This work was supported by a grant from the National Science Foundation CBET&#160;1336544.
The FIJI, R, and Python logos are used with the in accordance with the following trademark policies: 
Python: <a href="https://www.python.org/psf/trademarks/">https://www.python.org/psf/trademarks/</a> 
R: <a href="https://www.r-project.org/logo/">https://www.r-project.org/Logo/</a> , as per the CC-BY-SA 4.0 license listed at: <a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/Licenses/by-sa/4.0/</a> 
Fiji: <a href="https://imagej.net/Licensing">https://imagej.net/Licensing</a>

1. Collect samples for particle analysis
<ol>
	<li>Determine the sample volume for specific reactors that will produce sufficient particles for statistical analysis10 (&#62;500) while avoiding particle overlap.
	<ol>
		<li>Assume that a range of 0.5 to 2 mL per sample of mixed liquor is sufficient for activated sludge samples with a mixed liquor suspended solids (MLSS) between 250 and 5,000 mg/L.</li>
		<li>Otherwise, prepare three test agar plates using 0.5, 2, and 5 mL of sample (steps 1.2 through 2.7).</li>
		<li>Visually estimate which (if any) sample volumes best meet the criteria listed in step 1.1.</li>
		<li>If particles still overlap for the 0.5 mL sample, repeat steps 1.1.2 and 1.1.3 with three 0.5 mL samples diluted with an added 0.5, 1, and 2 mL of phosphate buffered saline to determine the degree to which a 0.5 mL sample must be diluted prior to step 2.1. 
		NOTE: Steps 1.1 &#8722; 1.1.4 need only be performed once per experiment, or if the reactor contents change such that subsequent measurements no longer meet the criteria listed in step 1.1.</li>
	</ol>
	</li>
	<li>Acquire a representative sample from a well-mixed portion of the reactor by grabbing ~40 mL in a beaker or 50 mL centrifuge tube, gently mixing, and immediately pouring the determined sample volume of the well-mixed grab into a 15 mL centrifuge tube. Dilute the sample, necessary, as determined by step 1.1.4. 
	NOTE: The protocol can be paused here and the sample may be stored under refrigeration (4 &#176;C) for up to 48 hours. Do not freeze the sample. 
	CAUTION: Common preservation media (e.g., formaldehyde/formamide) are not suitable. The large surface area of the plate, in combination with the open container, heat from the light source, and potentially poorly ventilated microscopy setup produce unnecessarily hazardous conditions for little gain in image quality.</li>
</ol>
2. Prepare agar plates of stained, immobilized particles
<ol>
	<li>Add 5 &#181;L of 1% (w/v) methylene blue to each sample, then cap and gently invert at 3 least times to mix. Allow samples to stain for at least 5 but no more than 30 minutes at room temperature.</li>
	<li>Prepare approximately 10 mL per sample of 7.5% (w/v) agar in deionized water. 
	NOTE: Agar may be produced ahead of time and stored indefinitely if sterilized. Agarose may be substituted, but does not substantially improve images.</li>
	<li>Melt agar using a microwave or water bath and allow to cool slightly before use. Ensure the agar is completely melted and pours easily. Solid globules of agar will stain differently, producing poor quality images.</li>
	<li>Transfer sufficient melted 7.5% (w/v) agar to the centrifuge tube to bring the total tube volume to between 6.5 and 9 mL.</li>
	<li>Recap centrifuge tubes and gently invert at least 3 times to mix.</li>
	<li>While pointing the cap away from oneself or in a hood, open the cap. Pour the tube contents into a 100 mm plastic Petri dish while gently rocking the dish to achieve a full, smooth coating and a visually uniform distribution of particles. 
	CAUTION: The heat from the agar may produce a slight overpressure in the tube. This often produces an audible hiss and has the potential to expel small droplets of hot agar.</li>
	<li>Allow the plates to cool at room temperature for at least 5 minutes, until the agar solidifies. 
	NOTE: The protocol may be paused here. Store plated inverted and sealed (e.g., in a sealing plastic bag or with paraffin film) for up to 48 hours under refrigeration (4 &#176;C).</li>
</ol>
3. Acquire particle images using a stereomicroscope and digital camera
<ol>
	<li>Place the uncovered plate face up on the microscope stage of a stereomicroscope capable of 10x to 20x magnification. Illuminate the sample from below with even, diffuse light using equipment such as an LED illuminator stand or light plate.</li>
	<li>Open the image capture software, ensure the microscope light path is set to Photo, and click on the appropriate camera from the camera list.</li>
	<li>Adjust the microscope so that multiple particles appear in the software in the focal plane with large, well-defined edges. Use a magnification of 10-20x to measure particles while maintaining a relatively deep focal plane.
	<ol>
		<li>Temporarily remove the agar plate and place the micrometer on the stage. Adjust the fine focus until the graduations on the micrometer appear sharply focused in the image capture software.</li>
		<li>If not previously calibrated, record the pixel to micro ratio for the current magnification.
		<ol>
			<li>Set the zoom to 100% by clicking Zoom &#62; Actual Size and select Options &#62; Calibrate then align the red calibration bar in the main viewport along the long axis of the micrometer, with the vertical bars centered on the 0 and 200 &#181;m graduations. In the Calibrate dialog box, enter the current magnification level and actual length of 200 &#181;m.</li>
		</ol>
		</li>
		<li>If already calibrated select Magnification from the menu bar, and the select the current magnification level and confirm the calibration.
		<ol>
			<li>Select Measurements &#62; Line &#62; Arbitrary Line. Click on the intersection of the 0 graduation and long axis of the micrometer. Click again on the intersection at 200 and the long the axis. A correct calibration should display approximately 200 &#181;m. Delete the line by clicking on it, pressing delete, and pressing Yes on the confirmation box. 
			NOTE: The instructions given for selecting the camera and calibration are specific to the software used for this hardware. Similar functions should be available in other imaging software. The goal is to determine the pixel to micron ratio of the image for accurate particle size measurement.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Replace the agar plate and adjust the fine focus to achieve maximum detail in the imaging software.</li>
	<li>Adjust the imaging software so that maximum image quality is achieved.
	<ol>
		<li>Increase the bit depth to the maximum value allowed, by selecting the radio button in the Bit Depth panel of the Camera sidebar. Set the software to acquire grayscale images by selecting the appropriate radio button in the Color/Gray panel of the Camera sidebar.</li>
		<li>Collapse any open sidebar panels between exposure and histogram. Reduce the gain to 1.0 and increase the exposure until a clear image appears in the viewport and until the histogram appears as a distribution that is not clipped by either end of the histogram box.</li>
		<li>Adjust the histogram to avoid over and underexposure. In the histogram panel of the camera side bar, slide the left boundary of the histogram to just outside the lowest values and the right boundary to just outside the highest values.</li>
	</ol>
	</li>
	<li>Save the image as an uncompressed TIFF, including magnification information in the image metadata, using the File &#62; Save as dialog box, selecting the TIFF format, and ensuring that the Save with calibration information box is checked. 
	NOTE: Saving image metadata, including spatial calibration, may vary between acquisition programs. FIJI15, the underlying software used by the pipeline, understands most common variants. The important information to record is the pixel height, width, and associated unit(s).</li>
	<li>Using either a mobile stage or manually moving the plate itself, select another area, which does not overlap previous images, following a path which alternates between left-to-right and right-to-left as one moves down the plate; also known as a &#8216;lawnmower search pattern&#8217;. Repeat step 3.6 until sufficient images are produced to capture at least 500 visually estimated particles, more are better. 
	NOTE: Alternatives patterns (e.g., circular, random) are acceptable but should be reported. Acquiring multiple overlapping images for combination into a mosaic via digital stitching produces a resulting file size which greatly hinders downstream processing and artifacts from stitching may be introduced and is not currently recommended.</li>
	<li>Retain plates until after image analysis for potential follow-up imaging. After final imaging, discard as appropriate for biological waste.</li>
</ol>
4. Measure and analyze particle silhouettes
<ol>
	<li>Install the required image analysis software packages
	<ol>
		<li>Install FIJI (an enhanced version of the National Institutes of Health&#8217;s ImageJ v1.52e following the instructions at: https://imagej.net/Fiji/Downloads</li>
		<li>Install git, if not already present, by following the instructions at: https://git-scm.com/downloads</li>
		<li>Acquire the particle analysis code from by cloning the git repository16.
		<ol>
			<li>At the command line, retrieve the latest version of the code by typing: 
			git clone https://github.com/joeweaver/SParMorIA-Sludge-Particle-Morphological-Image-Analysis.git
			<ol>
				<li>Install the analysis code following the instructions in the README.md text file found in the top-level directory of the cloned repository. 
				NOTE: Using git is preferred, as it will automatically retrieve the latest version of the code. If git is not available, it is also possible to download the code as a zip file on the release page at: https://github.com/joeweaver/SParMorIA-Sludge-Particle-Morphological-Image-Analysis/releases</li>
			</ol>
			</li>
		</ol>
		</li>
		<li>Edit a text file listing the directories to be processed, along with optional parameters. Refer to the examples/analysis subdirectory for a list of parameters and examples.</li>
	</ol>
	</li>
	<li>Run the analysis on the command line by typing: 
	&#60;FIJI-PATH&#62;\ImageJ-win64.exe --console -macro SParMorIA-SludgeParticle_Morphological_Image_Analysis &#60;paramsfile&#62; 
	where &#60;FIJI-path&#62; is the directory in which ImageJ-win64.exe is located and &#60;paramsfile&#62; the location of the text file describing the analysis setup 
	NOTE: The name of the executable may differ, depending on which operating system FIJI is installed on. Depending on the number and size of images, the analysis may take a few minutes to hours and will run automatically.</li>
	<li>Perform a quality control check
	<ol>
		<li>Examine the quality control files located in the overlay subdirectory of the specified output directory. Note images with spurious, missed, and poorly captured particles, all apparent as shaded outlines which do not match the background. Refer to Figure 3 for examples of such. The particle data are now ready for experiment-specific analysis figure generation.</li>
	</ol>
	</li>
	<li>Reject either whole plates or individual particles by specifying in the analysis code the noted files and/or particle IDs to be ignored. Refer to examples/censoring in the repository for relevant R and Python code.</li>
	<li>Generate experiment specific figures using the image analysis results for each image are stored in a TIDY12 comma separated text file in the results subdirectory of the specified output directory. Refer to the examples/figures/R and examples/figures/Python subdirectories for examples of how to read the results files.</li>
</ol>

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Effect of Hydrodynamics on Aerobic Granulation Available from: <a target="_blank" href="https://repository.lib.ncsu.edu/handle/1840.16/8761">https://repository.lib.ncsu.edu/handle/1840.16/8761</a> (2012)</li><li>Tay, J. H., Liu, Q. S., Liu, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microscopic+observation+of+aerobic+granulation+in+sequential+aerobic+sludge+blanket+reactor.">Microscopic observation of aerobic granulation in sequential aerobic sludge blanket reactor.</a> Journal of Applied Microbiology. 91 (1), 168-175 (2001).</li><li>Kelly, R. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Graphical+Comparison+of+Image+Analysis+and+Laser+Diffraction+Particle+Size+Analysis+Data+Obtained+From+the+Measurements+of+Nonspherical+Particle+Systems.">Graphical Comparison of Image Analysis and Laser Diffraction Particle Size Analysis Data Obtained From the Measurements of Nonspherical Particle Systems.</a> AAPS Pharm SciTech. 7 (3), E1-E14 (2006).</li><li>Walisko, 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=The+Taming+of+the+Shrew+-Controlling+the+Morphology+of+Filamentous+Eukaryotic+and+Prokaryotic+Microorganisms.">The Taming of the Shrew -Controlling the Morphology of Filamentous Eukaryotic and Prokaryotic Microorganisms.</a> Advances in Biochemical Engineering/Biotechnology. 149, 1-27 (2015).</li><li>Campbell, R. A. A., Eifert, R. W., Turner, G. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Openstage:+A+Low-Cost+Motorized+Microscope+Stage+with+Sub-Micron+Positioning+Accuracy.">Openstage: A Low-Cost Motorized Microscope Stage with Sub-Micron Positioning Accuracy.</a> PLoS ONE. 9 (2), e88977 (2014).</li><li>Dias, P. A., 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=Image+processing+for+identification+and+quantification+of+filamentous+bacteria+in+in+situ+acquired+images.">Image processing for identification and quantification of filamentous bacteria in in situ acquired images.</a> BioMedical Engineering OnLine. 15 (1), 64 (2016).</li><li>Liao, J., Lou, I., de los Reyes, F. 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W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Avoiding+twisted+pixels:+ethical+guidelines+for+the+appropriate+use+and+manipulation+of+scientific+digital+images.">Avoiding twisted pixels: ethical guidelines for the appropriate use and manipulation of scientific digital images.</a> Science and Engineering Ethics. 16 (4), 639-667 (2010).</li><li>Wickham, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tidy+Data.">Tidy Data.</a> Journal of Statistical Software. 59 (10), (2014).</li><li>van Rossum, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Python tutorial. , (1995).</li><li>Schindelin, J., Arganda-Carreras, I., Frise, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FIJI:+an+open-source+platform+for+biological-image+analysis.">FIJI: an open-source platform for biological-image analysis.</a> Nature Methods. 9 (7), 676-682 (2012).</li><li>. SParMorIA: Sludge Particle Morphological ImageAnalysis Available from: <a target="_blank" href="https://github.com/joeweaver/SParMorIA-Sludge-Particle-Morphological-Image-Analysis">https://github.com/joeweaver/SParMorIA-Sludge-Particle-Morphological-Image-Analysis</a> (2018)</li><li>Ferreira, T., Rasband, 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> ImageJ User Guide: IJ 1.42 r. , (2012).</li><li>Sezgin, M., Sankur, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Survey+over+image+thresholding+techniques+and+quantitative+performance+evaluation.">Survey over image thresholding techniques and quantitative performance evaluation.</a> Journal of Electronic Imaging. 13 (1), 146-166 (2004).</li><li>Kr&#246;ner, S., Dom&#233;nech Carb&#243;, M. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Determination+of+minimum+pixel+resolution+for+shape+analysis:+Proposal+of+a+new+data+validation+method+for+computerized+images.">Determination of minimum pixel resolution for shape analysis: Proposal of a new data validation method for computerized images.</a> Powder Technology. 245, 297-313 (2013).</li><li>McKinney, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Data+Structures+for+Statistical+Computing+in+Python.">Data Structures for Statistical Computing in Python.</a> Proceedings of the 9th Python in Science Conference. , 51-56 (2010).</li><li>Waskom, M., 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=.">.</a> seaborn: v0.5.0 (November 2014). , (2014).</li><li>. dplyr: A Grammar of Data Manipulation Available from: <a target="_blank" href="https://cran.r-project.org/web/packages/dplyr/index.html">https://cran.r-project.org/web/packages/dplyr/index.html</a> (2016)</li><li>Riley, R. S., Ben-Ezra, J. M., Massey, D., Slyter, R. L., Romagnoli, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Digital+Photography:+A+Primer+for+Pathologists.">Digital Photography: A Primer for Pathologists.</a> Journal of Clinical Laboratory Analysis. 18 (2), 91-128 (2004).</li><li>Singh, S., Bray, M. A., Jones, T. R., Carpenter, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pipeline+for+illumination+correction+of+images+for+high-throughput+microscopy.">Pipeline for illumination correction of images for high-throughput microscopy.</a> Journal of Microscopy. 256 (3), 231-236 (2014).</li><li>Eastwood, B. S., Childs, E. 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The authors have nothing to disclose.

Although the image analysis system is fairly robust and QC steps are taken to ensure poor images are removed, proper attention to specific issues in sampling, plate preparation, and image acquisition can improve both the accuracy of the data and the proportion of images passing QC.
Sampling concentration 
Assuming a representative sample has been taken, the most important step is to ensure that sufficient particles are present for representative9 a...

The purpose of this method is to provide a well-defined, repeatable, and partially-automated method for determining size and shape distributions of particles in bioreactors, particularly those containing activated sludge flocs and aerobic granules1,2. The rationale behind this method were to enhance the affordability, simplicity, throughput, and repeatability of our existing in-house protocols3,4, ease particle measurement for others, and facilitate sharing and comparison of data.
There are two broad categories of particle measurement analysis - direct imaging and inferential methods using such qualities as light scattering5. Although inferential methods can be automated and have large throughput, the equipment is expensive. In addition, while inferential methods can accurately determine the equivalent size of a particle6, they do not provide detailed shape information7.
Because of the need for shape data, we have based our method on direct imaging. While some high-throughput imaging methods exist, they have traditionally required either expensive commercial hardware or custom built solutions8,9. Our method has been developed to employ common, affordable hardware and software that, although suffering from a reduction in throughput, produces far more particle images than the minimum needed for many analyses10.
Existing protocols may not specify important sampling and image acquisition steps. Other protocols may specify manual steps that introduce subjective bias (such as ad hoc thresholding11). A well-defined method that specifies sampling, immobilization and image acquisition steps combined with freely available analysis software will enhance both within-lab image analysis and comparisons between labs. A major goal of this protocol is to provide a workflow and tools that should lead to reproducible results from different labs for the same sample.
Apart from normalizing the image analysis process, the data produced by this pipeline is recorded in a well-defined, well-formatted file12 suitable for use by popular data analysis packages13,14, easing experiment specific analyses (such as custom figure generation) and facilitating data sharing between labs.
This protocol is especially suggested for researchers who require particle shape data, do not have access to inferential methods, do not wish to develop their own image analysis pipeline, and wish to share their data easily with others

<table>
	<tbody>
		<tr>
			<td>10% Bleach solution</td>
			<td>Chlorox</td>
			<td>31009</td>
			<td>For workspace disinfection.</td>
		</tr>
		<tr>
			<td>15 mL centrifuge tube with cap</td>
			<td>Corning</td>
			<td>430790</td>
			<td>Per sample.</td>
		</tr>
		<tr>
			<td>50 mL Erlenmeyer flask</td>
			<td>Corning</td>
			<td>4980-50</td>
			<td>Other vessels are suitable so long as they can contain &#62; 40 mL of sample and allow mixing</td>
		</tr>
		<tr>
			<td>500 mL Kimax Bottle</td>
			<td>Kimble-Chase</td>
			<td>14395-50</td>
			<td>Or otherwise sufficient for agar handling</td>
		</tr>
		<tr>
			<td>Agar</td>
			<td>BD</td>
			<td>214010</td>
			<td>Solid, to prepare 7.5% gel. 7 mL per sample.</td>
		</tr>
		<tr>
			<td>Data analysis software</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>R or Python are suggested</td>
		</tr>
		<tr>
			<td>Deionized water</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Sufficient to prepare stain and agar. If unavailable, tap should be fine.</td>
		</tr>
		<tr>
			<td>Desktop computer</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Image analysis is not CPU intensive, any &#39;ordinary&#39; desktop computer circa 2017 should be sufficient.</td>
		</tr>
		<tr>
			<td>External hard drive</td>
			<td>Seagate</td>
			<td>STEB5000100</td>
			<td>Not fully required, but extremely useful given the number an size of images. 2 or more TB of storage suggested.</td>
		</tr>
		<tr>
			<td>FIJI</td>
			<td>NIH</td>
			<td>version 1.51d</td>
			<td>Version is ImageJ core. Plugins are updated as of writing. Available at: https://imagej.net/Fiji/Downloads</td>
		</tr>
		<tr>
			<td>GIT</td>
			<td>Open Source</td>
			<td>version 2.19.1 or later</td>
			<td>Available at: https://git-scm.com/</td>
		</tr>
		<tr>
			<td>Image capture software</td>
			<td>ToupView</td>
			<td>version 3.7.5177</td>
			<td>Any compatible with camera, may come with camera. Should allow saving TIFF images with spatial calibration data.</td>
		</tr>
		<tr>
			<td>Mechanical (X/Y) Stage</td>
			<td>OMAX</td>
			<td>A512</td>
			<td>Not fully required, but greatly aids image acquisition.</td>
		</tr>
		<tr>
			<td>Methylene blue</td>
			<td>Fisher</td>
			<td>M291-100</td>
			<td>Solid, to prepare 1% w/v solution. 5 uL solution per sample.</td>
		</tr>
		<tr>
			<td>Microscope camera</td>
			<td>OMAX</td>
			<td>A35140U</td>
			<td>Any digitial camera compatible with microscope. Resolution providing at least 5 um per pixel at 10x magnification and a dynamic range of at least 8 bits per pixel per color channel is suggested.</td>
		</tr>
		<tr>
			<td>Optical Stage Micrometer</td>
			<td>OMAX</td>
			<td>A36CALM1</td>
			<td>Or otherwise sufficient for spatial calibration.</td>
		</tr>
		<tr>
			<td>Petri dish, 100 mm</td>
			<td>Fisher</td>
			<td>FB0875712</td>
			<td>1 per sample.</td>
		</tr>
		<tr>
			<td>PPE</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Standard lab coat, gloves, and eyewear.</td>
		</tr>
		<tr>
			<td>Sparmoria macro</td>
			<td>NCSU</td>
			<td>version 0.2.1</td>
			<td>Available at github repository : https://github.com/joeweaver/SParMorIA-Sludge-Particle-Morphological-Image-Analysis</td>
		</tr>
		<tr>
			<td>Stereo/dissecting microscope</td>
			<td>Nikon</td>
			<td>SMZ-2T</td>
			<td>Should provide 10 to 20x magnficiation and allow digital photos either with a buit-in camera or profide a mounting point for a CCD.</td>
		</tr>
	</tbody>
</table>

measuring the shape and size of activated sludge particles immobilized in agar with an open source software pipeline

Experimental bioreactors, such as those treating wastewater, contain particles whose size and shape are important parameters. For example, the size and shape of activated sludge flocs can indicate the conditions at the microscale, and also directly affect how well the sludge settles in a clarifier.
Particle size and shape are both misleadingly &#39;simple&#39; measurements. Many subtle issues, often unaddressed in informal protocols, can arise when sampling, imaging, and analyzing particles. Sampling methods may be biased or not provide enough statistical power. The samples themselves may be poorly preserved or undergo alteration during immobilization. Images may not be of sufficient quality; overlapping particles, depth of field, magnification level, and various noise can all produce poor results. Poorly specified analysis can introduce bias, such as that produced by manual image thresholding and segmentation.
Affordability and throughput are desirable alongside reproducibility. An affordable, high throughput method can enable more frequent particle measurement, producing many images containing thousands of particles. A method that uses inexpensive reagents, a common dissecting microscope, and freely-available open source analysis software allows repeatable, accessible, reproducible, and partially-automated experimental results. Further, the product of such a method can be well-formatted, well-defined, and easily understood by data analysis software, easing both within-lab analyses and data sharing between labs.
We present a protocol that details the steps needed to produce such a product, including: sampling, sample preparation and immobilization in agar, digital image acquisition, digital image analysis, and examples of experiment-specific figure generation from the analysis results. We have also included an open-source data analysis pipeline to support this protocol.

Files Generated 
The process illustrated in Figure 1 will produce two files per image analyzed. The first file is a comma separated value (CSV) text file where each row corresponds to an individual particle and the columns describe various particle metrics such as area, circularity, and solidity and defined in the ImageJ manual17. Example CSV files are included as supplemental information and in the examples/data ...

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Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

The size and shape of particles in activated sludge are important parameters that are measured using varying methods. Inaccuracies arise from non-representative sampling, suboptimal images, and subjective analysis parameters. To minimize these errors and ease measurement, we present a protocol specifying every step, including an open source software pipeline.

Experimental bioreactors, such as those treating wastewater, contain particles whose size and shape are important parameters. For example, the size and ...

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JoVE Journal

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7.0K Views.  North Carolina State University. The size and shape of particles in activated sludge are important parameters that are measured using varying methods. Inaccuracies arise from non-representative sampling, suboptimal images, and subjective analysis parameters. To minimize these errors and ease measurement, we present a protocol specifying every step, including an open source software pipeline.

Video: Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Development of Sulfidogenic Sludge from Marine Sediments and Trichloroethylene Reduction in an Upflow Anaerobic Sludge Blanket Reactor

The importance of microbial sulfate reduction relies on the various applications that it offers in environmental biotechnology. Engineered sulfate reduction is used in industrial wastewater treatment to remove large concentrations of sulfate along with the chemical oxygen demand (COD) and heavy metals. The most common approach to the process is with anaerobic bioreactors in which sulfidogenic sludge is obtained through adaptation of predominantly methanogenic granular sludge to sulfidogenesis. This process may take a long time and does not always eliminate the competition for substrate due to the presence of methanogens in the sludge. In this work, we propose a novel approach to obtain sulfidogenic sludge in which hydrothermal vents sediments are the original source of microorganisms. The microbial community developed in the presence of sulfate and volatile fatty acids is wide enough to sustain sulfate reduction over a long period of time without exhibiting inhibition due to sulfide. 
This protocol describes the procedure to generate the sludge from the sediments in an upflow anaerobic sludge blanket (UASB) type of reactor. Furthermore, the protocol presents the procedure to demonstrate the capability of the sludge to remove by reductive dechlorination a model of a highly toxic organic pollutant such as trichloroethylene (TCE). The protocol is divided in three stages: (1) the formation of the sludge and the determination of its sulfate reducing activity in the UASB, (2) the experiment to remove the TCE by the sludge, and (3) the identification of microorganisms in the sludge after the TCE reduction. Although in this case the sediments were taken from a site located in Mexico, the generation of a sulfidogenic sludge by using this procedure may work if a different source of sediments is taken since marine sediments are a natural pool of microorganisms that may be enriched in sulfate reducing bacteria.

Microbial sulfate reduction is a process of great importance in environmental biotechnology. The success of the sulfidogenic reactors depends among other factors on the microbial composition of the sludge. Here, we present a protocol to develop sulfidogenic sludge from hydrothermal vents sediments in a UASB reactor for reductive dechlorination purposes.

The importance of microbial sulfate reduction relies on the various applications that it offers in environmental biotechnology. Engineered sulfate ...

Measuring Rates of Herbicide Metabolism in Dicot Weeds with an Excised Leaf Assay

In order to isolate and accurately determine rates of herbicide metabolism in an obligate-outcrossing dicot weed, waterhemp (Amaranthus tuberculatus), we developed an excised leaf assay combined with a vegetative cloning strategy to normalize herbicide uptake and remove translocation as contributing factors in herbicide-resistant (R) and &#8211;sensitive (S) waterhemp populations. Biokinetic analyses of organic pesticides in plants typically include the determination of uptake, translocation (delivery to the target site), metabolic fate, and interactions with the target site. Herbicide metabolism is an important parameter to measure in herbicide-resistant weeds and herbicide-tolerant crops, and is typically accomplished with whole-plant tests using radiolabeled herbicides. However, one difficulty with interpreting biokinetic parameters derived from whole-plant methods is that translocation is often affected by rates of herbicide metabolism, since polar metabolites are usually not mobile within the plant following herbicide detoxification reactions. Advantages of the protocol described in this manuscript include reproducible, accurate, and rapid determination of herbicide degradation rates in R and S populations, a substantial decrease in the amount of radiolabeled herbicide consumed, a large reduction in radiolabeled plant materials requiring further handling and disposal, and the ability to perform radiolabeled herbicide experiments in the lab or growth chamber instead of a greenhouse. As herbicide resistance continues to develop and spread in dicot weed populations worldwide, the excised leaf assay method developed and described herein will provide an invaluable technique for investigating non-target site-based resistance due to enhanced rates of herbicide metabolism and detoxification.

This manuscript describes how herbicide metabolism rates can be effectively quantified with excised leaves from a dicot weed, thereby reducing variability and removing any possible confounding effects of herbicide uptake or translocation typically observed in whole-plant assays.

In order to isolate and accurately determine rates of herbicide metabolism in an obligate-outcrossing dicot weed, waterhemp (Amaranthus tuberculatus), we ...

Spotting Cheetahs: Identifying Individuals by Their Footprints

The cheetah (Acinonyx jubatus) is Africa's most endangered large felid and listed as Vulnerable with a declining population trend by the IUCN1. It ranges widely over sub-Saharan Africa and in parts of the Middle East. Cheetah conservationists face two major challenges, conflict with landowners over the killing of domestic livestock, and concern over range contraction. Understanding of the latter remains particularly poor2. Namibia is believed to support the largest number of cheetahs of any range country, around 30%, but estimates range from 2,9053 to 13,5204. The disparity is likely a result of the different techniques used in monitoring.
Current techniques, including invasive tagging with VHF or satellite/GPS collars, can be costly and unreliable. The footprint identification technique5 is a new tool accessible to both field scientists and also citizens with smartphones, who could potentially augment data collection. The footprint identification technique analyzes digital images of footprints captured according to a standardized protocol. Images are optimized and measured in data visualization software. Measurements of distances, angles, and areas of the footprint images are analyzed using a robust cross-validated pairwise discriminant analysis based on a customized model. The final output is in the form of a Ward's cluster dendrogram. A user-friendly graphic user interface (GUI) allows the user immediate access and clear interpretation of classification results.
The footprint identification technique algorithms are species specific because each species has a unique anatomy. The technique runs in a data visualization software, using its own scripting language (jsl) that can be customized for the footprint anatomy of any species. An initial classification algorithm is built from a training database of footprints from that species, collected from individuals of known identity. An algorithm derived from a cheetah of known identity is then able to classify free-ranging cheetahs of unknown identity. The footprint identification technique predicts individual cheetah identity with an accuracy of &gt;90%.

The cheetah (Acinonyx jubatus) is an iconic, endangered species, but conservation efforts are challenged by habitat shrinkage and conflict with commercial farmers. The footprint identification technique, a robust, accurate and cost-effective image classification system, is a new approach to monitoring cheetahs.

The cheetah (Acinonyx jubatus) is Africa's most endangered large felid and listed as Vulnerable with a declining population trend by the IUCN1. It ranges ...

An Ultra-clean Multilayer Apparatus for Collecting Size Fractionated Marine Plankton and Suspended Particles

The distributions of many trace elements in the ocean are strongly associated with the growth, death, and re-mineralization of marine plankton and those of suspended/sinking particles. Here, we present an all plastic (Polypropylene and Polycarbonate), multi-layer filtration system for collection of suspended particulate matter (SPM) at sea. This ultra-clean sampling device has been designed and developed specifically for trace element studies. Meticulous selection of all non-metallic materials and utilization of an in-line flow-through procedure minimizes any possible metal contamination during sampling. This system has been successfully tested and tweaked for determining trace metals (e.g., Fe, Al, Mn, Cd, Cu, Ni) on particles of varying size in coastal and open ocean waters. Results from the South China Sea at the South East Asia Time-Series (SEATS) station indicate that diurnal variations and spatial distribution of plankton in the euphotic zone can be easily resolved and recognized. Chemical analysis of size-fractionated particles in surface waters of the Taiwan Strait suggests that the larger particles (&#62;153 &#181;m) were mostly biologically derived, while the smaller particles (10 - 63 &#181;m) were mostly composed of inorganic matter. Apart from Cd, the concentrations of metals (Fe, Al, Mn, Cu, Ni) decreased with increasing size.

Plankton and suspended particles play a major role in the biogeochemical cycles in the ocean. Here, we provide an ultra-clean, low stress method for the collection of various sizes of particles and plankton at sea with the capability of handling large volumes of seawater.

The distributions of many trace elements in the ocean are strongly associated with the growth, death, and re-mineralization of marine plankton and those ...

Measuring Hypopharyngeal Gland Acinus Size in Honey Bee (Apis mellifera) Workers

The nurse hypopharyngeal glands produce the protein fraction of the worker and royal jelly that is fed to developing larvae and queens. These paired glands that are located in the head of the bee are highly sensitive to the quantity and quality of pollen and pollen substitutes that the nurse bee consumes. The glands get smaller when nurses are fed deficient diets and are large when they are fed complete diets. Because nurse hypopharyngeal gland size is a robust indicator of nurse nutrition, it is essential that those studying honey bee nutrition know how to measure these glands. Here, we provide detailed methods for dissecting, staining, imaging, and measuring nurse bee hypopharyngeal glands. We present comparisons of unstained and stained tissue and data that were used to study the impact of pollen on gland size. This method has been used to test how diet impacts hypopharyngeal gland size but has further use for understanding the role of these glands in hive health.

Measuring Hypopharyngeal Gland Acinus Size in Honey Bee Apis mellifera Workers

Hypopharyngeal gland acinus size is a robust measure of nurse honey bee nutrition. Here, we provide a detailed protocol for dissecting, staining, imaging, and measuring nurse bee hypopharyngeal gland acini.

The nurse hypopharyngeal glands produce the protein fraction of the worker and royal jelly that is fed to developing larvae and queens. These paired ...

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. ...

BtM, a Low-cost Open-source Datalogger to Estimate the Water Content of Nonvascular Cryptogams

Communities of nonvascular cryptogams, such as mosses or lichens, are an important part of the Earth's biodiversity, contributing to the regulation of the carbon and nitrogen cycles in many ecosystems. Being poikilohydric organisms, they do not actively control their internal water content and need a humid environment to activate their metabolism. Therefore, studying water relationships of nonvascular cryptogams is crucial to understand both their diversity patterns and their functions in the ecosystems. We present the BtM datalogger, a low-cost open-source platform for the study of the water content of nonvascular cryptogams. The datalogger is designed to measure ambient temperature, humidity, and conductance from up to eight samples simultaneously. We provide a design for a printed circuit board (PCB), a detailed protocol to assemble the components, and the required source code. All this makes the assembly of the BtM datalogger accessible to any research group, even to those without previous specialized knowledge. Therefore, the design presented here has the potential to help popularize the use of this type of device among ecologists and field biologists.

We present a simple and cost-effective method to build an open-source datalogger that measures the conductance of nonvascular cryptogams together with the environmental temperature and humidity. We describe the hardware design of the datalogger and provide step-by-step assembly instructions, the list of required open-source logging software, the code to run the datalogger, and a calibration protocol.

Communities of nonvascular cryptogams, such as mosses or lichens, are an important part of the Earth's biodiversity, contributing to the regulation of the ...

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole

An experimental method is presented in this paper to facilitate visualization of the detailed flow fields and determination of the near-boundary shear and normal stresses within an equilibrium scour hole induced by a vibrating pipeline. This method involves the implementation of a pipeline vibration system in a straight flume, a time-resolved particle image velocimetry (PIV) system for pipeline displacement tracking and flow fields measurements. The displacement time-series of the vibrating pipeline are obtained by using the cross-correlation algorithms. The steps for processing raw particle laden images obtained by using the time-resolved PIV are described. The detailed instantaneous flow fields around the vibrating pipeline at different vibrating phases are calculated by using a multiple-time-interval cross-correlation algorithm to avoid displacement bias error in the flow regions with a large velocity gradient. By applying the wavelet transform technique, the captured images that have the same vibrating phase are accurately cataloged before the phase-averaged velocity fields are obtained. The key advantages of the flow measurement technique described in this paper are that it has a very high temporal and spatial resolution and can be simultaneously used to obtain the pipeline dynamics, flow fields, and near-boundary flow stresses. By using this technique, more in-depth studies of the 2-dimensional flow field in a complex environment, such as that around a vibrating pipeline, can be conducted to better understand the associated sophisticated scour mechanism.

The goal of the protocol is to enable visualization of the detailed flow fields and determination of the near-boundary shear and normal stresses within an equilibrium scour hole induced by a vibrating pipeline.

An experimental method is presented in this paper to facilitate visualization of the detailed flow fields and determination of the near-boundary shear and ...

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

The size spectrum is an inverse, allometric scaling relationship between average body mass (M) and the density (D) of individuals within an ecological community or food web. Importantly, the size spectrum assumes that individual size, rather than species&#8217; behavioral or life history characteristics, is the primary determinant of abundance within an ecosystem. Thus, unlike traditional allometric relationships that focus on species-level data (e.g., mean species&#8217; body size vs. population density), size spectra analyses are &#8216;ataxic&#8217; &#8211; individual specimens are identified only by their size, without consideration of taxonomic identity. Size spectra models are efficient representations of traditional, complex food webs and can be used in descriptive as well as predictive contexts (e.g., predicting responses of large consumers to changes in basal resources). Empirical studies from diverse aquatic ecosystems have also reported moderate to high levels of similarity in size spectra slopes, suggesting that common processes may regulate the abundances of small and large organisms in very different settings. This is a protocol to model the community-level size spectrum in wadable streams. The protocol consists of three main steps. First, collect quantitative benthic fish and invertebrate samples that can be used to estimate local densities. Second, standardize the fish and invertebrate data by converting all individuals to ataxic units (i.e., individuals identified by size, irrespective of taxonomic identity), and summing individuals within log2 size bins. Third, use linear regression to model the relationship between ataxic M and D estimates. Detailed instructions are provided herein to complete each of these steps, including custom software to facilitate D estimation and size spectra modeling.

This is a protocol to model the size spectrum (scaling relationship between individual mass and population density) for combined fish and invertebrate data from wadable streams and rivers. Methods include: field techniques to collect quantitative fish and invertebrate samples; lab methods to standardize the field data; and statistical data analysis.

The size spectrum is an inverse, allometric scaling relationship between average body mass (M) and the density (D) of individuals within an ecological ...

The Effect of Construction and Demolition Waste Plastic Fractions on Wood-Polymer Composite Properties

Construction and demolition waste (CDW), including valuable materials such as plastics, have a remarkable influence on the waste sector. In order for plastic materials to be re-utilized, they must be identified and separated according to their polymer composition. In this study, the identification of these materials was performed using near-infrared spectroscopy (NIR), which identified material based on their physical-chemical properties. Advantages of the NIR method are a low environmental impact and rapid measurement (within a few seconds) in the spectral range of 1600-2400 nm without special sample preparation. Limitations include its inability to analyze dark materials. The identified polymers were utilized as a component for wood-polymer composite (WPC) that consists of a polymer matrix, low cost fillers, and additives. The components were first compounded with an agglomeration apparatus, followed by production by extrusion. In the agglomeration process, the aim was to compound all materials to produce uniformly distributed and granulated materials as pellets. During the agglomeration process, the polymer (matrix) was melted and fillers and other additives were then mixed into the melted polymer, being ready for the extrusion process. In the extrusion method, heat and shear forces were applied to a material within the barrel of a conical counter-rotating twin-screw type extruder, which reduces the risk of burning the materials and lower shear mixing. The heated and sheared mixture was then conveyed through a die to give the product the desired shape. The above-described protocol proved the potential for re-utilization of CDW materials. Functional properties must be verified according to the standardized tests, such as flexural, tensile, and impact strength tests for the material.

Secondary material streams have been shown to include potential raw materials for production. Presented here is a protocol in which CDW-plastic waste as a raw material is identified, followed by various processing steps (agglomeration, extrusion). As a result, a composite material was produced, and mechanical properties were analyzed.

Construction and demolition waste (CDW), including valuable materials such as plastics, have a remarkable influence on the waste sector. In order for ...