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 trajector...

<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, wit...

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 ...

trajectory-data-analyses-for-pedestrian-space-time-activity-study

Research

JoVE Journal

Biology

13.4K ビュー.  Kean University. 時空間処理方法のスイートは、モデリング·歩行者の時空間活動の目的のため、GPSデバイスを使用して収集されたものと人間の軌道データを分析するために提示されています。

ビデオ: 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 ...

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 ...

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 ...

In Vitro SUMOylation Assay to Study SUMO E3 Ligase Activity

Small ubiquitin-like modifier (SUMO) modification is an important post-translational modification (PTM) that mediates signal transduction primarily through modulating protein-protein interactions. Similar to ubiquitin modification, SUMOylation is directed by a sequential enzyme cascade including E1-activating enzyme (SAE1/SAE2), E2-conjugation enzyme (Ubc9), and E3-ligase (i.e., PIAS family, RanBP2, and Pc2). However, different from ubiquitination, an E3 ligase is non-essential for the reaction but does provide precision and efficacy for SUMO conjugation. Proteins modified by SUMOylation can be identified by in vivo assay via immunoprecipitation with substrate-specific antibodies and immunoblotting with SUMO-specific antibodies. However, the demonstration of protein SUMO E3 ligase activity requires in vitro reconstitution of SUMOylation assays using purified enzymes, substrate, and SUMO proteins. Since in the in vitro reactions, usually SAE1/SAE2 and Ubc9, alone are sufficient for SUMO conjugation, enhancement of SUMOylation by a putative E3 ligase is not always easy to detect. Here, we describe a modified in vitro SUMOylation protocol that consistently identifies SUMO modification using an in vitro reconstituted system. A step-by-step protocol to purify catalytically active K-bZIP, a viral SUMO-2/3 E3 ligase, is also presented. The SUMOylation activities of the purified K-bZIP are shown on p53, a well-known target of SUMO. This protocol can not only be employed for elucidating novel SUMO E3 ligases, but also for revealing their SUMO paralog specificity.

In Vitro SUMOylation Assay to Study SUMO E3 Ligase Activity

Unlike ubiquitin ligases, few E3 SUMO ligases have been identified. This modified in vitro SUMOylation protocol is able to identify novel SUMO E3 ligases by an in vitro reconstitution assay.

Small ubiquitin-like modifier (SUMO) modification is an important post-translational modification (PTM) that mediates signal transduction primarily ...

Application of Electrophysiology Measurement to Study the Activity of Electro-Neutral Transporters

The transport of ions through cell membranes ensures the fine control of ion content within and outside the cell that is indispensable for cell survival. These transport mechanisms are mediated by the activities of specialized transporter proteins. Specifically,pH dynamics are finely controlled by plasma membrane proton (H+) extrusion systems, such as the Na+/H+ exchanger (NHE) protein family. Despite extensive efforts to study the mechanisms underlying NHE regulation, our current understanding of the biophysical and molecular properties of the NHE family is inadequate because of the limited availability of methods to effectively measure NHE activity. In this manuscript, we used H+-selective electrodes during whole-cell patch clamping recording to measure NHE-induced H+ flux. We proposed this approach to overcome some limitations of typically used methods to measure NHE activity, such as radioactive uptake and fluorescent membrane permeants. Measurement of NHE activity using the described method enables high sensitivity and time resolution and more efficient control of intracellular H+ concentrations. H+-selective electrodes are based on the fact that transporter activity creates an ion gradient in close proximity to the cell membrane. An H+-selective electrode moving up to and away from the cell membrane in a repetitive, oscillatory fashion records a voltage difference that is dependent on H+ flux. While H+-selective electrodes are used to detect H+ flux moving out of the cell, the patch clamp method in the whole-cell configuration is used to control the intracellular ion composition. Moreover, application of the giant patch clamp technique allows modification of the intracellular composition of not only ions but also lipids. The transporter activity of NHE isoform 3 (NHE3) was measured using this technical approach to study the molecular basis of NHE3 regulation by phosphoinositides.

This manuscript describes the applications of proton-selective electrodes and patch clamping methods to measure the activity of proton transport systems. These methods overcome some limitations of techniques commonly used to study proton transport activity, such as moderate sensitivity, time resolution and insufficient intracellular milieu control.

The transport of ions through cell membranes ensures the fine control of ion content within and outside the cell that is indispensable for cell survival. ...

Tissue Collection of Bats for -Omics Analyses and Primary Cell Culture

As high-throughput sequencing technologies advance, standardized methods for high quality tissue acquisition and preservation allow for the extension of these methods to non-model organisms. A series of protocols to optimize tissue collection from bats has been developed for a series of high-throughput sequencing approaches. Outlined here are protocols for the capture of bats, desired demographics to be collected for each bat, and optimized methods to minimize stress on a bat during tissue collection. Specifically outlined are methods for collecting and treating tissue to obtain (i) DNA for high molecular weight genomic analyses, (ii) RNA for tissue-specific transcriptomes, and (iii) proteins for proteomic-level analyses. Lastly, also outlined is a method to avoid lethal sampling by creating viable primary cell cultures from wing clips. A central motivation of these methods is to maximize the amount of potential molecular and morphological data for each bat and suggest optimal ways to preserve tissues so they retain their value as new methods develop in the future. This standardization has become particularly important as initiatives to sequence chromosome-level, error-free genomes of species across the world have emerged, in which multiple scientific parties are spearheading the sequencing of different taxonomic groups. The protocols outlined herein define the ideal tissue collection and tissue preservation methods for Bat1K, the consortium that is sequencing the genomes of every species of bat.

This is a protocol for the optimal tissue preparation for genomic, transcriptomic, and proteomic analyses of bats caught in the wild. It includes protocols for bat capture and dissection, tissue preservation, and cell culturing of bat tissue.

As high-throughput sequencing technologies advance, standardized methods for high quality tissue acquisition and preservation allow for the extension of ...