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 thr...

<ol>
	<li>Show, K. Y., Lee, D. J., Tay, J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Aerobic+granulation:+Advances+and+challenges.">Aerobic granulation: Advances and challenges.</a> Applied Biochemistry and Biotechnology. 167 (6), 1622-1640 (2012).</li><li>Adav, S. S., Lee, D. -. J., Show, K. -. Y., Tay, J. -. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Aerobic+granular+sludge:+Recent+advances.">Aerobic granular sludge: Recent advances.</a> Biotechnology Advances. 26 (5), 411-423 (2008).</li><li>Initial Investigations of Aerobic Granulation in an Annular Gap Bioreactor. North Carolina State University Available from: <a target="_blank" href="https://repository.lib.ncsu.edu/handle/1840.16/2162">https://repository.lib.ncsu.edu/handle/1840.16/2162</a> (2004)</li><li>. 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. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Relationship+of+Species-Specific+Filament+Levels+to+Filamentous+Bulking+in+Activated+Sludge.">Relationship of Species-Specific Filament Levels to Filamentous Bulking in Activated Sludge.</a> Applied and Environmental Microbiology. 70 (4), 2420-2428 (2004).</li><li>Cromey, D. 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. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image+Alignment+for+Multiple+Camera+High+Dynamic+Range+Microscopy.">Image Alignment for Multiple Camera High Dynamic Range Microscopy.</a> Proceedings. IEEE Workshop on Applications of Computer Vision. , 225-232 (2012).</li><li>High Dynamic Range Microscopy for Cytopathological Cancer Diagnosis. IEEE Journal of Selected Topics in Signal Processing (Special Issue on: Digital Image Processing Techniques for Oncology) Available from: <a target="_blank" href="https://www.lfb.rwth-aachen.de/en/">https://www.lfb.rwth-aachen.de/en/</a> (2009)</li><li>Jain, V., 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=Supervised+Learning+of+Image+Restoration+with+Convolutional+Networks.">Supervised Learning of Image Restoration with Convolutional Networks.</a> 2007 IEEE 11th International Conference on Computer Vision. , 1-8 (2007).</li></ol>

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 ...

Watch this Scientific Journal Video about Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline at JoVE.com

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 ...

The size and shape of activated sludge particles is critical for wastewater treatment efficiency. Yet their measurement is often ad hoc. This protocol provides a repeatable, well defined, and semi-automatable measure for their measurement.The main advantage of this protocol is that it can measure a large population of particles over a wide range of morphologies while reducing subjective and systematic bias. While this technique is focused on activated sludge, technically anything that has the same particle morphology such as microplastics can be measured with this method. When attempting this technique for the first time, try making one plate at a time and practice the physical motions of making that one plate.Also, take your time with the microscopy. The video version of this article is important because sampling and plate preparation require a lot of little techniques that are best communicated visually. Also, showing and seeing good pictures of plates are important to ensure reproducibility.Demonstrating this protocol today is Joseph Weaver, a PHD candidate in my lab. To begin, acquire a representative sample from a well mixed portion of the reactor. Gently mix the sample and then immediately pour the determined sample volume into a 15 milliliter centrifuge tube.Next add five microliters of one percent methylene blue to each sample. Cap and mix the sample by gently inverting the tube at least three times. Allow the samples to stain for at least five but no more than 30 minutes at room temperature.Once stained, transfer a sufficient volume of melted 7.5 percent agar and deionized water to the centrifuge tube in order to bring the total tube volume to between 6.5 and 9 milliliters. Then recap the centrifuge tubes and gently invert them at least three times to mix the sample into the agar. While pointing the cap away from oneself, open the cap.Pour the tube contents of each tube into their own 100 milliliter plastic petri dish while gently rocking the dish to achieve a full, smooth coating and a visually uniform distribution of particles. Allow the plates to cool at room temperature for at least 5 minutes, until the agar solidifies. Place the uncovered plate face up on the microscope stage of a stereo microscope that is capable of 10 to 20 times magnification.Illuminate the sample from below with even diffuse light, using equipment such as an LED illuminator stand or light plate. Open the image capture software and ensure the microscope light path is set to photo. Then, select the appropriate camera from the camera list.Adjust the microscope so that multiple particles appear in the focal plane with large, well defined edges. Use a magnification of 10 to 20 times to measure particles while maintaining a relatively deep focal plane. 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. And calibrate the pixel to micron ratio. Next, set the zoom to 100 percent by clicking on view and selecting actual size.Then select options and go to calibrate. Here, align the red calibration bar in the main viewport along the long axis of the micrometer. With the vertical bars centered on the zero and 200 micron graduations.In the calibrate dialogue box, enter the current magnification level and actual length of 200 microns. If already calibrated, select magnification from the menu bar and then select the current magnification level to confirm the calibration. In the measurements menu, go to line and select arbitrary line.Click on the intersection of the zero graduation and long axis of the micrometer. Then click again on the intersection at 200 micron line and the long axis. A correct calibration should display as approximately 200 microns.Delete the line by clicking the select object button, clicking on the line, pressing delete, and pressing yes on the confirmation box. Next, replace the agar plate and adjust the fine focus to achieve maximum detail in the imaging software. To accomplish this, first increase the bit depth of the maximum value allowed by selecting the radio button in the bit depth panel of the camera sidebar.Also, set the software to acquire grayscale images by selecting the appropriate radio button in the color grade panel of the camera sidebar. Now, collapse any open sidebar panels between exposure and histogram. Reduce the gain to one 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.Adjust the histogram to avoid over and under exposure. In the histogram panel of the camera sidebar, slide the left boundary of the histogram to just outside the lowest values and the right boundary to just outside the highest values. Save the image as an uncompressed TIF file.Include magnification information in the image metadata by checking the save with calibration information box when saving. Select another area that does not overlap the previous images by following a path which alternates between left to right and right to left as one moves down the plate. Capture at least 500 visually estimated particles for analysis.Acquire the particle analysis code by cloning the GIT repository. At the command line, retrieve the latest version of the code by typing in the command shown here. Install the analysis code following the instructions in the read me text file found in the top level directory of the cloned repository.Next, edit a text file listing the directories to be processed along with optional parameters. Refer to the examples and analysis subdirectory for a list of parameters and examples. Now, run the analysis on the command line by typing the command shown here.Fiji path is the directory in which imageJ-win64. exe is located. And paramsfile the location of the text file describing the analysis setup.The name of the executable may differ depending on which operating system Fiji is installed on. The analysis will run automatically and take a few minutes to a few hours depending on the size and number of images. Next, examine the quality control files located in the overlay subdirectory of the specified output directory.Note images with spurious mist or poorly captured particles, all apparent as shaded outlines which did not match the background. That's it. The data are now ready for experiment specific analysis and figure generation.Although no particle thresholding method is perfect, here is an example of acceptable results. When evaluating QC images, there are three common errors found. Failure to accurately conform to the particle boundaries, failure to identify particles, and artifact inclusion.Here, the particle distributions between two experimental reactors over time are displayed and combined with qualitative metadata noted by the researcher. This chart shows that in this case, the reactors had generally similar particle size distributions which tended towards a larger mean and wider spread as time progressed. When performing this procedure, it is critical to evenly coat the plate with a thin layer of agar containing uniformly distributed particles.Be sure to practice and take your time. Beyond this procedure, interesting particles could be excised from the agar and studied. In terms of data, the resulting files from this protocol are well defined and the sky's the limit analytically.This technique paved the way into providing new insights into aerobic granular sludge. It's morphology is very important and it allowed us to measure it in a standard way. Wastewater is biologically active.Bio safety level one practices are encouraged. Also, how agar can bubble over when microwaved and can spray hot droplets when opening the centrifuge tube.

measuring-shape-size-activated-sludge-particles-immobilized-agar-with

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7.0K ビュー.  North Carolina State University. 活性汚泥粒子の大きさと形状は、排水処理の効率に不可欠です。しかし、彼らの測定はしばしばアドホックです。このプロトコルは、測定のための反復可能な、明確に定義された、半自動マブル測定を提供する。このプロトコルの主な利点は、主観的かつ体系的なバイアスを低減しながら、幅広い形態に渡って大量の粒子を測定できることです。この技術は活性汚?...