cumbersome and uncomfortable for a person with limb loss.  Moreover, for those with high proximal amputations, limited residual limb length may prevent exoprosthesis attachment all together. Osseointegrated implant technology is a novel operative procedure which allows firm skeletal attachment between the host bone and an implant. Preliminary results in European amputees with osseointegrated implants have shown improved clinical outcomes by allowing direct transfer of loads to the bone-implant interface.  Despite the apparent advantages of osseointegration over socket technology, the current rehabilitation procedures require long periods of restrictive load bearing prior which may be reduced with expedited skeletal attachment via electrical stimulation.  The goal of the osseointegrated intelligent implant design (OIID) system is to make the implant part of an electrical system to accelerate skeletal attachment and help prevent periprosthetic infection. To determine optimal electrode size and placement, we initiated proof of concept with computational modeling of the electric fields and current densities that arise during electrical stimulation of amputee residual limbs.  In order to provide insure patient safety, subjects with retrospective computed tomography scans were selected and three dimensional reconstructions were created using customized software programs to ensure anatomical accuracy (Seg3D and SCIRun) in an IRB and HIPAA approved study.  These software packages supported the development of patient specific models and allowed for interactive manipulation of electrode position and size. Preliminary results indicate that electric fields and current densities can be generated at the implant interface to achieve the homogenous electric field distributions   required to induce osteoblast migration, enhance skeletal fixation and may help prevent periprosthetic infections.  Based on the electrode configurations experimented with in the model, an external two band configuration will be advocated in the future.

There is a need to develop alternative prosthesis attachment due to limb loss attributed to vascular occlusive diseases and trauma. The goal of the work is to introduce an osseointegrated intelligent implant design system to increase skeletal fixation and reduce periprosthetic infection rates for patients needing osseointegrated technology.

The projected number of American amputees is expected to rise to 3.6 million by 2050.  Many of these individuals depend on artificial limbs to perform ...

Isolation and Purification of Drosophila Peripheral Neurons by Magnetic Bead Sorting

The Drosophila peripheral nervous system (PNS) is a powerful model for investigating the complex processes of neuronal development and dendrite morphogenesis at the functional and molecular levels. To aid in these analyses, we have developed a strategy for the isolation of a subclass of PNS neurons called dendritic arborization (da) neurons that have been widely used for studying dendrite morphogenesis1,2. These neurons are very difficult to isolate as a pure population, due in part to their extremely low occurrence and their difficult-to-reach location below the tough chitinous larval cuticle. Our newly developed method overcomes these challenges, and is based on a fast and specific cell enrichment using antibody-coated magnetic beads. For our magnetic bead sorting studies, we have used age-matched third instar larvae expressing a mouse CD8 tagged GFP fusion protein (UAS-mCD8-GFP)3 under the control of either the class IV dendritic arborization (da) neuron-specific pickpocket (ppk)-GAL4 driver4 or the control of the pan-da neuron-specific GAL421-7 driver5. Although this protocol has been optimized for isolating PNS cells which are attached to the inner wall of the larval cuticle, by varying a few parameters, the same protocol could be used to isolate many different cell types attached to the cuticle at larval or pupal stages of development (e.g. epithelia, muscle, oenocytes etc.), or other cell types from larval organs depending upon the GAL4-specific driver expression pattern. The RNA isolated by this method is of high quality and can be readily used for downstream genomic analyses such as microarray gene expression profiling studies. This approach offers a powerful new tool to perform studies on isolated Drosophila dendritic arborization (da) neurons thereby providing novel insights into the molecular mechanisms underlying dendrite morphogenesis.

Isolation and Purification of Drosophila Peripheral Neurons by Magnetic Bead Sorting

In this video-article we present a method for the isolation and purification of Drosophila peripheral neurons using a fast magnetic bead assisted cell sorting strategy. RNA obtained from the isolated cells can be readily used for downstream applications including microarray analyses.

The Drosophila peripheral nervous system (PNS) is a powerful model for investigating the complex processes of neuronal development and dendrite ...

Isolation of Translating Ribosomes Containing Peptidyl-tRNAs for Functional and Structural Analyses

Recently, structural and biochemical studies have detailed many of the molecular events that occur in the ribosome during inhibition of protein synthesis by antibiotics and during nascent polypeptide synthesis. Some of these antibiotics, and regulatory nascent polypeptides mostly in the form of peptidyl-tRNAs, inhibit either peptide bond formation or translation termination1-7. These inhibitory events can stop the movement of the ribosome, a phenomenon termed "translational arrest". Translation arrest induced by either an antibiotic or a nascent polypeptide has been shown to regulate the expression of genes involved in diverse cellular functions such as cell growth, antibiotic resistance, protein translocation and cell metabolism8-13. Knowledge of how antibiotics and regulatory nascent polypeptides alter ribosome function is essential if we are to understand the complete role of the ribosome in translation, in every organism.
Here, we describe a simple methodology that can be used to purify, exclusively, for analysis, those ribosomes translating a specific mRNA and containing a specific peptidyl-tRNA14. This procedure is based on selective isolation of translating ribosomes bound to a biotin-labeled mRNA. These translational complexes are separated from other ribosomes in the same mixture, using streptavidin paramagnetic beads (SMB) and a magnetic field (MF). Biotin-labeled mRNAs are synthesized by run-off transcription assays using as templates PCR-generated DNA fragments that contain T7 transcriptional promoters. T7 RNA polymerase incorporates biotin-16-UMP from biotin-UTP; under our conditions approximately ten biotin-16-UMP molecules are incorporated in a 600 nt mRNA with a 25% UMP content. These biotin-labeled mRNAs are then isolated, and used in in vitro translation assays performed with release factor 2 (RF2)-depleted cell-free extracts obtained from Escherichia coli strains containing wild type or mutant ribosomes. Ribosomes translating the biotin-labeled mRNA sequences are stalled at the stop codon region, due to the absence of the RF2 protein, which normally accomplishes translation termination. Stalled ribosomes containing the newly synthesized peptidyl-tRNA are isolated and removed from the translation reactions using SMB and an MF. These beads only bind biotin-containing messages.
The isolated, translational complexes, can be used to analyze the structural and functional features of wild type or mutant ribosomal components, or peptidyl-tRNA sequences, as well as determining ribosome interaction with antibiotics or other molecular factors 1,14-16. To examine the function of these isolated ribosome complexes, peptidyl-transferase assays can be performed in the presence of the antibiotic puromycin1. To study structural changes in translational complexes, well established procedures can be used, such as i) crosslinking to specific amino acids14 and/or ii) alkylation protection assays1,14,17.

A major impediment to biochemical analyses of ribosomes containing nascent peptidyl-tRNAs has been the presence of other ribosomes in the same samples, ribosomes not involved in the translation of the specific mRNA sequence being analyzed. We developed a simple methodology to purify, exclusively, the ribosomes containing the nascent peptidyl-tRNA of interest.

Recently, structural and biochemical studies have detailed many of the molecular events that occur in the ribosome during inhibition of protein synthesis ...

DNA Extraction from Paraffin Embedded Material for Genetic and Epigenetic Analyses

Disease development and progression are characterized by frequent genetic and epigenetic aberrations including chromosomal rearrangements, copy number gains and losses and DNA methylation. Advances in high-throughput, genome-wide profiling technologies, such as microarrays, have significantly improved our ability to identify and detect these specific alterations. However as technology continues to improve, a limiting factor remains sample quality and availability. Furthermore, follow-up clinical information and disease outcome are often collected years after the initial specimen collection. Specimens, typically formalin-fixed and paraffin embedded (FFPE), are stored in hospital archives for years to decades. DNA can be efficiently and effectively recovered from paraffin-embedded specimens if the appropriate method of extraction is applied. High quality DNA extracted from properly preserved and stored specimens can support quantitative assays for comparisons of normal and diseased tissues and generation of genetic and epigenetic signatures 1. To extract DNA from paraffin-embedded samples, tissue cores or microdissected tissue are subjected to xylene treatment, which dissolves the paraffin from the tissue, and then rehydrated using a series of ethanol washes. Proteins and harmful enzymes such as nucleases are subsequently digested by proteinase K. The addition of lysis buffer, which contains denaturing agents such as sodium dodecyl sulfate (SDS), facilitates digestion 2. Nucleic acids are purified from the tissue lysate using buffer-saturated phenol and high speed centrifugation which generates a biphasic solution. DNA and RNA remain in the upper aqueous phase, while proteins, lipids and polysaccharides are sequestered in the inter- and organic-phases respectively. Retention of the aqueous phase and repeated phenol extractions generates a clean sample. Following phenol extractions, RNase A is added to eliminate contaminating RNA. Additional phenol extractions following incubation with RNase A are used to remove any remaining enzyme. The addition of sodium acetate and isopropanol precipitates DNA, and high speed centrifugation is used to pellet the DNA and facilitate isopropanol removal. Excess salts carried over from precipitation can interfere with subsequent enzymatic assays, but can be removed from the DNA by washing with 70% ethanol, followed by centrifugation to re-pellet the DNA 3. DNA is re-suspended in distilled water or the buffer of choice, quantified and stored at -20&deg;C. Purified DNA can subsequently be used in downstream applications which include, but are not limited to, PCR, array comparative genomic hybridization 4 (array CGH), methylated DNA Immunoprecipitation (MeDIP) and sequencing, allowing for an integrative analysis of tissue/tumor samples.

This video demonstrates the protocol for DNA extraction from formalin-fixed paraffin-embedded material. This is a multi-day procedure in which tissue sections are deparaffinized with xylene, rehydrated with ethanol and treated with proteinase K to purify and isolate DNA for subsequent gene-specific or genome-wide analysis.

Disease development and progression are characterized by frequent genetic and epigenetic aberrations including chromosomal rearrangements, copy number ...

Evisceration of Mouse Vitreous and Retina for Proteomic Analyses

While the mouse retina has emerged as an important genetic model for inherited retinal disease, the mouse vitreous remains to be explored. The vitreous is a highly aqueous extracellular matrix overlying the retina where intraocular as well as extraocular proteins accumulate during disease.1-3 Abnormal interactions between vitreous and retina underlie several diseases such as retinal detachment, proliferative diabetic retinopathy, uveitis, and proliferative vitreoretinopathy.1,4 The relative mouse vitreous volume is significantly smaller than the human vitreous (Figure 1), since the mouse lens occupies nearly 75% of its eye.5 This has made biochemical studies of mouse vitreous challenging. In this video article, we present a technique to dissect and isolate the mouse vitreous from the retina, which will allow use of transgenic mouse models to more clearly define the role of this extracellular matrix in the development of vitreoretinal diseases.

The dissection technique illustrates evisceration of the vitreous, retina, and lens from the mouse eye, separation by centrifugation, and characterization with protein assays.

While the mouse retina has emerged as an important genetic model for inherited retinal disease, the mouse vitreous remains to be explored. The vitreous is ...

Do-It-Yourself Device for Recovery of Cryopreserved Samples Accidentally Dropped into Cryogenic Storage Tanks

Liquid nitrogen is colorless, odorless, extremely cold (-196 &deg;C) liquid kept under pressure. It is commonly used as a cryogenic fluid for long term storage of biological materials such as blood, cells and tissues 1,2. The cryogenic nature of liquid nitrogen, while ideal for sample preservation, can cause rapid freezing of live tissues on contact - known as 'cryogenic burn'2, which may lead to severe frostbite in persons closely involved in storage and retrieval of samples from Dewars. Additionally, as liquid nitrogen evaporates it reduces the oxygen concentration in the air and might cause asphyxia, especially in confined spaces2.
In laboratories, biological samples are often stored in cryovials or cryoboxes stacked in stainless steel racks within the Dewar tanks1. These storage racks are provided with a long shaft to prevent boxes from slipping out from the racks and into the bottom of Dewars during routine handling. All too often, however, boxes or vials with precious samples slip out and sink to the bottom of liquid nitrogen filled tank. In such cases, samples could be tediously retrieved after transferring the liquid nitrogen into a spare container or discarding it. The boxes and vials can then be relatively safely recovered from emptied Dewar. However, the cryogenic nature of liquid nitrogen and its expansion rate makes sunken sample retrieval hazardous. It is commonly recommended by Safety Offices that sample retrieval be never carried out by a single person. Another alternative is to use commercially available cool grabbers or tongs to pull out the vials3. However, limited visibility within the dark liquid filled Dewars poses a major limitation in their use.
In this article, we describe the construction of a Cryotolerant DIY retrieval device, which makes sample retrieval from Dewar containing cryogenic fluids both safe and easy.

Here we present a low cost, durable cryotolerant device for sample retrieval from Dewar tanks filled with liquid nitrogen. The ease of construction and modular design of the device makes the process of sample retrieval from cryogenic tanks safe and easy.

Liquid nitrogen is colorless, odorless, extremely cold (-196 &deg;C) liquid kept under pressure. It is commonly used as a cryogenic fluid for long term ...

Nanogold Labeling of the Yeast Endosomal System for Ultrastructural Analyses

Endosomes are one of the major membrane sorting checkpoints in eukaryotic cells and they regulate recycling or destruction of proteins mostly from the plasma membrane and the Golgi. As a result the endosomal system plays a central role in maintaining cell homeostasis, and mutations in genes belonging to this network of organelles interconnected by vesicular transport, cause severe pathologies including cancer and neurobiological disorders. It is therefore of prime relevance to understand the mechanisms underlying the biogenesis and organization of the endosomal system. The yeast Saccharomyces cerevisiae has been pivotal in this task. To specifically label and analyze at the ultrastructural level the endosomal system of this model organism, we present here a detailed protocol for the positively charged nanogold uptake by spheroplasts followed by the visualization of these particles through a silver enhancement reaction. This method is also a valuable tool for the morphological examination of mutants with defects in endosomal trafficking. Moreover, it is not only applicable for ultrastructural examinations but it can also be combined with immunogold labelings for protein localization investigations.

Yeast, Saccharomyces cerevisiae, has been a key model organism to identify and study genes regulating the biogenesis and functions of the endosomal system. Here we present a detailed protocol for the specific labeling of the endosomal compartments for ultrastructural studies.

Endosomes are one of the major membrane sorting checkpoints in eukaryotic cells and they regulate recycling or destruction of proteins mostly from the ...

Improved Swiss-rolling Technique for Intestinal Tissue Preparation for Immunohistochemical and Immunofluorescent Analyses

Understanding the role of factors that regulate intestinal epithelial homeostasis and response to injury and regeneration is important. The current literature describes several different methodological approaches to obtain images of intestinal tissues for data validation. In this paper, we delineate a common protocol relating to the derivation and processing of mouse intestinal tissues. Proper fixation of intestinal tissues and Swiss-roll techniques that enhance intestinal epithelial morphology are discussed. Postresection processing and reorientation of embedded intestinal tissues are critical in obtaining paraffin-embedded blocks that display intact intestinal structural features after sectioning. The Swiss-rolling technique helps in histological assessment of the complete intestinal or colonic sections examined. An ability to differentiate intestinal structural features can be vital in quantitative measurements of intestinal inflammation and tumorigenesis along the entire length. Finally, paraffin-embedded sections are ideal for robust processing using both immunohistochemical and immunofluorescent detection methods. Nonfluorescent immunohistochemical sections provide a vibrant image of the tissue detailing different cellular structural features but do not provide flexibility for intracellular co-localization experiments. Multiple fluorescent channels can be appropriately utilized with immunofluorescent detection for co-localization experiments, lending support to mechanistic studies.

Accurate identification and location of epithelial cells along the intestinal mucosal lining are essential to define different cell lineages. Proper imaging of intestinal tissues is crucial for identification of protein expression patterns with maximum resolution. This study aims to delineate the optimal methods and conditions for processing mouse intestinal tissues.

Understanding the role of factors that regulate intestinal epithelial homeostasis and response to injury and regeneration is important. The current ...