This work was performed in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the Department of Energy's Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory.

Caution: The operation of an X-ray tomography scanner requires both general radiation training, and instrument-specific radiation safety training. All corresponding procedures relevant to the experimenter&#39;s laboratory should be followed.&#160;
1. Root Imaging
Note: This step describes the imaging of a grass specimen held in its original soil in a tubular plastic pot (a plastic tube with a diameter of 40 mm, a height of 210 mm, and wall thickness of about 2 mm).
<ol>
	<li>Place potted plant on the sample manipulator of the instrument at a distance desired for target magnification. For a plant in a 2-inch diameter holder, the sample to source distance should be about 3 inches (7 cm).</li>
	<li>Adjust the X-ray scan settings to achieve optimal color (grey level) contrast in detector image. Note: These settings are available in the instrument control software used.
	<ol>
		<li>Set X-ray power settings; 85 kV and 190 &#956;A were used in this example.</li>
		<li>Set exposure time. Here, a relatively long exposure time of 1 sec was used for better signal-to-noise ratio.</li>
		<li>Set number of projections and frames per projection; 4 frames per projection for a total of 3,142 projections is suggested for good data statistics.</li>
		<li>Run a shading correction using the measurement conditions set above by selecting the &#34;Shading correction&#34; tab, and clicking &#34;Create&#34;. 
		Note: The shading correction compensates for the variation in response of the imaging device&#39;s pixels when illuminated with a constant set of X-ray flux. The process takes blank images (with sample removed from beam path) with the X-ray beam turned on, and with the beam turned off. This correction is applied to all images collected.</li>
		<li>Select the &#34;Minimize ring artefacts&#34; option (also called &#34;shuttling mode&#34;); the sample will be rotated in angular steps whilst the projection images are acquired. This leads to data acquisition at a slower rate, but helps eliminate ring artefacts.</li>
		<li>Start the scan by clicking the &#34;Acquire&#34; button under the Acquisition tab (with the settings outlined above, image collection will take approximately 4 hr).</li>
	</ol>
	</li>
</ol>
2. Data Reconstruction&#160;
Note: This section describes the reconstruction of 3D volume data from the raw images (radiographs from the CT scan).
<ol>
	<li>Load the raw data into the program.</li>
	<li>Compare first and last image (they should be nearly identical as the last image is taken after a 360o rotation of the specimen) to make sure the specimen did not move or the scan settings did not change during data acquisition.</li>
	<li>Calculate the center of rotation (COR) by selecting the &#34;Center of rotation&#34; tab, and clicking &#34;Start&#34;; use options &#34;Automatic&#34; COR finding with &#34;High Quality&#34; accuracy, and &#34;Dual&#34; (upper and lower) slice selection for COR calculation.</li>
	<li>Select the sample volume to be reconstructed: select the &#34;Volume&#34; tab, and edit the volume selection windows using the thumbnails.</li>
	<li>Perform reconstruction to create the volume file containing 3D data by clicking &#34;Start&#34;.</li>
</ol>
3. Data Processing / Segmentation
Note: This section describes the steps to be taken to prepare the reconstructed data for further processing in the program RooTrak to track roots as they branch out through soil, and isolate the roots from any surrounding material to produce a stack of binary images of just the root itself.
<ol>
	<li>Processing of volume data in ImageJ to prepare a RooTrak processable image stack:
	<ol>
		<li>Load the volume file into ImageJ.</li>
		<li>Optimize image contrast between root and soil by adjusting brightness and contrast settings (Click Image/Adjust/Brightness/Contrast). When the region of interest within the image is visible and clearly distinguishable, the settings are considered optimized.</li>
		<li>Save as an image stack in jpeg, bmp, or png format.</li>
	</ol>
	</li>
	<li>Processing in RooTrak to segment the root:
	<ol>
		<li>Load image stack into RooTrak (go to &#34;Tools&#34; tab, and press &#34;Tracker&#34;).</li>
		<li>Set seed points inside root: click several points inside each of the pertinent root sections visible in the top view slice of the volume data.</li>
		<li>Set tracker parameters &#34;Smoothness&#34; and &#34;Similarity&#34; to 0.3 and 0.8, respectively.</li>
		<li>Run the tracking function. This will follow the root from the top image slice all the way to the bottom slice.</li>
		<li>After viewing the volume data, select the number of slices according to the useable data volume; in this case, tracking was stopped at 200 slices, equivalent to a depth of 6.2 mm, where root boundaries became ill-defined (the image of the tracked root started to blend into that of the soil). 
		Note: The image stack produced will be saved automatically to wherever the output directory was created.</li>
	</ol>
	</li>
</ol>
4. Volume and Surface Analysis
Note: This step describes the isosurface mesh generation from the image stack created by RooTrak.
<ol>
	<li>Convert image stack from RooTrak into a binary image format in ImageJ. Select &#34;Process&#34;, then &#34;Binary&#34;, then &#34;Make Binary&#34;.</li>
	<li>Use the open-source ImageJ plugin, BoneJ, to create the triangular mesh; in ImageJ select &#34;Plugins&#34;, then &#34;BoneJ&#34;, then &#34;Isosurface&#34;.</li>
	<li>Set &#34;Resampling&#34; and &#34;Threshold&#34; to 6 and 120, respectively (Default settings). Check &#34;Show surface&#34;, and press the &#34;OK&#34; button.</li>
	<li>On the &#34;3D viewer&#34; click on the File tab, then on &#34;Export surfaces&#34;, then save as &#34;STL (binary)&#34;.</li>
	<li>Open imeshJ, select the STL file and enter voxel size in microns. Click &#34;Calculate Surface Area&#34; to acquire total sample root surface area in mm2. Similarly, click &#34;Calculate Volume&#34; to obtain total sample root volume in mm3.</li>
</ol>

<ol>
	<li>McKenzie, B. 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+Rhizosphere:+An+Ecological+Perspective.">The Rhizosphere: An Ecological Perspective.</a> Eur. J. Soil Sci. 59 (2), 416-417 (2008).</li><li>Farrar, J., Hawes, M., Jones, D., Lindow, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+roots+control+the+flux+of+carbon+to+the+rhizosphere.">How roots control the flux of carbon to the rhizosphere.</a> Ecology. 84 (4), 827-837 (2003).</li><li>Gregory, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Roots+rhizosphere+and+soil:+the+route+to+a+better+understanding+of+soil+science?.">Roots rhizosphere and soil: the route to a better understanding of soil science?.</a> Eur. J. Soil Sci. 57 (1), 2-12 (2006).</li><li>Philippot, L., Raaijmakers, J. M., Lemanceau, P., van der Putten, W. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Going+back+to+the+roots:+the+microbial+ecology+of+the+rhizosphere.">Going back to the roots: the microbial ecology of the rhizosphere.</a> Nat. Rev. Microbiol. 11 (11), 789-799 (2013).</li><li>Gregory, P. J., Hutchison, D. J., Read, D. B., Jenneson, P. M., Gilboy, W. B., Morton, E. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Non-invasive+imaging+of+roots+with+high+resolution+X-ray+micro-tomography.">Non-invasive imaging of roots with high resolution X-ray micro-tomography.</a> Plant and Soil. 255 (1), 351-359 (2003).</li><li>Mairhofer, S., 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=RooTrak:+Automated+Recovery+of+Three-Dimensional+Plant+Root+Architecture+in+Soil+from+X-Ray+Microcomputed+Tomography+Images+Using+Visual+Tracking.">RooTrak: Automated Recovery of Three-Dimensional Plant Root Architecture in Soil from X-Ray Microcomputed Tomography Images Using Visual Tracking.</a> Plant Physiol. 158 (2), 561-569 (2012).</li><li>Anderson, S. H., Hopmans, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Soil-Water-Root Processes: Advances in Tomography and Imaging. , (2013).</li><li>Mairhofer, S., 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=Recovering+complete+plant+root+system+architectures+from+soil+via+X-ray+mu-Computed+Tomography.">Recovering complete plant root system architectures from soil via X-ray mu-Computed Tomography.</a> Plant Methods. 9, 8 (2013).</li><li>Lorensen, W. E., Cline, H. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Marching+cubes:+a+high+resolution+3D+surface+construction+algorithm.">Marching cubes: a high resolution 3D surface construction algorithm.</a> Comput. Graph. 21 (4), 163-169 (1987).</li><li>Lobet, G., Draye, X., Perilleux, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+online+database+for+plant+image+analysis+software+tools.">An online database for plant image analysis software tools.</a> Plant Methods. 9 (38), (2013).</li><li>Schmidt, T., 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+iRoCS+Toolbox+-+3D+analysis+of+the+plant+root+apical+meristem+at+cellular+resolution.">The iRoCS Toolbox - 3D analysis of the plant root apical meristem at cellular resolution.</a> Plant J. 77 (5), 806-814 (2014).</li><li>Galkovskyi, T., 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=GiA+Roots:+software+for+the+high+throughput+analysis+of+plant+root+system+architecture.">GiA Roots: software for the high throughput analysis of plant root system architecture.</a> BMC Plant Biol. 12, 116 (2012).</li><li>Clark, R., 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=3-Dimensional+Root+Phenotyping+with+a+Novel+Imaging+and+Software+Platform.">3-Dimensional Root Phenotyping with a Novel Imaging and Software Platform.</a> Plant Physiol. 156, 455-465 (2011).</li><li>. RootSnap! Available from: <a target="_blank" href="https://www.cid-inc.com">https://www.cid-inc.com</a> (2013)</li><li>Arsenault, J. L., Pouleur, S., Messier, C., Guay, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=WinRHIZO&#8482;+a+root-measuring+system+with+a+unique+overlap+correction+method.">WinRHIZO&#8482; a root-measuring system with a unique overlap correction method.</a> HortSci. 30, 906-906 (1995).</li></ol>

We have nothing to disclose.

A combination of X-ray computed tomography and several open-source programs proved to be a powerful combination to noninvasively image plant root samples, segment root data, and extract quantitative information (surface area and volume) from the 3D data. Our ability to visualize and measure features is always limited by scan resolution, as well as by limitations of the RooTrak software. However, scan resolution was sufficient to capture the majority of the features of the sample in this study, and RooTrak was able to successfully segment a significant portion of the root. The version of RooTrak employed in this work did not track upward traveling root segments (upward branching was not present to a significant extent in the specimen studied here); a newer version of the program addresses this problem9.
As hinted in the Introduction, a survey of the literature suggested that a costless software option to calculate root volume/surface area from 3D tomography data on specimens in the centimeter range and above was highly desirable. The significance of the approach reported here is that, after the root is segmented, it works with a 3D data format created by the widely used program ImageJ. The heart of the analysis is the calculation of the volume and surface area of the root from its image approximated by a triangular mesh. The triangular mesh generated by ImageJ in the .stl format, is represented by the indices of the triangles that make up the surface approximation. imeshJ extracts the indices from the binary file format and is able to calculate the surface area from the indices (which is simply half the magnitude of the cross product between vectors joining points [1,2] and points [1,3].) The volume is calculated by constructing a tetrahedron between the triangle points and the origin, and finding the scalar triple-product between the position vectors of the triangle. Although there are several resources for such calculations, the challenge was to efficiently compute the surface area and volume for meshes that contained upwards of 10 million triangles (as may be the case in larger root structures.) By eliminating loop structures from the code, which would process the triangles one by one, and implementing several methods that allow simultaneous processing of multiple data points, we were able to run the calculations on a 6 million-triangle mesh in under 5 sec. With regards to sample size, a potted plant in a 20-ounce container would cover the whole detector range under the same magnification and would result in a 2,000-slice image stack as dataset. If we extrapolate the speed of the calculation from a 200-slice image stack (the sample size of the processed root in this study, 6.2 mm) processed in 5 sec&#160;to a 2,000-slice image stack, imeshJ should still process that under 1 min.
The products of this method are the surface area and volume of the root sample, which were determined without removing the plant from the soil, or disturbing it in any way. The calculated surface area is 351.87 mm2, the volume is 47.27 mm3. Other, intermediate, products are a 3D visualization of the root structure, and the triangular mesh of the roots (Figure&#160;6).
The critical steps in the present protocol are the noninvasive tomographic imaging of the specimen to provide data with sufficient density contrast (step 1 in Protocol), the segmentation of the root portion of study from the rest of the sample (step 3), and the calculation of the root volume and surface area from the triangular mesh, isosurface (step 4). To achieve optimal density contrast, the X-ray power settings, 85 kV and 190 &#956;A, were chosen based on detector response for the present sample; lower X-ray power would have produced inferior color contrast, while higher power would have saturated the detector. The grey level histogram feature of the data collection software guides the user in deciding what power settings to use. In general, soil-plant samples with significant organic content tend to require lower (&#62;100 kV) X-ray voltage settings.
The accuracy of the calculated surface area is dependent on the assumption that the isosurface produced by ImageJ is a reasonable approximation to the actual surface of the root. This assumption is reasonable for large root segments, but may prove lesser so when roots with dimensions comparable to the voxel size are imaged. For the sample in this study, the size of the whole root and its segments were magnitudes greater than the voxel size. The default ImageJ/BoneJ settings (see &#34;Threshold&#34; parameter in 4.3 of Protocol) resulting in 3.6x10-4 mm2 triangles versus the 9.6x10-4 mm2 image pixel size must have provided an accurate estimate of the root surface area. For specimens with smaller features requiring greater detail, the number of triangles approximating the surface may be increased (triangle size decreased) by lowering the &#34;Threshold&#34; value. This will result in longer computational time. For root segments with dimensions approaching the voxel size, the instrumental resolution becomes the bottleneck, since the voxel representation becomes less accurate. The accuracy of the calculated volume figure is also dependent on the above assumption, but to a smaller degree because smaller-diameter roots contribute proportionally less to the volume of the root than the surface area of the root. However, a smaller cross section of the root can be scanned just as effectively if distance from the X-ray source is minimized. In other words, root segments requiring higher-resolution data, would need to be re-scanned at a higher magnification, and more accurate metrics may be obtained. The accuracy of the surface area calculation may also be affected by imperfect segmentation of the root from the soil. While our ability to distinguish the root from the soil depends on the color (grey level) contrast achieved in the imaging step, improvement of the segmentation step would help reduce any error in the calculation. Development of a new code that would improve the RooTrak segmentation process is underway. The calculation done by imeshJ was verified by comparing the output of the program for simple, single-component specimens of known volume and surface area in our laboratory.
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 sample segmentation. imeshJ will work on 3D data from any sources (PET, MRI scans) on any object of interest as long as an image stack is created from the data, which is used to create an isosurface, and the STL file that imeshJ uses. The calculated surface area and volume figures should be compared to values obtained on the same sample by other means (to be developed) to assess the accuracy of these calculations. Such comparison will be important for our ability to further refine the imeshJ code. Future plans include the development of a new root tracking tool and the imeshJ code for high-throughput imaging of plant root samples.

Roots, as part of the rhizosphere2-5, represent an &#34;invisible&#34; part of plant biology since soil makes it difficult to image roots non-invasively 6, 7. However, studying root growth and interaction within the soil environment is critical to understanding root/plant growth and nutrient cycling, which in turn affect forestation, food security, and climate. X-ray computed tomography (XCT) has proven to be a valuable tool for noninvasive imaging of plant root samples in their local environments 8. In order to measure root development and dimensional changes under different conditions, and be able to compare data from different datasets/specimens, one needs to extract quantitative information from the tomography data. Segmentation of the root data from that of the surrounding soil, that is, the isolation of the root image from everything else around it (including, for example, a neighboring plant) is a critical step before accurate size analysis can be done. However, a simple thresholding approach is often unfeasible for root data. The challenges associated with imaging plant roots in soil include variations in the X-ray attenuation properties of the root material, and the overlap in attenuation values between root and soil caused by water and organic matter. These issues have been superbly addressed recently by Mairhofer et al. in their visual tracking tool RooTrak 7, 9. The next step after a successful segmentation is the accurate determination of root volume and surface area. The volume may be estimated by counting the number of voxels and multiplying by the voxels&#39; size cubed as shown before7. For a more accurate determination of root surface area and volume, the isosurface of the segmented root system can be represented by a mesh of triangles, using an algorithm known as Marching Cubes 10. The open-source ImageJ11 can be employed to approximate the root volume based on the Marching Cubes algorithm. To the best of our knowledge, only a limited number of open-source software dedicated to calculating tomography-based volume/surface data for root specimens in the centimeter range and above is currently available 12. One open-source software we looked at13 focuses on root growth and is aimed at cellular features enabling quantitative volume analysis at single-cell resolution. Some open-source software dedicated to whole root systems14 is excellent for small-diameter tubular root systems based on the approximation that their shape is actually tubular. However, some work with 2D images and are unable to handle 3D stacks14. Furthermore, the tubular shape approximation may not be valid when root systems with rough surfaces and non-uniform shapes, such as those of trees, are studied. Another approach15 uses two-dimensional (2D) rotational image sequences innovatively circumventing the need for a costly CT scanner. It measures, records, and displays root system lengths. The software we have tested from those only available commercially16-18; one does not appear to be able to handle 3D image stacks16, the second is a leaf area and root length measurement tool17, while the third is based on color analysis18. Based on this survey, we suggest that a costless option that approximates the surface and volume of the root regardless of its shape from 3D tomography data is desirable.
Building on the freely available RooTrak and ImageJ, we have developed a program, named imeshJ (see Supplemental Code File) which processes an isosurface mesh (surface stereolithography file) generated from segmented root data, and calculates the volume and surface area of the root by doing simple geometrical calculations on the mesh triangle index data. Here we report a method that combines the use of XCT imaging, data reconstruction and visualization (software CT Pro 3D and VG Studio), segmentation of the root of the specimen from the soil in the 3D data (open-source software ImageJ and RooTrak), and extraction of the surface and volume information from a triangular mesh (ImageJ and the computer code imeshJ).

<table>
	<tbody>
		<tr>
			<td>X-Tek/Metris XTH 320/225 kV&#160;</td>
			<td>Nikon Metrology</td>
			<td>n/a</td>
			<td>X-ray tomography scanner</td>
		</tr>
		<tr>
			<td>Inspect X</td>
			<td>Nikon Metrology</td>
			<td>n/a</td>
			<td>Instrument control software</td>
		</tr>
		<tr>
			<td>CT Pro 3D</td>
			<td>Nikon Metrology</td>
			<td>n/a</td>
			<td>Reconstruction software, version XT 2.2</td>
		</tr>
		<tr>
			<td>VG Studio MAX</td>
			<td>Visual Graphics GmbH</td>
			<td>n/a</td>
			<td>Visualization software for 3D volumes, version 2.1.5</td>
		</tr>
		<tr>
			<td>ImageJ</td>
			<td>Open-source</td>
			<td>n/a</td>
			<td>Image processing and analysis software, version 1.6</td>
		</tr>
		<tr>
			<td>RooTrak</td>
			<td>Open-source</td>
			<td>n/a</td>
			<td>Root segmentation software, version 0.3.1-b1 beta</td>
		</tr>
		<tr>
			<td>imeshJ</td>
			<td>EMSL</td>
			<td>n/a</td>
			<td>MATLAB script developed by the authors</td>
		</tr>
		<tr>
			<td>Prairie dropseed grass sample</td>
			<td>n/a</td>
			<td>n/a</td>
			<td>Sample obtained from ground in residential area</td>
		</tr>
	</tbody>
</table>

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.

The specimen consisting of two stems of the native grass Prairie dropseed (Sporobolus heterolepis) and the original soil around it was taken from a residential area and placed in a small tube-shaped holder seen in Figure&#160;1. The reconstructed data voxel size was approximately 31 &#181;m x 31 &#181;m x 31 &#181;m. The reconstructed volume file was used to create a stack of images from a selected orientation (top view) using the open-source image-processing program ImageJ 1.611. The volume data was also brightened in this program to increase contrast between root and soil values. From the reconstructed data, it was clear that the root and some components of the soil, most likely organic matter, have very similar X-ray attenuation factors resulting in little to no grey-scale contrast in the images (Figure&#160;2).
RooTrak, the program used for segmentation, is an open source program developed at the Center for Integrative Plant Biology at the University of Nottingham7. It is specifically designed to track roots as they branch out through soil, and isolate the roots from surrounding material to produce a stack of binary images. RooTrak has been shown to produce segmentations better than simple thresholding for root data7, 9. A seed point was selected inside each of the pertinent root sections visible in the top slice of the volume data (Figure&#160;3) then the tracking function of the software was run. The RooTrak parameters &#34;Smoothness&#34; and &#34;Similarity&#34; were set to 0.3 and 0.8 respectively. This range constantly provides good grey value separation and isolates the region of interest well. RooTrak successfully segmented the selected 200 slices of volume data (Figure&#160;4), which was equivalent to a depth of 6.2 mm. See segmentation of the root in the animation file (Rootvideo.mov).
ImageJ was used to generate a triangular mesh, isosurface, of the 3D volume (approximating the surface of the isolated root) from the data produced by RooTrak (Figure&#160;5). Default settings employed for &#34;Resampling&#34; and &#34;Threshold&#34; in ImageJ plugin BoneJ (see 4.3 in Protocol) were selected because they produce a detailed isosurface in a relatively quick manner. Adjusting the resampling level will affect the amount of time it takes to render the isosurface. The mesh was saved in the STL format, and imeshJ was used to compute the surface area and volume of the mesh. For the reconstructed volume in the present study, the calculated surface area was 351.87 mm2, and the volume 47.27 mm3 (see Figure&#160;6).
<img alt="Figure 1" src="/files/ftp_upload/53788/53788fig1.jpg" /> 
Figure 1. The live specimen used in the study. The grass sample was imaged in its native soil in a 7&#34; tall, 1.5&#34; diameter plastic pot. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53788/53788fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/53788/53788fig2.jpg" /> 
Figure 2. The Segmentation Problem. Left: Top view of a horizontal slice of the sample showing organic matter (OM) components of similar gray level to that of the root. Right: 3D rendering of the data showing all components that are of similar grey level to the root. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53788/53788fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/53788/53788fig3.jpg" /> 
Figure 3. Starting point in RooTrak. Top left: top view slice of the volume data where seeding is started; Top right: augmented view of the slice are marked by a red square in the left figure, seed points are selected inside the pertinent root cross section; Bottom: The root saturated in red color is selected for segmentation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53788/53788fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/53788/53788fig4.jpg" /> 
Figure 4. Comparison of slices from RooTrak. Three representative pairs of top view slices from different &#34;heights&#34; showing the root segmented by RooTrak. (A-B): top slice and the corresponding slice of the segmented stem; (C-D): mid region slice and the corresponding slice of the segmented root; (E-F): bottom region slice and the corresponding slice of the segmented root. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53788/53788fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/53788/53788fig5.jpg" /> 
Figure 5. Isosurface of the root as captured from ImageJ. The root surface was approximated by a triangular mesh. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53788/53788fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/53788/53788fig6.jpg" /> 
Figure 6. The imeshJ interface. The graphical user interface of imeshJ showing the actual results of the calculation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53788/53788fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data at JoVE.com

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

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 ...

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Nanyang Technological University

Research

JoVE Journal

Environment

8.5K Views.  Environmental Molecular Sciences Laboratory. A methodology for obtaining visual and quantitative root structure information from X-ray computed tomography data acquired in-soil is presented.

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

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Advanced micrometeorological methods have become increasingly important in soil, crop, and environmental sciences. For many scientists without formal training in atmospheric science, these techniques are relatively inaccessible. Surface renewal and other flux measurement methods require an understanding of boundary layer meteorology and extensive training in instrumentation and multiple data management programs. To improve accessibility of these techniques, we describe the underlying theory of surface renewal measurements, demonstrate how to set up a field station for surface renewal with eddy covariance calibration, and utilize our open-source turnkey data logger program to perform flux data acquisition and processing. The new turnkey program returns to the user a simple data table with the corrected fluxes and quality control parameters, and eliminates the need for researchers to shuttle between multiple processing programs to obtain the final flux data. An example of data generated from these measurements demonstrates how crop water use is measured with this technique. The output information is useful to growers for making irrigation decisions in a variety of agricultural ecosystems. These stations are currently deployed in numerous field experiments by researchers in our group and the California Department of Water Resources in the following crops: rice, wine and raisin grape vineyards, alfalfa, almond, walnut, peach, lemon, avocado, and corn.

Surface renewal is a micrometeorological method that is being used increasingly to determine energy fluxes, but its technical complexity makes it inaccessible to a broad audience. We describe the steps needed to set up and calibrate a surface renewal field station, to acquire and process data, and to correctly interpret results.

Advanced micrometeorological methods have become  increasingly important in soil, crop, and environmental sciences.  For many scientists without formal ...

A Small Volume Procedure for Viral Concentration from Water

Small-scale concentration of viruses (sample volumes 1-10 L, here simulated with spiked 100 ml water samples) is an efficient, cost-effective way to identify optimal parameters for virus concentration. Viruses can be concentrated from water using filtration (electropositive, electronegative, glass wool or size exclusion), followed by secondary concentration with beef extract to release viruses from filter surfaces, and finally tertiary concentration resulting in a 5-30 ml volume virus concentrate. In order to identify optimal concentration procedures, two different electropositive filters were evaluated (a glass/cellulose filter [1MDS] and a nano-alumina/glass filter [NanoCeram]), as well as different secondary concentration techniques; the celite technique where three different celite particle sizes were evaluated (fine, medium and large) followed by comparing this technique with that of the established organic flocculation method. Various elution additives were also evaluated for their ability to enhance the release of adenovirus (AdV) particles from filter surfaces. Fine particle celite recovered similar levels of AdV40 and 41 to that of the established organic flocculation method when viral spikes were added during secondary concentration. The glass/cellulose filter recovered higher levels of both, AdV40 and 41, compared to that of a nano-alumina/glass fiber filter. Although not statistically significant, the addition of 0.1% sodium polyphosphate amended beef extract eluant recovered 10% more AdV particles compared to unamended beef extract.

An approach was developed for identifying optimal viral concentration conditions for small volume water samples using spikes of human adenovirus. The techniques described here are used to identify concentration parameters for other viral targets, and applied to large-scale viral concentration experimentation.

Small-scale concentration of viruses (sample volumes 1-10 L, here simulated with spiked 100 ml water samples) is an efficient, cost-effective way to ...

Use of Chironomidae (Diptera) Surface-Floating Pupal Exuviae as a Rapid Bioassessment Protocol for Water Bodies

Rapid bioassessment protocols using benthic macroinvertebrate assemblages have been successfully used to assess human impacts on water quality. Unfortunately, traditional benthic larval sampling methods, such as the dip-net, can be time-consuming and expensive. An alternative protocol involves collection of Chironomidae surface-floating pupal exuviae (SFPE). Chironomidae is a species-rich family of flies (Diptera) whose immature stages typically occur in aquatic habitats. Adult chironomids emerge from the water, leaving their pupal skins, or exuviae, floating on the water&#8217;s surface. Exuviae often accumulate along banks or behind obstructions by action of the wind or water current, where they can be collected to assess chironomid diversity and richness. Chironomids can be used as important biological indicators, since some species are more tolerant to pollution than others. Therefore, the relative abundance and species composition of collected SFPE reflect changes in water quality. Here, methods associated with field collection, laboratory processing, slide mounting, and identification of chironomid SFPE are described in detail. Advantages of the SFPE method include minimal disturbance at a sampling area, efficient and economical sample collection and laboratory processing, ease of identification, applicability in nearly all aquatic environments, and a potentially more sensitive measure of ecosystem stress. Limitations include the inability to determine larval microhabitat use and inability to identify pupal exuviae to species if they have not been associated with adult males.

Use of Chironomidae Diptera Surface-Floating Pupal Exuviae as a Rapid Bioassessment Protocol for Water Bodies

Rapid bioassessment protocols using benthic macroinvertebrates are often used to monitor and assess water quality. An efficient protocol involves collections of Chironomidae surface-floating pupal exuviae (SFPE). Here, techniques for field collection, laboratory processing, slide mounting, and identification of Chironomidae SFPE are described.

Rapid bioassessment protocols using benthic macroinvertebrate assemblages have been successfully used to assess human impacts on water quality. ...

Soil Lysimeter Excavation for Coupled Hydrological, Geochemical, and Microbiological Investigations

Studying co-evolution of hydrological and biogeochemical processes in the subsurface of natural landscapes can enhance the understanding of coupled Earth-system processes. Such knowledge is imperative in improving predictions of hydro-biogeochemical cycles, especially under climate change scenarios. We present an experimental method, designed to capture sub-surface heterogeneity of an initially homogeneous soil system. This method is based on destructive sampling of a soil lysimeter designed to simulate a small-scale hillslope. A weighing lysimeter of one cubic meter capacity was divided into sections (voxels) and was excavated layer-by-layer, with sub samples being collected from each voxel. The excavation procedure was aimed at detecting the incipient heterogeneity of the system by focusing on the spatial assessment of hydrological, geochemical, and microbiological properties of the soil. Representative results of a few physicochemical variables tested show the development of heterogeneity. Additional work to test interactions between hydrological, geochemical, and microbiological signatures is planned to interpret the observed patterns. Our study also demonstrates the possibility of carrying out similar excavations in order to observe and quantify different aspects of soil-development under varying environmental conditions and scale.

This study presents an excavation method for investigating subsurface hydrological, geochemical, and microbiological heterogeneity of a soil lysimeter. The lysimeter simulates an artificial hillslope which was initially under homogeneous condition and had been subjected to approximately 5,000 mm of water over eight cycles of irrigation in an 18-month period.

Studying co-evolution of hydrological and biogeochemical processes in the subsurface of natural landscapes can enhance the understanding of coupled ...

Modification and Application of a Leaf Blower-vac for Field Sampling of Arthropods

Rice fields host a large diversity of arthropods, but investigating their population dynamics and interactions is challenging. Here we describe the modification and application of a leaf blower-vac for suction sampling of arthropod populations in rice. When used in combination with an enclosure, application of this sampling device provides absolute estimates of the populations of arthropods as numbers per standardized sampling area. The sampling efficiency depends critically on the sampling duration. In a mature rice crop, a two-minute sampling in an enclosure of 0.13 m2 yields more than 90% of the arthropod population. The device also allows sampling of arthropods dwelling on the water surface or the soil in rice paddies, but it is not suitable for sampling fast flying insects, such as predatory Odonata or larger hymenopterous parasitoids. The modified blower-vac is simple to construct, and cheaper and easier to handle than traditional suction sampling devices, such as D-vac. The low cost makes the modified blower-vac also accessible to researchers in developing countries.

Assessment of arthropod abundance in crops is critical for investigating population dynamics and species interactions. Here we describe the modification and application of a leaf blower-vac for suction sampling of arthropods in rice.

Rice fields host a large diversity of arthropods, but investigating their population dynamics and interactions is challenging. Here we describe the ...

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

Microplastic pollution in the marine environment is a scientific topic that has received increasing attention over the last decade. The majority of scientific publications address microplastic pollution of the sea surface. The protocol below describes the methodology for sampling, sample preparation, separation and chemical identification of microplastic particles. A manta net fixed on an &#187;A frame&#171; attached to the side of the vessel was used for sampling. Microplastic particles caught in the cod end of the net were separated from samples by visual identification and use of stereomicroscopes. Particles were analyzed for their size using an image analysis program and for their chemical structure using ATR-FTIR and micro FTIR spectroscopy. The described protocol is in line with recommendations for microplastics monitoring published by the Marine Strategy Framework Directive (MSFD) Technical Subgroup on Marine Litter. This written protocol with video guide will support the work of researchers that deal with microplastics monitoring all over the world.

The protocol below describes the methodology for: microplastics sampling on the sea surface, separation of microplastic and chemical identification of particles. This protocol is in line with the recommendations for microplastics monitoring published by the MSFD Technical Subgroup on Marine Litter.

Microplastic pollution in the marine environment is a scientific topic that has received increasing attention over the last decade. The majority of ...

Vegetated Treatment Systems for Removing Contaminants Associated with Surface Water Toxicity in Agriculture and Urban Runoff

Urban stormwater and agriculture irrigation runoff contain a complex mixture of contaminants that are often toxic to adjacent receiving waters. Runoff may be treated with simple systems designed to promote sorption of contaminants to vegetation and soils and promote infiltration. Two example systems are described: a bioswale treatment system for urban stormwater treatment, and a vegetated drainage ditch for treating agriculture irrigation runoff. Both have similar attributes that reduce contaminant loading in runoff: vegetation that results in sorption of the contaminants to the soil and plant surfaces, and water infiltration. These systems may also include the integration of granulated activated carbon as a polishing step to remove residual contaminants. Implementation of these systems in agriculture and urban watersheds requires system monitoring to verify treatment efficacy. This includes chemical monitoring for specific contaminants responsible for toxicity. The current paper emphasizes monitoring of current use pesticides since these are responsible for surface water toxicity to aquatic invertebrates.

This article summarizes the design attributes and the effectiveness of treatment systems that treat urban stormwater and agriculture irrigation runoff to remove pesticides and other contaminants associated with aquatic toxicity.

Urban stormwater and agriculture irrigation runoff contain a complex mixture of contaminants that are often toxic to adjacent receiving waters. Runoff may ...

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 ...

Stress Distribution During Cold Compression of Rocks and Mineral Aggregates Using Synchrotron-based X-Ray Diffraction

We report detailed procedures for performing compression experiments on rocks and mineral aggregates within a multi-anvil deformation apparatus (D-DIA) coupled with synchrotron X-radiation. A cube-shaped sample assembly is prepared and compressed, at room temperature, by a set of four X-ray transparent sintered diamond anvils and two tungsten carbide anvils, in the lateral and the vertical planes, respectively. All six anvils are housed within a 250-ton hydraulic press and driven inward simultaneously by two wedged guide blocks. A horizontal energy dispersive X-ray beam is projected through and diffracted by the sample assembly. The beam is commonly in the mode of either white or monochromatic X-ray. In the case of white X-ray, the diffracted X-rays are detected by a solid-state detector array that collects the resulting energy dispersive diffraction pattern. In the case of monochromatic X-ray, the diffracted pattern is recorded using a two-dimensional (2-D) detector, such as an imaging plate or a charge-coupled device (CCD) detector. The 2-D diffraction patterns are analyzed to derive lattice spacings. The elastic strains of the sample are derived from the atomic lattice spacing within grains. The stress is then calculated using the predetermined elastic modulus and the elastic strain. Furthermore, the stress distribution in two-dimensions allow for understanding how stress is distributed in different orientations. In addition, a scintillator in the X-ray path yields a visible light image of the sample environment, which allows for the precise measurement of sample length changes during the experiment, yielding a direct measurement of volume strain on the sample. This type of experiment can quantify the stress distribution within geomaterials, which can ultimately shed light on the mechanism responsible for compaction. Such knowledge has the potential to significantly improve our understanding of key processes in rock mechanics, geotechnical engineering, mineral physics, and material science applications where compactive processes are important.

We report detailed procedures for compression experiments on rocks and mineral aggregates within a multi-anvil deformation apparatus coupled with synchrotron X-radiation. Such experiments allow quantification of the stress distribution within samples, that ultimately sheds light on compaction processes in geomaterials.

We report detailed procedures for performing compression experiments on rocks and mineral aggregates within a multi-anvil deformation apparatus (D-DIA) ...

Isolation and Analysis of Microbial Communities in Soil, Rhizosphere, and Roots in Perennial Grass Experiments

Plant and soil microbiome studies are becoming increasingly important for understanding the roles microorganisms play in agricultural productivity. The purpose of this manuscript is to provide detail on how to rapidly sample soil, rhizosphere, and endosphere of replicated field trials and analyze changes that may occur in the microbial communities due to sample type, treatment, and plant genotype. The experiment used to demonstrate these methods consists of replicated field plots containing two, pure, warm-season grasses (Panicum virgatum and Andropogon gerardii) and a low-diversity grass mixture (A. gerardii, Sorghastrum nutans, and Bouteloua curtipendula). Briefly, plants are excavated, a variety of roots are cut and placed in phosphate buffer, and then shaken to collect the rhizosphere. Roots are brought to the laboratory on ice and surface sterilized with bleach and ethanol (EtOH). The rhizosphere is filtered and concentrated by centrifugation. Excavated soil from around the root ball is placed into plastic bags and brought to the lab where a small amount of soil is taken for DNA extractions. DNA is extracted from roots, soil, and rhizosphere and then amplified with primers for the V4 region of the 16S rRNA gene. Amplicons are sequenced, then analyzed with open access bioinformatics tools. These methods allow researchers to test how the microbial community diversity and composition varies due to sample type, treatment, and plant genotype. Using these methods along with statistical models, the representative results demonstrate there are significant differences in microbial communities of roots, rhizosphere, and soil. Methods presented here provide a complete set of steps for how to collect field samples, isolate, extract, quantify, amplify, and sequence DNA, and analyze microbial community diversity and composition in replicated field trials.

Excavation of plant roots from the field as well as processing of samples into endosphere, rhizosphere, and soil are described in detail, including DNA extraction and data analysis methods. This paper is designed to enable other laboratories to use these techniques for the study of soil, endosphere, and rhizosphere microbiomes.

Plant and soil microbiome studies are becoming increasingly important for understanding the roles microorganisms play in agricultural productivity. The ...