This work was supported by USDA in-house funding.

1. Preparation
<ol>
	<li>Gather equipment, materials, and software to record and process the video of plant freezing.
	<ol>
		<li>Start a programmable freezer by setting the power switch to On, and set the temperature to 0 &#176;C. Program the freezer to reach -8 &#176;C at 1 &#176;C/h.</li>
	</ol>
	</li>
	<li>Place one 6-weeks-old strawberry plant with 2 - 5 flowers that was grown in a 1 L container into the freezer.</li>
	<li>Set up 2 IR cameras (e.g., FLIR T620 cameras) using fastening straps and a small block of wood to produce the correct convergence angle of the lenses. 
	Note: The optimum distance to space the center of the lenses of the 2 cameras is generally considered to be the same as the distance between the eyes1 or approximately 7 cm.</li>
	<li>Mount both cameras to a 10 x 10 cm laboratory jack and position the jack in the freezer close enough to the plant to allow the image to be focused. Adjust the cameras vertically and horizontally so that the same part of the plant is visible from both cameras. Use the jack to position both cameras vertically in such a way that the 2 images contain the whole plant and a portion of the soil.</li>
	<li>Connect the 2 cameras using a USB connector to the USB outlets on the computer.</li>
	<li>Plug the 2 A/C outlets into both cameras to allow a continuous monitoring of the plants.</li>
</ol>
2. Computer and Software Setup for Recording
<ol>
	<li>Open 2 windows (1 window for each camera) of the software by double-clicking the icon for the IR camera software 2x. Follow the instructions in the help menu to connect the left camera in the left window and the right camera in the right window. 
	Note: The details for using the software can be accessed through the help menu. A monochromatic palette is best suited for this example due to the necessity of using red-blue tinting for the 3D rendering.</li>
	<li>Open the screen capture software by double-clicking the icon for the program. Adjust the capture frame by clicking and dragging the frame so it includes both cameras to allow a screen capture of both cameras simultaneously. 
	Note: Screen capturing the video stream from both cameras simultaneously is crucial because it allows a flawless synchronization of both left and right views.</li>
	<li>Record the video in 3-h increments for an easier processing in the video processing software. 
	Note: It is impossible to know exactly when the plant will freeze, so it is important to record for some time prior to the freeze event. The option to record in segments is a feature of this software, so it is recommended that this is set to record for 3 h. The software will automatically save the 3-h recording and then begin a new recording. The file for each 3-h recording will automatically be given a numerical sequence after the name. Each video file will be from 10 to 20 GB, so ensure that sufficient space is available on a hard drive for multiple files of this size.</li>
	<li>Start the freezer program by selecting Run in the controller menu and begin the screen capture. Then press the Rec button on the window. Ensure that the outline showing the region of the screen being captured turns red.</li>
	<li>Record the strawberry plant freezing down to -8 &#176;C and hold the temperature of the freezer for 1 h.</li>
	<li>Raise the temperature of the freezer at 2 &#176;C/h until the freezer is at +2 &#176;C. Stop the recording. 
	Note: The total freezing time is 14 h.</li>
	<li>Convert the file(s) of interest from .mp4 format to .mov using a file conversion software. 
	Note: In this case, a single 3 h file containing 1 or more freeze events will be used.</li>
</ol>
3. Processing Video Using a Video Imaging Software
Note: Video imaging software will be used in this example. Tutorials on how to use the software are available online. This example will assume a basic knowledge of the software. The understanding of terms such as &#8220;composition&#8221;, &#8220;layer&#8221;, and &#8220;render queue&#8221;, as well as the various panels and how to manipulate them, is assumed.
<ol>
	<li>Double-click anywhere inside the project panel to import the .mov file of interest into the imaging software and drag the file to the Composition icon at the bottom of the project panel. Then save the project to the same folder containing the original videos. 
	Note: The video recorded will be visible in the preview pane.</li>
	<li>Click on the Region of Interest icon along the bottom of the preview window and, using the cursor, outline only the recording from the left camera.</li>
	<li>Drag the same .mov video to the Composition icon to create a second composition of the same video. Repeat step 3.3, but this time, use the cursor to select only the right camera.</li>
	<li>Select Composition &#62; Crop Comp to Region of Interest for the left view. Repeat this for the right view. Rename each composition to indicate which is left and right.</li>
	<li>Highlight the left composition by clicking on it and, in the main menu at the top, selecting Composition &#62; Add to Render Queue.</li>
	<li>In the render queue, click on Output Module and make sure the video will be rendered as a video (e.g., a QuickTime video). Click on the Render Settings to reduce the resolution to allow a faster render. Click on Output to, name the video Strawberry Left, and save it to the same folder as the original recording and the project. Click Save, and then click the Render button at the top right side of the render panel.</li>
	<li>Repeat step 3.6 for the Strawberry Right composition.</li>
	<li>Double-click the project panel and import the Strawberry Left and Strawberry Right videos that were just rendered.</li>
	<li>Highlight both videos and drag them to the composition icon at the bottom of the project panel. In the pop-up screen asking for the Still Duration, enter 3 with 5 zeros for a 3-h duration. 
	Note: Both videos, precisely synchronized, will be in the project panel, but only the topmost video in the composition panel will be visible.</li>
	<li>To view the other image, click on the little eyeball to turn off the layer. Press Control/W to allow a rotational control of the images in the preview panel using the cursor. Using the cursor and clicking the top layer on and off, adjust the rotational aspect of either the top or the bottom view to make sure both images are in the same rotational plane. Then adjust the X- and Y-plane directly within the 3D glasses subroutine.</li>
	<li>Highlight the top layer of the composition panel and select Effect &#62; Perspective &#62; 3D Glasses from the menu at the top. 
	Note: The parameters for the 3D Glasses Effect will pop up inside the control panel.</li>
	<li>In the control panel, click the box to the right of &#8220;left view&#8221; &#8212; if the control panel is not separated from the project panel, click the control panel tab at the top of the project panel. List the 2 videos in the composition panel in a drop-down menu, highlight the video in the list for the &#8220;left view&#8221;. Repeat this step for &#8220;right view&#8221;.</li>
	<li>In the box to the right of 3D View, select Red Blue LR.</li>
	<li>Using red-blue glasses, inspect the view in the project panel. If the 3D view seems to be incorrect, try clicking Swap Left-Right. Adjust Scene convergence and Vertical alignment to eliminate any ghosting and eye strain.</li>
	<li>When the 3D aspect of the video is acceptable, highlight the composition by clicking on it and select Composition &#62; Add to render queue as was done in step 3.7. Render the video into the same folder as the other files in the project. 
	Note: This file will be rather large. Once the file has been rendered, it can be re-rendered to a smaller file size using the video processing software.</li>
</ol>

<ol>
	<li>Geng, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Three-dimensional+display+technologies.">Three-dimensional display technologies.</a> Advances in Optics and Photonics. 5, 456-535 (2013).</li><li>Ceccardi, T. L., Heath, R. L., Ting, I. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Low-temperature+exotherm+measurement+using+infrared+thermography.">Low-temperature exotherm measurement using infrared thermography.</a> HortScience. 30, 140-142 (1995).</li><li>Wimmer, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> History of thermal imaging, Security Sales and Integration. , (2011).</li><li>Wisniewski, M., Lindow, S. E., Ashworth, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Observations+of+ice+nucleation+and+propagation+in+plants+using+infrared+video+thermography.">Observations of ice nucleation and propagation in plants using infrared video thermography.</a> Plant Physiology. 113, 327-334 (1997).</li><li>Kuprian, E., Tuong, T., Pfaller, K., Livingston, D. P., Neuner, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Persistent+supercooling+of+reproductive+shoots+is+enabled+by+structural+ice+barriers+being+active+despite+an+intact+xylem+connection.">Persistent supercooling of reproductive shoots is enabled by structural ice barriers being active despite an intact xylem connection.</a> Public Library of Science ONE. 11, e0163160 (2016).</li><li>Livingston, D. P., Tuong, T. D., Murphy, J. P., Gusta, L., Wisniewski, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-definition+infrared+thermography+of+ice+nucleation+and+propagation+in+wheat+under+natural+frost+conditions+and+controlled+freezing.">High-definition infrared thermography of ice nucleation and propagation in wheat under natural frost conditions and controlled freezing.</a> Planta. 247, 791-806 (2017).</li><li>Boyles, R. P., Raman, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+climate+patterns+and+trends+in+North+Carolina+(1949-1998).">Analysis of climate patterns and trends in North Carolina (1949-1998).</a> Environment International. 29 (2-3), 263-275 (2003).</li><li>Poling, E. B., Poling, E. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Managing+Cold+Events.">Managing Cold Events.</a> A Growers' Guide to Production, Economics and Marketing. , 75-97 (2015).</li><li>Hacker, J., Neuner, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ice+porpagaion+in+plants+visualized+at+the+tissue+level+by+infrared+differential+thermal+analysis+(IDTA).">Ice porpagaion in plants visualized at the tissue level by infrared differential thermal analysis (IDTA).</a> Tree Physiology. 27, 1661-1670 (2007).</li><li>He, J. Q., Harrison, R. J., Li, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+3D+imaging+system+for+strawberry+phenotyping.">A novel 3D imaging system for strawberry phenotyping.</a> Plant Methods. 13, 93-101 (2017).</li></ol>

The authors have nothing to disclose.

Two IR cameras are necessary for this protocol, and they must be aimed at the subject from slightly different angles1. This will require the lenses to be from 5 - 8 cm apart, but both must be aimed at the same place on the subject to be filmed. Think of the 2 camera lenses as a kind of surrogate for the viewer&#39;s eyes. The left camera is analogous to the left eye and the right camera to the right eye. The post-processing software will tint the left image to a red color and the right image to a blue color, so by wearing red-blue glasses, the left eye can only see the left image and the right eye only the right image. This means that it is important to use the gray-scale palette of the IR camera software when recording the freeze event. The brain will combine the 2 images which the viewer will observe in 3D1.
Another critical step to this protocol is the use of screen capture software to capture the output of both cameras simultaneously. By capturing the output of both cameras simultaneously, a perfect synchronization of the output from both cameras is guaranteed. Synchronizing the right and left images is a crucial aspect of producing 3D films and are discussed elsewhere in detail.1
To prevent any eye strain, it is important that the vertical and the horizontal convergence of the left and right images are correct. While the cameras should be positioned to ensure a correct convergence prior to the recording, they do not have to be perfect. The post-production software described here will allow adjustments in right-left, up-down, and rotational convergence. The software will also allow a red-green anaglyph video to be produced if red-blue glasses are not available.
One limitation of the technique is the requirement of red-blue glasses to view the 3D video. It is likely that many individuals will not have red-blue glasses readily available. Also, while producing a red-blue anaglyph video is the easiest and least expensive way to produce a 3D video, red-blue anaglyph videos can only convey a limited chromatic view of their subject. However, this is arguably an insignificant limitation since IR radiation, in reality, can only be observed in grayscale. Colors are only perceived by humans in the visible portion of the electromagnetic spectrum.
The limited resolution in early IR technology made it difficult to determine the precise locations of ice nucleation as well as which tissues the ice propagated into. Differential thermal analysis9 has improved the ability to detect sites of ice nucleation; however, it remains a 2-dimensional perspective that lacks information about depth. The lack of information provides a limited perspective and does not fully represent freezing as it occurs in the real world.
Commercial films use various techniques for visualizing images in 3D, the most common being polarization-interlacing1. The most popular techniques require headgear that is specific to the interlacing process, but auto-stereoscopic techniques that do not require headgear are in developmental stages1. None of the 3D rendering techniques, however, are available for viewing the IR video in 3 dimensions. In addition, while these techniques provide the clearest 3D video available, they require synchronization and special projection devices as well as reflective surfaces on which to project the images1.
Communicating scientific findings in the clearest possible manner is essential for creating a community that will promote an efficient and timely progress in scientific discoveries. Observations of the world we live in are always in 3 dimensions, but it is difficult to accurately represent those observations using only 2D images. For example, it would be difficult, if not impossible, to determine precisely which root(s) had frozen in the IR imaging of the freezing in wheat roots (Figure 2B). However, using a 3D anaglyph process makes it relatively simple to determine exactly which root froze at what time (Figure 2D). Admittedly, it remains to be determined what new information (not obtainable from 2D videography) might be gleaned from a 3D perspective of freezing in plants. However, it is not unusual for unique information to be obtained when analyzing plant material in 3D10. By using screen capture software to precisely synchronize the right-left images and commercially available software to create an anaglyph video, any laboratory that uses visual data to understand biological processes can generate images and video in 3D.

Despite living in a world of three physical dimensions, researchers are often limited to reporting visual observations in 2D. Although 2D images are generally sufficient to convey important information, this lack of information about depth restricts our ability to perceive and understand the complexity of real-world objects.1
This deficiency in information about the depth provided an incentive to produce 3D videos mainly in the commercial film industry since the early 1900s1. However, generating clear 3D information in still images and video is hindered by the complexities involved in producing those images. The simplest approach to generating 3D film is based on principles used in stereoscopic photography. Stereoscopic photography utilizes two images of the same object from slightly different angles that conveys a 3D image in the brain.To make this possible, each eye must look only at its respective image (i.e., the left eye at the left image and the right eye at the right image). Since the eyes will not naturally do this, stereoscopic headgear was designed to make this possible1. Several stereoscopic viewing techniques, as well as polarization-interlaced, time-multiplexed, and head-mount display techniques, have been used during the development of 3D films, but the color-interlacing or anaglyph method using red and green (or cyan) glasses is one of the simplest and least expensive techniques. For a comprehensive review of 3D imaging and the various techniques involved, see the review by Geng1.
Monitoring freezing in plants using IR thermography is based on the principle that when water freezes, it must give up internal energy2. This energy is in the form of heat, which is detectable in the IR region of the electromagnetic spectrum. Cameras able to record the IR energy have been in use since 19293. The first published report using IR technology to film freezing in plants is from Cecardi et al.2, but the resolution of the camera used makes it difficult to accurately determine the tissue where the freezing is initiated. Wisniewski et al.4 determined more precise sites of ice nucleation in several plant species using a higher resolution camera. As the technology used in IR thermography improved, higher resolution images led to discoveries such as barriers to freezing5 and the precise cellular localization of ice formation6.
One difficulty in filming subjects in IR is caused by small differences in temperatures. This will cause most objects in the field of view to be a similar color, making it difficult to determine precisely which object(s) is/are freezing. This can be important when determining the order of freezing in specific tissues, such as leaves or roots in wheat6. If the IR video of plants freezing could be imaged in 3D, the accuracy of determining which part of the plant is freezing at a certain point in time could be improved.
Strawberry is a crop in certain areas of the United States in which freezing temperatures are of considerable concern for the growers. Under some growing conditions, it is common for strawberry flowers to appear 2 - 3 weeks before the average last spring freeze. A freeze event can occur as late as June in some areas of the Appalachian Mountains7 and usually results in the death of the flower. Frost protection is, therefore, critical for strawberry growers in areas subject to these freeze events. Strawberry growers in North Carolina, for example, must frost-protect, on average, between 4 - 6 frost events before bloom and 1 - 2 hard freezes during the early bloom period8. To help develop strawberry genotypes that are more freezing tolerant, it is important to understand various aspects of the freezing, such as the sites of ice nucleation and propagation into other parts of the plant. IR thermography provides an effective means to address these issues.
Here, we use strawberry to illustrate a technique for recording freezing events in 3D using the anaglyph method. Strawberry is well suited for this example because the leaves and flowers are widely distributed in the 3D space and can be difficult to differentiate when viewed in 2D infrared videos.

<table border="0" cellpadding="0" cellspacing="0" width="811">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:299px;">T620 Infrared Camera and software</td>
			<td style="width:71px;">FLIR</td>
			<td style="width:119px;">55903-5122</td>
			<td style="width:323px;">2 cameras are needed. Software works only on a Windows-based computer</td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:299px;">After Effects</td>
			<td style="width:71px;">Adobe</td>
			<td style="width:119px;">15.0.1.73</td>
			<td style="width:323px;">Post-Production Video Editing Software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:299px;">Bandicam</td>
			<td style="width:71px;">Bandisoft</td>
			<td style="width:119px;">4.1.2.1385</td>
			<td>Screen Capture Software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:299px;">Laboratory Scissor Jack&#160;&#160;</td>
			<td style="width:71px;">Eisco</td>
			<td style="width:119px;">CH0642A</td>
			<td>Steel Platform 13X15 cm</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:299px;">Fastening Strap</td>
			<td style="width:71px;">Velcro</td>
			<td style="width:119px;">90441</td>
			<td>To hold camera on jack.&#160; Should be at least 60cm long by 2cm wide</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:299px;">Media Converter</td>
			<td style="width:71px;">iSkysoft</td>
			<td style="width:119px;">10.0.6</td>
			<td>Software to convert mp4 files to .mov&#160;</td>
		</tr>
	</tbody>
</table>

protocol for producing three-dimensional infrared video of freezing in plants

Freezing in plants can be monitored using infrared (IR) thermography, because when water freezes, it gives off heat. However, problems with color contrast make 2-dimensions (2D) infrared images somewhat difficult to interpret. Viewing an IR image or the video of plants freezing in 3 dimensions (3D) would allow a more accurate identification of sites for ice nucleation as well as the progression of freezing. In this paper, we demonstrate a relatively simple means to produce a 3D infrared video of a strawberry plant freezing. Strawberry is an economically important crop that is subjected to unexpected spring freeze events in many areas of the world. An accurate understanding of the freezing in strawberry will provide both breeders and growers with more economical ways to prevent any damage to plants during freezing conditions.
The technique involves a positioning of two IR cameras at slightly different angles to film the strawberry freezing. The two video streams will be precisely synchronized using a screen capture software that records both cameras simultaneously. The recordings will then be imported into the imaging software and processed using an anaglyph technique. Using red-blue glasses, the 3D video will make it easier to determine the precise site of ice nucleation on leaf surfaces.

Surprisingly, the IR video of the strawberry plant freezing (Supplemental Video 1) indicated that not all leaves/flowers froze at the same time. The leaves and flowers both froze individually at different temperatures, but the leaves froze earlier than the flowers and at a higher temperature. In addition, the freezing began in the leaves but not necessarily at the same position on each leaf. While these results have not been described previously in strawberry, similar results have been found in other plant species6. Once the leaves were frozen, the ice progressed down the petiole to the crown of the plant. When the freezer temperature became 1 or 2 degrees colder, the flowers froze beginning at the calyx and quickly spreading into the petals and receptacle (Figure 1). The receptacle remained a lighter color (warmer) longer than most other plant parts, suggesting a greater amount of water freezing.
When comparing the 2D infrared image with the 3D (wearing glasses), the 3D image makes it easier to precisely determine the order in which the leaves and the flowers froze (Figure 1). When viewing the video in 3D, it is also easier to determine the exact position on the leaves where the freezing began (Supplemental Video 1).
The survival results (not shown) indicated that despite the freezing, the leaves were not killed (not shown) by the freeze. The flowers that froze, on the other hand, died within 3 or 4 days.
A second video, this time of wheat roots (Supplemental Video 2), showed an interesting sequence of freezing. The base of these roots was submerged in growing medium consisting primarily of peat. Ice shavings were added prior to the freezing to ensure the roots would freeze. Freeze nucleation occurred at about -0.5 &#176;C midway along a root on the right side. The freezing then progressed upwards to the crown of the plant causing the base of the outer leaves to freeze. The freezing then progressed downwards in the roots at the back of the plant. Note that, without the 3D perspective, it is nearly impossible to determine the order in which the specific roots froze (Figure 2).
When considering freezing in roots (Figure 2 and Supplemental Video 2), if only a 2D perspective was viewed, it would be nearly impossible to determine which root was freezing due to the lack of information about depth. The 3D perspective of this freeze event represents the event as it occurred in the real world and greatly improves the ability of the viewer to distinguish the sequence of freezing in individual roots.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58025/58025fig1.jpg" /> 
Figure 1: A comparison of an image of strawberries in 2D to the same image in 3D. These images are freeze-frames from Supplemental Video 1 showing 2 leaves and a single flower of a strawberry plant freezing. (A) This panel shows the left view only, in 2D. (B) This panel shows the 3D anaglyph view. Red-blue glasses must be worn to see this image in true 3D. A comparison between the two panels illustrates the improvement in visual perception when the subject has been captured in 3D. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58025/58025fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58025/58025fig2.jpg" /> 
Figure 2. A comparison of images of a root mass of wheat in 2D to the same image in 3D. These images are freeze-frames from Supplemental Video 2. Panels A and B show the root mass in 2D. (A) This is an image of the roots prior to freezing, while (B) this is about midway through the freezing event. Panels C and D show the same images as Panels A and B but in anaglyph format. (C) This panel shows the root mass prior to the freezing (corresponding to panel A). (B) This is an image of the roots at the same point in the freeze event as in panel D. Panels C and D must be viewed with red-blue glasses to see the images in 3D. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58025/58025fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Video 1: A red-blue anaglyph video showing freezing in a strawberry plant in 3D. This video was generated using the protocol demonstrated here. Note that red-blue glasses are necessary to observe the video in 3D. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58025/Supplimental_Video_1.mov">Please click here to download this file.</a>
Supplemental Video 2: A red-blue anaglyph video showing freezing in wheat roots in 3D. This video was generated using the protocol demonstrated here. Note that red-blue glasses are necessary to observe the video in 3D. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58025/Supplimental_Video_2.mov">Please click here to download this file.</a>

Watch this Scientific Journal Video about Protocol for Producing Three-Dimensional Infrared Video of Freezing in Plants at JoVE.com

Protocol for Producing Three-Dimensional Infrared Video of Freezing in Plants

Here, we present a protocol to image a strawberry plant freezing in 3 dimensions. Two infrared cameras positioned at slightly different angles are used to produce a red-blue anaglyph video to observe the freezing of the plant in 3 dimensions.

Freezing in plants can be monitored using infrared (IR) thermography, because when water freezes, it gives off heat. However, problems with color contrast ...

protocol-for-producing-three-dimensional-infrared-video-freezing

Nanyang Technological University

Research

JoVE Journal

Environment

6.1K Views.  United States Department of Agriculture and North Carolina State University. Here, we present a protocol to image a strawberry plant freezing in 3 dimensions. Two infrared cameras positioned at slightly different angles are used to produce a red-blue anaglyph video to observe the freezing of the plant in 3 dimensions.

Video: Protocol for Producing Three-Dimensional Infrared Video of Freezing in Plants

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Coexistence theory has often treated environmental heterogeneity as being independent of the community composition; however biotic feedbacks such as plant-soil feedbacks (PSF) have large effects on plant performance, and create environmental heterogeneity that depends on the community composition. Understanding the importance of PSF for plant community assembly necessitates understanding of the role of heterogeneity in PSF, in addition to mean PSF effects. Here, we describe a protocol for manipulating plant-induced soil heterogeneity. Two example experiments are presented: (1) a field experiment with a 6-patch grid of soils to measure plant population responses and (2) a greenhouse experiment with 2-patch soils to measure individual plant responses. Soils can be collected from the zone of root influence (soils from the rhizosphere and directly adjacent to the rhizosphere) of plants in the field from conspecific and heterospecific plant species. Replicate collections are used to avoid pseudoreplicating soil samples. These soils are then placed into separate patches for heterogeneous treatments or mixed for a homogenized treatment. Care should be taken to ensure that heterogeneous and homogenized treatments experience the same degree of soil disturbance. Plants can then be placed in these soil treatments to determine the effect of plant-induced soil heterogeneity on plant performance. We demonstrate that plant-induced heterogeneity results in different outcomes than predicted by traditional coexistence models, perhaps because of the dynamic nature of these feedbacks. Theory that incorporates environmental heterogeneity influenced by the assembling community and additional empirical work is needed to determine when heterogeneity intrinsic to the assembling community will result in different assembly outcomes compared with heterogeneity extrinsic to the community composition.

Understanding the role of environmental heterogeneity in species coexistence has typically focused on types of heterogeneity that are extrinsic to the community&#8217;s species composition. We provide novel detailed methods for creating soil heterogeneity treatments using soils subject to plant-soil feedback conditioning, or heterogeneity intrinsic to the community composition.

Coexistence theory has often treated environmental heterogeneity as being independent of the community composition; however biotic feedbacks such as ...

Expression of Recombinant Cellulase Cel5A from Trichoderma reesei in Tobacco Plants

Cellulose degrading enzymes, cellulases, are targets of both research and industrial interests. The preponderance of these enzymes in difficult-to-culture organisms, such as hyphae-building fungi and anaerobic bacteria, has hastened the use of recombinant technologies in this field. Plant expression methods are a desirable system for large-scale production of enzymes and other industrially useful proteins. Herein, methods for the transient expression of a fungal endoglucanase, Trichoderma reesei Cel5A, in Nicotiana tabacum are demonstrated. Successful protein expression is shown, monitored by fluorescence using an mCherry-enzyme fusion protein. Additionally, a set of basic tests are used to examine the activity of transiently expressed T. reesei Cel5A, including SDS-PAGE, Western blotting, zymography, as well as&#160;fluorescence and dye-based substrate degradation assays. The system described here can be used to produce an active cellulase in a short time period, so as to assess the potential for further production in plants through constitutive or inducible expression systems.

Expression of Recombinant Cellulase Cel5A from Trichoderma reesei in Tobacco Plants

Tobacco plants were used to produce a fungal cellulase, TrCel5A, via a transient expression system. The expression could be monitored using a fluorescent fusion protein, and the protein activity was characterized post-expression.

Cellulose degrading enzymes, cellulases, are targets of both research and industrial interests. The preponderance of these enzymes in difficult-to-culture ...

Measuring Fluxes of Mineral Nutrients and Toxicants in Plants with Radioactive Tracers

Unidirectional influx and efflux of nutrients and toxicants, and their resultant net fluxes, are central to the nutrition and toxicology of plants. Radioisotope tracing is a major technique used to measure such fluxes, both within plants, and between plants and their environments. Flux data obtained with radiotracer protocols can help elucidate the capacity, mechanism, regulation, and energetics of transport systems for specific mineral nutrients or toxicants, and can provide insight into compartmentation and turnover rates of subcellular mineral and metabolite pools. Here, we describe two major radioisotope protocols used in plant biology: direct influx (DI) and compartmental analysis by tracer efflux (CATE). We focus on flux measurement of potassium (K+) as a nutrient, and ammonia/ammonium (NH3/NH4+) as a toxicant, in intact seedlings of the model species barley (Hordeum vulgare L.). These protocols can be readily adapted to other experimental systems (e.g., different species, excised plant material, and other nutrients/toxicants). Advantages and limitations of these protocols are discussed.

In planta measurement of nutrient and toxicant fluxes is essential to the study of plant nutrition and toxicity. Here, we cover radiotracer protocols for influx and efflux determination in intact plant roots, using potassium (K+) and ammonia/ammonium (NH3/NH4+) fluxes as examples. Advantages and limitations of such techniques are discussed.

Unidirectional influx and efflux of nutrients and toxicants, and their resultant net fluxes, are central to the nutrition and toxicology of plants. ...

The Use of High-resolution Infrared Thermography (HRIT) for the Study of Ice Nucleation and Ice Propagation in Plants

Freezing events that occur when plants are actively growing can be a lethal event, particularly if the plant has no freezing tolerance. Such frost events often have devastating effects on agricultural production and can also play an important role in shaping community structure in natural populations of plants, especially in alpine, sub-arctic, and arctic ecosystems. Therefore, a better understanding of the freezing process in plants can play an important role in the development of methods of frost protection and understanding mechanisms of freeze avoidance. Here, we describe a protocol to visualize the freezing process in plants using high-resolution infrared thermography (HRIT). The use of this technology allows one to determine the primary sites of ice formation in plants, how ice propagates, and the presence of ice barriers. Furthermore, it allows one to examine the role of extrinsic and intrinsic nucleators in determining the temperature at which plants freeze and evaluate the ability of various compounds to either affect the freezing process or increase freezing tolerance. The use of HRIT allows one to visualize the many adaptations that have evolved in plants, which directly or indirectly impact the freezing process and ultimately enables plants to survive frost events.

The Use of High-resolution Infrared Thermography HRIT for the Study of Ice Nucleation and Ice Propagation in Plants

Here we present a protocol that allows one to visualize sites of ice formation and avenues of ice propagation in plants utilizing high resolution infrared thermography (HRIT).

Freezing events that occur when plants are actively growing can be a lethal event, particularly if the plant has no freezing tolerance. Such frost events ...

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to climate change and crop management. Quantitative size information on roots in their native environment is invaluable for studying root growth and environmental processes involving plants. X-ray computed tomography (XCT) has been demonstrated to be an effective tool for in situ root scanning and analysis. We aimed to develop a costless and efficient tool that approximates the surface and volume of the root regardless of its shape from three-dimensional (3D) tomography data. The root structure of a Prairie dropseed (Sporobolus heterolepis) specimen was imaged using XCT. The root was reconstructed, and the primary root structure was extracted from the data using a combination of licensed and open-source software. An isosurface polygonal mesh was then created for ease of analysis. We have developed the standalone application imeshJ, generated in MATLAB1, to calculate root volume and surface area from the mesh. The outputs of imeshJ are surface area (in mm2) and the volume (in mm3). The process, utilizing a unique combination of tools from imaging to quantitative root analysis, is described. A combination of XCT and open-source software proved to be a powerful combination to noninvasively image plant root samples, segment root data, and extract quantitative information from the 3D data. This methodology of processing 3D data should be applicable to other material/sample systems where there is connectivity between components of similar X-ray attenuation and difficulties arise with segmentation.

A methodology for obtaining visual and quantitative root structure information from X-ray computed tomography data acquired in-soil is presented.

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to ...

Video Tracking Protocol to Screen Deterrent Chemistries for Honey Bees

The European honey bee, Apis mellifera L., is an economically and agriculturally important pollinator that generates billions of dollars annually. Honey bee colony numbers have been declining in the United States and many European countries since 1947. A number of factors play a role in this decline, including the unintentional exposure of honey bees to pesticides. The development of new methods and regulations are warranted to reduce pesticide exposures to these pollinators. One approach is the use of repellent chemistries that deter honey bees from a recently pesticide-treated crop. Here, we describe a protocol to discern the deterrence of honey bees exposed to select repellent chemistries. Honey bee foragers are collected and starved overnight in an incubator 15 h prior to testing. Individual honey bees are placed into Petri dishes that have either a sugar-agarose cube (control treatment) or sugar-agarose-compound cube (repellent treatment) placed into the middle of the dish. The Petri dish serves as the arena that is placed under a camera in a light box to record the honey bee locomotor activities using video tracking software. A total of 8 control and 8 repellent treatments were analyzed for a 10 min period with each treatment was duplicated with new honey bees. Here, we demonstrate that honey bees are deterred from the sugar-agarose cubes with a compound treatment whereas honey bees are attracted to the sugar-agarose cubes without an added compound.

The loss of honey bee colonies presents a challenge to crop pollination services. Current pollinator protection practices warrant an alternative approach to minimize the contact of honey bees to harmful pesticides using repellent chemistries. Here, we provide detailed methods for a visual tracking protocol to screen deterrents for bees.

The European honey bee, Apis mellifera L., is an economically and agriculturally important pollinator that generates billions of dollars annually. Honey ...

Dispersion of Nanomaterials in Aqueous Media: Towards Protocol Optimization

The sonication process is commonly used for de-agglomerating and dispersing nanomaterials in aqueous based media, necessary to improve homogeneity and stability of the suspension. In this study, a systematic step-wise approach is carried out to identify optimal sonication conditions in order to achieve a stable dispersion. This approach has been adopted and shown to be suitable for several nanomaterials (cerium oxide, zinc oxide, and carbon nanotubes) dispersed in deionized (DI) water. However, with any change in either the nanomaterial type or dispersing medium, there needs to be optimization of the basic protocol by adjusting various factors such as sonication time, power, and sonicator type as well as temperature rise during the process. The approach records the dispersion process in detail. This is necessary to identify the time points as well as other above-mentioned conditions during the sonication process in which there may be undesirable changes, such as damage to the particle surface thus affecting surface properties. Our goal is to offer a harmonized approach that can control the quality of the final, produced dispersion. Such a guideline is instrumental in ensuring dispersion quality repeatability in the nanoscience community, particularly in the field of nanotoxicology.

Here, we present a step-wise protocol for the dispersion of nanomaterials in aqueous media with real-time characterization to identify the optimal sonication conditions, intensity, and duration for improved stability and uniformity of nanoparticle dispersions without impacting the sample integrity.

The sonication process is commonly used for de-agglomerating and dispersing nanomaterials in aqueous based media, necessary to improve homogeneity and ...

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

The use of video camera systems in ecological studies of fish continues to gain traction as a viable, non-extractive method of measuring fish lengths and estimating fish abundance. We developed and implemented a rotating stereo-video camera tool that covers a full 360 degrees of sampling, which maximizes sampling effort compared to stationary camera tools. A variety of studies have detailed the ability of static, stereo-camera systems to obtain highly accurate and precise measurements of fish; the focus here was on the development of methodological approaches to quantify fish density using rotating camera systems. The first approach was to develop a modification of the metric MaxN, which typically is a conservative count of the minimum number of fish observed on a given camera survey. We redefine MaxN to be the maximum number of fish observed in any given rotation of the camera system. When precautions are taken to avoid double counting, this method for MaxN may more accurately reflect true abundance than that obtained from a fixed camera. Secondly, because stereo-video allows fish to be mapped in three-dimensional space, precise estimates of the distance-from-camera can be obtained for each fish. By using the 95% percentile of the observed distance from camera to establish species-specific areas surveyed, we account for differences in detectability among species while avoiding diluting density estimates by using the maximum distance a species was observed. Accounting for this range of detectability is critical to accurately estimate fish abundances. This methodology will facilitate the integration of rotating stereo-video tools in both applied science and management contexts.

We describe a new method for counting fishes, and estimating relative abundance (MaxN) and fish density using rotating stereo-video camera systems. We also demonstrate how to use distance from camera (Z distance) to estimate species-specific detectability.

The use of video camera systems in ecological studies of fish continues to gain traction as a viable, non-extractive method of measuring fish lengths and ...

Experimental Protocol for Detecting Mitochondrial Function in Hepatocytes Exposed to Organochlorine Pesticides

This paper presents detailed methods on detecting hepatic mitochondrial function for a better understanding the cause of metabolic disorders caused by environmental organochlorine pesticides (OCPs) in hepatocytes. HepG2 cells were exposed to &#946;-hexachlorocyclohexane (&#946;-HCH) for 24 h at an equivalent dose of internal exposure in general population. Ultrastructure in hepatocytes was examined by transmission electron microscopy (TEM) to show the damage of mitochondria. Mitochondrial function was further evaluated by mitochondrial fluorescence intensity, adenosine 5'-triphosphate (ATP) levels, oxygen consumption rate (OCR) and mitochondrial membrane potential (MMP) in HepG2 cells incubated with &#946;-HCH. The mitochondria fluorescence intensity after stained by mitochondrial green fluorescent probe was observed with a fluorescence microscopy. The luciferin-luciferase reaction was used to determine ATP levels. The MMP was detected by the cationic dye JC-1 and analyzed under flow cytometry. OCR was measured with an extracellular flux analyzer. In summary, these protocols were used in detecting mitochondrial function in hepatocytes with to investigate mitochondria damages.

Understanding the influence of environmental organochlorine pesticides (OCPs) on mitochondrial function in hepatocytes is important in exploring the mechanism of OCPs causing metabolic disorders. This paper presents detailed methods on detecting hepatic mitochondrial function.

This paper presents detailed methods on detecting hepatic mitochondrial function for a better understanding the cause of metabolic disorders caused by ...

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

As individuals increasingly live in cities, methods to study their everyday movements and the data that can be collected becomes important and valuable. Eye-tracking informatics are known to connect to a range of feelings, health conditions, mental states and actions. But because vision is the result of constant eye-movements, teasing out what is important from what is noise is complex and data intensive. Furthermore, a significant challenge is controlling for what people look at compared to what is presented to them.
The following presents a methodology for combining and analyzing eye-tracking on a video of a natural and complex scene with a machine learning technique for analyzing the content of the video. In the protocol we focus on analyzing data from filmed videos, how a video can be best used to record participants&#39; eye-tracking data, and importantly how the content of the video can be analyzed and combined with the eye-tracking data. We present a brief summary of the results and a discussion of the potential of the method for further studies in complex environments.

The objective of the protocol is to detail how to collect video data for use in the laboratory; how to record eye-tracking data of participants looking at the data and how to efficiently analyze the content of the videos that they were looking at using a machine learning technique.

As individuals increasingly live in cities, methods to study their everyday movements and the data that can be collected becomes important and valuable. ...