We thank Deok Joong Lee for critical reading of the manuscript and testing the Python code. This research was sponsored by the National Research Foundation of Korea 2017R1A5A2015369 (K.-h.Y.) and 2019R1C1C1008708 (K.-h.Y.).

1. Setting up the imaging apparatus (Figure 1A-E)
<ol>
	<li>Ensure that a smartphone camera with the following minimum requirements is available: 
	12&#160;megapixel (MP) camera 
	1080p resolution video 
	5 GB&#160;of storage space (20 min video is 3-4 Gb) 
	Time-lapse video app from the application store (free applications available)</li>
	<li>Place the LED light box on the bottom rack of the 25 &#176;C incubator where the assay will take place.</li>
	<li>To hide the dotted pattern on the LED light surface, spread two sheets of tissues to cover the entire surface of the LED box.</li>
	<li>Make an elevated stage for the specimen (Figure 1A,D). The elevated stage is a clear plastic sheet supported by hollow rectangular tunnels. Tunnels function like a condenser to focus light, providing better contrast to the specimen (Figure 1C). Ensure that the tunnel&#39;s walls are somewhat dark to minimize light scatter. This study used brown paper boxes. The dimension of the tunnel is 5.5 cm x 17 cm x 4.5 cm (W x L x H). The LED light box can fit up to five tunnels.</li>
	<li>Place another rack above the stage to place the phones for recording (Figure 1B,E). Each phone will record three to six plates (one to two rows of three plates), so adjust the rack height accordingly. This will be approximately 15 cm above the specimen (Figure 1B).</li>
	<li>Put a power strip inside the incubator to plug in the phones during overnight recording.</li>
</ol>
2. Preparation of buffers and media
<ol>
	<li>Prepare M9 buffer by adding 3 g of KH2PO4, 6 g of Na2HPO4, and 5 g of NaCl to 1 L of distilled H2O. Sterilize by autoclaving at 121 &#176;C for 20 min. Cool the buffer and then add 1 mL of 1 M MgSO4.</li>
	<li>Prepare 1 M KPO4 buffer by adding 108.3 g of KH2PO4 and 35.6 g of K2HPO4 to 1 L of H2O. Adjust the pH to 6.0 by adding KOH. Sterilize by autoclaving.</li>
	<li>Prepare worm bleaching solution by mixing 1 mL of bleach, 0.4 mL of 1 M NaOH, and 2.6 mL of H2O.</li>
	<li>Prepare nematode growth media (NGM) agar plates.
	<ol>
		<li>Add 3 g of NaCl, 2.5 g of bacto peptone, and 17 g of bacto agar in a 3 L flask. Add 975 mL of distilled water and insert a stir bar.</li>
		<li>Sterilize by autoclaving, then cool to 55 &#176;C, and add 1 mL of cholesterol (5 mg/mL in ethanol), 1 mL of 1 M CaCl2, 1 mL of 1 M MgSO4, and 25 mL of 1 M KPO4 buffer (pH 6.0). Stir to mix well. Pour into 6 cm plates. Let the plates dry for at least 2 days.</li>
	</ol>
	</li>
	<li>Seed NGM agar plates with OP50 E. coli by pipetting approximately 1 mL of an overnight culture of OP50 to form a lawn of bacteria. Leave at room temperature (RT) until ready to use.</li>
</ol>
3. Preparation of high-peptone NGM plates (for PA14)
NOTE: These plates should be made at least 5 days before the assay.
<ol>
	<li>Make NGM containing 0.35% peptone. Mix 0.3 g of NaCl, 0.35 g of bacto peptone, and 1.7 g of bacto agar in a 250 mL Erlenmeyer flask. Add 97.5 mL of distilled water and insert a stir bar.</li>
	<li>Cover the mouth of the flask with aluminum foil and autoclave at 121 &#176;C for 20 min.</li>
	<li>Cool down to 55 &#176;C and add 0.1 mL of cholesterol (5 mg/mL in ethanol), 0.1 mL of 1 M CaCl2, 0.1 mL of 1 M MgSO4, and 2.5 mL of 1 M KPO4 buffer (pH 6.0). Stir to mix well.</li>
	<li>Pour high-peptone NGM into 35 mm Petri dishes.</li>
	<li>Dry the plates for at least 2 days.</li>
</ol>
4. Synchronizing worms by bleaching
NOTE: Start this step 3 days before the assay.
<ol>
	<li>Take plates with gravid adult worms and collect them into a 1.7 mL microtube by washing the plates with M9 buffer.</li>
	<li>Remove as much liquid as possible, then add 400 &#181;L of bleaching solution. Wait about 4-5 min with intermittent vortexing, until the adult worm bodies break, releasing the eggs.</li>
	<li>Add M9 buffer to fill the rest of the microtube to dilute the bleaching solution. Spin at maximum speed (12,000 to 13,000 x g) for 1-2 s. Remove the supernatant and wash three more times with M9 buffer.</li>
	<li>Transfer the eggs into an empty 35 mm Petri dish containing M9 buffer. Let the eggs hatch overnight at 20 &#176;C. In the absence of food, hatched worms will arrest at the L1 larval stage, synchronizing the developmental stage of all worms. 
	NOTE: Coating the 35 mm Petri dish with gelatin solution (0.05% gelatin in autoclaved water) can prevent the eggs from sticking to the bottom and minimize egg loss.</li>
	<li>The next day, transfer L1 stage worms to OP50 seeded NGM plates.</li>
	<li>Incubate the worms at 20 &#176;C for 53-54 h until the worms reach the L4 larval stage.</li>
</ol>
5. Preparation of bacteria (&#160;Pseudomonas aeruginosa, PA14)
NOTE: Start this step 4 days before the assay.
<ol>
	<li>Streak thawed bacteria from -80 &#176;C on a Luria Bertani (LB) agar plate without any antibiotic and incubate overnight at 37 &#176;C. 
	NOTE: Always use fresh bacteria. Streaked plates should be stored at 4 &#176;C for no more than 1 week.</li>
	<li>Inoculate a single colony into 3 mL of King&#39;s broth and grow overnight in a 37 &#176;C shaking incubator.</li>
	<li>The next day, seed 7 &#181;L of the overnight culture onto the high-peptone NGM plates and incubate at 37 &#176;C for 24 h.</li>
	<li>Move the seeded plates to RT and incubate for another 24 h before use. Once ready, use the plate within the next 24 h.</li>
</ol>
6. Preparing to record
NOTE: Do this right before the assay.
<ol>
	<li>Plug in the smartphone to the power strip connected to a power outlet. Make sure to disable the auto-lock setting to prevent the phone returning to the lock screen while recording.</li>
	<li>Open the time-lapse camera app and set the time-lapse interval to 2 s. Set the video quality to 1080p at 30 fps.</li>
	<li>Place the smartphone with the screen facing up to record with the back-facing camera. Check the screen to make sure that the paper box tunnels fit within the field of view.</li>
</ol>
7. Lawn avoidance assay
<ol>
	<li>Using a platinum wire pick, transfer 30 synchronized L4 stage worms (53-54 h from L1) to the PA14 plate. Place the worms in the middle of the bacteria lawn. For each condition in this study, two plates were tested (i.e., 60 worms per condition).</li>
	<li>Place the two plates on the elevated stage of the recording apparatus with the lid facing down. The side with the agar will be facing up toward the camera.</li>
	<li>On the smartphone screen, tap where the plate is, so the camera can focus on the assay plates. It helps to have a label or writing on the plate as the camera can use that to focus correctly. 
	NOTE: Writing on the bottom of the plates does not interfere with the imaging of worms as long as it is toward the edge. Fortunately, worms stay near the lawn even after they leave, so an unobstructed view is only needed of the immediate area surrounding the lawn.</li>
	<li>Start the recording.</li>
	<li>Once the recording has started, add more plates to the stage. There may be a significant lag time between the plates due to the time it takes to transfer worms by picking. Note the lag time afterward so that each condition can be counted at the time it began.</li>
	<li>Record for 20 h from the last set of plates placed on the stage. In the final time-lapse video, 20 h of recording will result in a 20 min long video. 
	NOTE: It may be worthwhile to count the worms directly from the plates after the assay, at least at the beginning for the first few occasions. This can be compared against values obtained through video imaging to ensure they yield similar numbers.</li>
</ol>
8. Processing of video using Python script
<ol>
	<li>Transfer the movie file to a computer for processing. The extension will be a MOV (iPhone) or MP4 file (Android).</li>
	<li>Use a Python code to process the videos. The code can be found at github.com/khyoon201/wormavoid.</li>
	<li>To run the Python scripts, ensure that the following are preinstalled on the computer: ffmpeg, a tool for converting video files (directions for installation can be found on its website, ffmpeg.org/download), and the Python packages os, pandas, tkinter, and ffmpeg-python.</li>
	<li>Find the dimensions and coordinates of each plate using the extract_frame.py script.
	<ol>
		<li>Run the extract_frame.py script. A window will appear to select the video file stored on the computer. After running is completed, a jpeg file with the same name will appear in the same directory.</li>
		<li>Open the jpeg file in ImageJ (imagej.org).</li>
		<li>From the menu, choose Analyze &#62; Set Measurements. Ensure that the Display Label box is checked (Figure 2A). Close the window.</li>
		<li>Using the Straight Line tool, measure the diameter of a plate by drawing a line across it, then choosing Analyze &#62; Measure from the menu. If the video is in 1080p, each plate will be about 480 pixels wide. Write this information down and close the Results window.</li>
		<li>Using the Multi-point tool, mark points on the upper left side of each plate. These points will become the upper left corner of the cropped videos (Figure 2B). The order matters; mark in order of when the plates were started. After creating a point for all the plates, choose Analyze &#62; Measure from the menu. Measurements, including the X and Y coordinates of the points, will appear in the Results window.</li>
		<li>To process multiple videos, repeat the process in ImageJ with other jpeg files. All X and Y coordinates will be listed in the same Results window.</li>
		<li>Save the Results window into a csv file. The file should be saved to the same directory as the movie files.</li>
	</ol>
	</li>
	<li>Find the starting time for each plate.
	<ol>
		<li>Play the movie, either on the computer or phone, and take note of the starting times of each set of plates placed under the camera.</li>
		<li>Open the Results.csv file with the coordinates and add a &#34;start&#34; column. For each row corresponding to individual plates, enter the appropriate start time, in seconds, under the &#34;start&#34; column (e.g., if the start time is 0:00:08, enter 8). Save. 
		NOTE: The column name must be &#34;start&#34; (in lower case, without quotation marks) to be recognized by the next script for cropping and trimming.</li>
	</ol>
	</li>
	<li>Crop and trim the videos.
	<ol>
		<li>Run the crop_n_trim.py script.</li>
		<li>When prompted, choose the Results.csv file. 
		NOTE: Make sure the Results.csv file and all the movie files are in the same directory.</li>
		<li>Enter the plate dimensions. Enter the pixel value noted earlier. 
		&#8203;NOTE: The script will now read each row of the Results.csv file to find the correct movie file by reading the file name in the &#34;label&#34; column, and crop according to the coordinates indicated in columns &#34;X&#34; and &#34;Y&#34;. The start time of each plate will be determined by the time indicated in the &#34;start&#34; column. After the script finishes running, a folder will appear with the same name as the movie, followed by the start time (e.g., &#34;Movie1_8&#34;), in which 10 s videos corresponding to each hourly time point of the assay will be saved.</li>
	</ol>
	</li>
</ol>
9. Manual counting using ImageJ
<ol>
	<li>Open each AVI file in ImageJ.</li>
	<li>Count the worms that are visible outside the lawn. Worms that are overlapped in one frame usually move apart in another frame so that they can be counted correctly.</li>
	<li>Calculate the occupancy rate for each time point: 
	Occupancy rate = (total worms - number of worms outside the lawn)/total worms 
	NOTE: The worms will move in and out of the lawn during the video, but this will not significantly alter the results. Try to go with the number that seems to be the average, or the number of worms at the exact hourly time point (5 s into the video).</li>
</ol>

Smartphone-Based Imaging for C. elegans Lawn Avoidance Assay

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H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Caenorhabditis+elegans+NPR-1-mediated+behaviors+are+suppressed+in+the+presence+of+mucoid+bacteria.">Caenorhabditis elegans NPR-1-mediated behaviors are suppressed in the presence of mucoid bacteria.</a> Proceedings of the National Academy of Sciences. 108 (31), 12887-12892 (2011).</li><li>Hao, Y., 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=Thioredoxin+shapes+the+C.+elegans+sensory+response+to+Pseudomonas+produced+nitric+oxide.">Thioredoxin shapes the C. elegans sensory response to Pseudomonas produced nitric oxide.</a> eLife. 7, 36833 (2018).</li><li>Liu, Y., Samuel, B. S., Breen, P. C., Ruvkun, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Caenorhabditis+elegans+pathways+that+surveil+and+defend+mitochondria.">Caenorhabditis elegans pathways that surveil and defend mitochondria.</a> Nature. 508 (7496), 406-410 (2014).</li><li>Melo, J. A., Ruvkun, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inactivation+of+conserved+C.+elegans+genes+engages+pathogen-+and+xenobiotic-associated+defenses.">Inactivation of conserved C. elegans genes engages pathogen- and xenobiotic-associated defenses.</a> Cell. 149 (2), 452-466 (2012).</li><li>Meisel, J. D., Panda, O., Mahanti, P., Schroeder, F. C., Kim, D. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chemosensation+of+bacterial+secondary+metabolites+modulates+neuroendocrine+signaling+and+behavior+of+C.+elegans.">Chemosensation of bacterial secondary metabolites modulates neuroendocrine signaling and behavior of C. elegans.</a> Cell. 159 (2), 267-280 (2014).</li><li>Singh, J., Aballay, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intestinal+infection+regulates+behavior+and+learning+via+neuroendocrine+signaling.">Intestinal infection regulates behavior and learning via neuroendocrine signaling.</a> eLife. 8, 50033 (2019).</li><li>Lee, K., Mylonakis, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+intestine-derived+neuropeptide+controls+avoidance+behavior+in+Caenorhabditis+elegans.">An intestine-derived neuropeptide controls avoidance behavior in Caenorhabditis elegans.</a> Cell Reports. 20 (10), 2501-2512 (2017).</li><li>Singh, J., Aballay, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bacterial+lawn+avoidance+and+bacterial+two+choice+preference+assays+in+Caenorhabditis+elegans.">Bacterial lawn avoidance and bacterial two choice preference assays in Caenorhabditis elegans.</a> Bio-Protocol. 10 (10), 3623 (2020).</li><li>Reddy, K. C., Andersen, E. C., Kruglyak, L., Kim, D. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+polymorphism+in+npr-1+is+a+behavioral+determinant+of+pathogen+susceptibility+in+C.+elegans.">A polymorphism in npr-1 is a behavioral determinant of pathogen susceptibility in C. elegans.</a> Science. 323 (5912), 382-384 (2009).</li><li>de Bono, M., Bargmann, C. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Natural+variation+in+a+neuropeptide+Y+receptor+homolog+modifies+social+behavior+and+food+response+in+C.+elegans.">Natural variation in a neuropeptide Y receptor homolog modifies social behavior and food response in C. elegans.</a> Cell. 94 (5), 679-689 (1998).</li><li>Mathew, M. D., Mathew, N. D., Ebert, P. 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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=Longitudinal+imaging+of+Caenorhabditis+elegans+in+a+microfabricated+device+reveals+variation+in+behavioral+decline+during+aging.">Longitudinal imaging of Caenorhabditis elegans in a microfabricated device reveals variation in behavioral decline during aging.</a> eLife. 6, 26652 (2017).</li><li>Marquina-Solis, J., 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=Peptidergic+signaling+controls+the+dynamics+of+sickness+behavior+in+Caenorhabditis+elegans.">Peptidergic signaling controls the dynamics of sickness behavior in Caenorhabditis elegans.</a> bioRxiv. , (2022).</li><li>Churgin, M. 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No conflicts of interest declared.

Imaging animal behavior, rather than relying on direct observation, is not only convenient but also has the advantage of leaving visual documentation. This allows for blind analysis by an objective third person, or could even be used for automated analysis using image recognition techniques. Despite the advantages, the standard equipment usually offered is high in cost, so one is committed to the setup once purchased.
Using smartphones to collect video recordings of simple C. elegans ...

Among the many advantages of studying C. elegans, its simple nervous system offers the opportunity to study how changes at the genetic and cellular level impact network function and behavioral output. Despite having a limited number of neurons, C. elegans display a wide range of complex behaviors. One of these is lawn avoidance, in which the bacterivorous nematode responds to a harmful food source by leaving the bacterial lawn. C. elegans avoid lawns of pathogenic bacteria1,2,3, lawns of bacteria that produce toxins or are spiked with toxins1,4, and even RNAi-expressing bacteria whose target gene knockdown is detrimental to the health of the worms4,5. Studies have shown that worms respond to external cues such as metabolites produced by the pathogenic bacteria1,6, or internal cues that indicate that the food is making them sick4,7. These cues are processed through conserved signaling pathways, such as the mitogen-activated protein kinase (MAPK) pathway and the transforming growth factor beta (TGF&#946;) pathway, and require communication between the gut and the nervous system4,6,7,8.
Although the assay is simple, the learned behavior develops over many hours, often overnight. While there are mutants that are incapable of leaving, in which case scoring avoidance at just one time point is sufficient to demonstrate the defect, many mutants do leave eventually but are slower to come out. For these, the movement of the worms needs to be tracked every few hours, which can be difficult to do overnight. Counting itself also takes time, creating a lag time between the plates, and thus limits the number of plates that can be tested at the same time. Using an imaging setup to record many plates simultaneously for the whole duration of the assay would be very useful, but the cost of setup can be prohibitive, depending on the funding situation of the research lab.
To address this, we devised a very simple method that uses smartphones to record avoidance assays. Each phone can record time-lapse videos of up to six assay plates. To provide transmitted light, we use a light emitting diode (LED) light box that can be easily purchased online. Assay plates are placed on an elevated platform, supported by hollow rectangular tunnels, that focus the incoming light, creating contrast. We also provide a Python script that converts the videos into audio video interleave (AVI) files showing 10 s clips of each hourly time point. The videos are then cropped to individual plates and saved in separate files to use for manual counting.
The method provides a low-cost procedure that is also extremely easy to use, using items that are readily available to most people. Here, we describe the method using the well-established lawn avoidance assay against the human pathogen Pseudomonas aeruginosa (PA14), whose protocol has been previously described2,9. Finally, we also review the considerations and limitations of the imaging method for those that want to apply it to other C. elegans behavior experiments.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>35 mm Petri dish</td>
			<td>SPL</td>
			<td>#10035</td>
			<td></td>
		</tr>
		<tr>
			<td>Bacto agar</td>
			<td>BD</td>
			<td>#214010</td>
			<td></td>
		</tr>
		<tr>
			<td>Bacto Peptone</td>
			<td>BD</td>
			<td>#211677</td>
			<td></td>
		</tr>
		<tr>
			<td>CaCl2</td>
			<td>DAEJUNG</td>
			<td>2507-1400</td>
			<td></td>
		</tr>
		<tr>
			<td>Cholesterol</td>
			<td>BioBasic</td>
			<td>CD0122</td>
			<td></td>
		</tr>
		<tr>
			<td>Dipotassium hydrogen phosphate (K2HPO4)</td>
			<td>JUNSEI</td>
			<td>84120-0350</td>
			<td></td>
		</tr>
		<tr>
			<td>Glycerol</td>
			<td>BioBasic</td>
			<td>GB0232</td>
			<td></td>
		</tr>
		<tr>
			<td>King B Broth</td>
			<td>MB cell</td>
			<td>MB-K0827</td>
			<td></td>
		</tr>
		<tr>
			<td>LED light box multi-pad</td>
			<td>Artmate</td>
			<td>N/A</td>
			<td>This is a USB powered, LED light pad for tracing and drawing purposes. Artmate is a Korean brand, but searching for &#34;LED light box for tracing&#34; in any search engine should yield numerous options from other brands. Overall dimension is around 9&#34; x 12&#34; (A4 size). For example, from amazon US: https://www.amazon.com/LITENERGY-Ultra-Thin-Adjustable-Streaming-Stenciling/dp/B07H7FLJX1/ref=sr_1_5?crid=YMYU0VYY226R&#38;keywords= 
			LED%2Blight%2Bbox&#38;qid=1674183224&#38;sprefix 
			=led%2Blight%2Bbo%2Caps%2C270&#38;sr=8-5&#38;th=1</td>
		</tr>
		<tr>
			<td>MgSO4</td>
			<td>DAEJUNG</td>
			<td>5514-4400</td>
			<td></td>
		</tr>
		<tr>
			<td>Plastic paper sleeve (clear)</td>
			<td>Smead</td>
			<td>#85753</td>
			<td>Any clear plastic sheet with a bit of stiffness can be used as stage. For example, from Amazon US: https://www.amazon.com/Smead-Organized-Translucent-Project-85753/dp/B07HJTRCT7/ref=psdc_1069554_t3_B09J48GXQ 
			8</td>
		</tr>
		<tr>
			<td>Potassium dihydrogen phosphate (KH2PO4)</td>
			<td>JUNSEI</td>
			<td>84185-0350</td>
			<td></td>
		</tr>
		<tr>
			<td>Power strip&#160;</td>
			<td></td>
			<td></td>
			<td>To accommodate 3 phones and one LED box, you need at least 4 outlets.</td>
		</tr>
		<tr>
			<td>Smartphone</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Minimum requirement: 12MP wide camera, 1080p HD video recording at 30fps</td>
		</tr>
		<tr>
			<td>Sodium chloride(NaCl)</td>
			<td>DAEJUNG</td>
			<td>#7548-4100</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium phosphate dibasic anhydrous (Na2HPO4)</td>
			<td>YAKURI</td>
			<td>#31727</td>
			<td></td>
		</tr>
	</tbody>
</table>

a smartphone-based imaging method for c. elegans lawn avoidance assay

When exposed to toxic or pathogenic bacteria, the nematode Caenorhabditis elegans displays a learned lawn avoidance behavior, in which the worms gradually leave their food source and prefer to remain outside the bacterial lawn. The assay is an easy way to test the worms' ability to sense external or internal cues to properly respond to harmful conditions. Though a simple assay, counting is time consuming, particularly with multiple samples, and assay durations that span overnight are inconvenient for researchers. An imaging system that can image many plates over a long period is useful but costly. Here, we describe a smartphone-based imaging method to record lawn avoidance in C. elegans. The method requires only a smartphone and a light emitting diode (LED) light box, to serve as a transmitted light source. Using free time-lapse camera applications, each phone can image up to six plates, with sufficient sharpness and contrast to manually count worms outside the lawn. The resulting movies are processed into 10 s audio video interleave (AVI) files for every hourly time point, then cropped to show each single plate to make them more amenable for counting. This method is a cost-effective way for those looking to examine avoidance defects and can potentially be extended to other C. elegans assays.

The first video produced by the script is 1 h from the start of the assay. The video for 0 h is not saved, as worms start the assay inside the lawn, so the occupancy rate is always 100%.
Wild-type N2 worms are compared against npr-1 mutants, whose lawn avoidance defect is well-established in the literature6,10 (Figure 3A-E). As can be seen in the wild type, worms ...

Watch this Scientific Journal Video about A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay at JoVE.com

Author Spotlight: A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay  

This article describes a simple, low-cost method of recording the lawn avoidance behavior of Caenorhabditis elegans, using readily available items such as a smartphone and a light emitting diode (LED) light box. We also provide a Python script to process the video file into a format more amenable for counting.

A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay

When exposed to toxic or pathogenic bacteria, the nematode Caenorhabditis elegans displays a learned lawn avoidance behavior, in which the worms gradually ...

a-smartphone-based-imaging-method-for-c-elegans-lawn-avoidance-assay

Research

JoVE Journal

Behavior

9.6K Views.  Yonsei University Wonju College of Medicine. This article describes a simple, low-cost method of recording the lawn avoidance behavior of Caenorhabditis elegans, using readily available items such as a smartphone and a light emitting diode (LED) light box. We also provide a Python script to process the video file into a format more amenable for counting. 

Video: Author Spotlight: A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay  

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). 
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% &#177; 7.2% and specificity of 98.96% &#177; 0.6%, while the second prediction set had a sensitivity of 88.35% &#177; 7.80% and specificity of 98.51% &#177; 0.62%. For the third classification set, sensitivity was 84.92% &#177; 6.38% and specificity was 98.17 &#177; 0.62. F1 scores for the first, second and third classification sets were 86.17 &#177; 6.3, 80.19 &#177; 6.36, and 78.42 &#177; 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that ...

Novel Object Exploration as a Potential Assay for Higher Order Repetitive Behaviors in Mice

Restricted, repetitive behaviors (RRBs) are a core feature of autism spectrum disorder (ASD) and disrupt the lives of affected individuals. RRBs are commonly split into lower-order and higher-order components, with lower order RRBs consisting of motor stereotypies and higher order RRBs consisting of perseverative and sequencing behaviors. Higher order RRBs are challenging to model in mice. Current assays for RRBs in mice focus primarily on the lower order components, making basic biomedical research into potential treatments or interventions for higher-order RRBs difficult. Here we describe a new assay, novel object exploration. This assay uses a basic open-field arena with four novel objects placed around the perimeter. The test mouse is allowed to freely explore the arena and the order in which the mouse investigates the novel objects is recorded. From these data, patterned sequences of exploration can be identified, as can the most preferred object for each mouse. The representative data shared here and past results using the novel object exploration assay illustrate that inbred mouse strains do demonstrate different behavior in this assay and that strains with elevated lower order RRBs also show elevated patterned behavior. As such, the novel object exploration assay appears to possess good face validity for higher order RRBs in humans and may be a valuable assay for future studies investigating novel therapeutics for ASD.

Higher order restricted, repetitive behaviors (RRBs) disrupt the lives of affected individuals. These behaviors are challenging to model in rodents, making basic biomedical research into potential treatments or interventions for RRBs difficult. Here we describe novel object exploration as a potential assay for higher order RRBs in mice.

Restricted, repetitive behaviors (RRBs) are a core feature of autism spectrum disorder (ASD) and disrupt the lives of affected individuals.  RRBs are ...

High-Throughput Method for Measuring Alcohol Sedation Time of Individual Drosophila melanogaster

Drosophila melanogaster provides an excellent model to study the genetic underpinnings of alcohol sensitivity. In contrast to studies in human populations, the Drosophila model allows strict control over genetic background, and virtually unlimited numbers of individuals of the same genotype can be reared rapidly under well-controlled environmental conditions without regulatory restrictions and at relatively low cost. Flies exposed to ethanol undergo physiological and behavioral changes that resemble human alcohol intoxication, including loss of postural control, sedation, and development of tolerance. Here, we describe a simple, low-cost, high-throughput assay for assessing alcohol sedation sensitivity in large numbers of single flies. The assay is based on video recording of single flies introduced without anesthesia in 24-well cell culture plates in a set-up that enables synchronous initiation of alcohol exposure. The system enables a single person to collect individual ethanol sedation data on as many as 2,000 flies within an 8 h work period. The assay can, in principle, be extended to assess the effects of exposure to any volatile substance and applied to measure effects of acute toxicity of volatiles on other insects, including other fly species.

High-Throughput Method for Measuring Alcohol Sedation Time of Individual Drosophila melanogaster

Current methods to measure alcohol sensitivity in Drosophila are designed to test groups of flies. We present a simple, low-cost, high-throughput assay for assessing alcohol sedation sensitivity in large numbers of single flies. The method does not require specialized tools and can be performed in any laboratory using common materials.

Drosophila melanogaster provides an excellent model to study the genetic underpinnings of alcohol sensitivity. In contrast to studies in human ...

Calcium Imaging in Freely Behaving Caenorhabditis elegans with Well-Controlled, Nonlocalized Vibration

Nonlocalized mechanical forces, such as vibrations and acoustic waves, influence a wide variety of biological processes from development to homeostasis. Animals cope with these stimuli by modifying their behavior. Understanding the mechanisms underlying such behavioral modification requires quantification of neural activity during the behavior of interest. Here, we report a method for calcium imaging in freely behaving Caenorhabditis elegans with nonlocalized vibration of specific frequency, displacement, and duration. This method allows the production of well-controlled, nonlocalized vibration using an acoustic transducer and quantification of evoked calcium responses at single-cell resolution. As a proof of principle, the calcium response of a single interneuron, AVA, during the escape response of C. elegans to vibration is demonstrated. This system will facilitate understanding of neural mechanisms underlying behavioral responses to mechanical stimuli.

Calcium Imaging in Freely Behaving Caenorhabditis elegans with Well-Controlled, Nonlocalized Vibration

Reported here is a system for calcium imaging in freely behaving Caenorhabditis elegans with well-controlled, nonlocalized vibration. This system allows researchers to evoke nonlocalized vibrations with well-controlled properties at nano-scale displacement and to quantify calcium currents during responses of C. elegans to the vibrations.

Nonlocalized mechanical forces, such as vibrations and acoustic waves, influence a wide variety of biological processes from development to homeostasis. ...

Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm

Avoidance behavior is a key contributor to the transition from acute pain to chronic pain disability. Yet, there has been a lack of ecologically valid paradigms to experimentally investigate pain-related avoidance. To fill this gap, we developed a paradigm (the robotic arm-reaching paradigm) to investigate the mechanisms underlying the development of pain-related avoidance behavior. Existing avoidance paradigms (mostly in the context of anxiety research) have often operationalized avoidance as an experimenter-instructed, low-cost response, superimposed on stimuli associated with threat during a Pavlovian fear conditioning procedure. In contrast, the current method offers increased ecological validity in terms of instrumental learning (acquisition) of avoidance, and by adding a cost to the avoidance response. In the paradigm, participants perform arm-reaching movements from a starting point to a target using a robotic arm, and freely choose between three different movement trajectories to do so. The movement trajectories differ in probability of being paired with a painful electrocutaneous stimulus, and in required effort in terms of deviation and resistance. Specifically, the painful stimulus can be (partly) avoided at the cost of performing movements requiring increased effort. Avoidance behavior is operationalized as the maximal deviation from the shortest trajectory on each trial. In addition to explaining how the new paradigm can help understand the acquisition of avoidance, we describe adaptations of the robotic arm-reaching paradigm for (1) examining the spread of avoidance to other stimuli (generalization), (2) modeling clinical treatment in the lab (extinction of avoidance using response prevention), as well as (3) modeling relapse, and return of avoidance following extinction (spontaneous recovery). Given the increased ecological validity, and numerous possibilities for extensions and/or adaptations, the robotic arm-reaching paradigm offers a promising tool to facilitate the investigation of avoidance behavior and to further our understanding of its underlying processes.

Avoidance is central to chronic pain disability, yet adequate paradigms for examining pain-related avoidance are lacking. Therefore, we developed a paradigm that allows investigating how pain-related avoidance behavior is learned (acquisition), spreads to other stimuli (generalization), can be mitigated (extinction), and how it may subsequently re-emerge (spontaneous recovery).

Avoidance behavior is a key contributor to the transition from acute pain to chronic pain disability. Yet, there has been a lack of ecologically valid ...

Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

To collect dietary intake data in a fast and reliable manner, a flexible and innovative smartphone application (app) called Traqq was developed (iOS/Android). This app can be used as a food record and&#160;24-h recall (or shorter recall periods). Different sampling schemes can be created on either prespecified or random days/times within a predetermined period for both methods, with&#160;push notifications to urge the participants to register their food intake. In case of non-response, notifications are automatically rescheduled to ensure complete data collection. For use as a food record, respondents can access the app and log their food intake throughout the day. Food records close automatically at the end of the day; recalls close after submission of the consumed items. The recall as well as the food record module provide access to an extensive food list based on the Dutch food composition database (FCDB), which can be accustomed to fit different research purposes. When selecting a food item, respondents are simultaneously prompted to insert portion size, i.e., in household measures (e.g., cups, spoons, glasses), standard portion sizes (e.g., small, medium, large), or weight in grams, and eating occasion/time of consumption. Portion size options can be adjusted, e.g., only entry in grams in case of a weighed food record or time of consumption instead of eating occasion). The app also includes a My Dishes function, which allows the respondent to create their own recipes or product combinations (e.g., a daily breakfast) and only report the total quantity consumed. Subsequently, the app accounts for yield and retention factors. The data are stored on a secure server. If desired, additional questions, i.e., in general or those related to specific food items or eating occasions can be incorporated. This paper describes the development of the system (app and backend), including expert evaluations and usability testing.

This article describes the protocol for the development of an innovative smartphone-based dietary assessment application Traqq, including expert evaluations and usability testing.

To collect dietary intake data in a fast and reliable manner, a flexible and innovative smartphone application (app) called Traqq was developed ...

Understanding Spatial Learning in Mice Using the Active Place Avoidance Test

The Active Place Avoidance (APA) Test, an Effective, Versatile and Repeatable Spatial Learning Task for Mice

Hippocampus-dependent spatial learning in rodents has been tested using a variety of methods. These include the Morris water maze (MWM), Y-maze, and novel object location (NOL) tasks. More recently, the active place avoidance (APA) task has been developed as an alternative to these more traditional approaches. In the APA task, mice must use spatial cues placed around a rotating arena to avoid a stationary shock zone. Due to the multiple parameters that can be adjusted, the APA task has been demonstrated to be a very versatile approach. It lends itself to being used longitudinally and repeatedly for the same cohort of mice. Here, we provide a detailed protocol to successfully conduct the APA task. We also highlight alternative APA approaches that can be used to examine different components of spatial learning. We describe the data collection and analysis processes. Critical steps during the APA task are discussed to increase the likelihood of successfully conducting the test. The APA task has several advantages over more traditional spatial navigation tests. It is appropriate to use with aged mice or those with disease phenotypes such as Alzheimer's disease. The complexity of the task can be easily altered, allowing a wide range of mouse strains to be tested. Further, the APA task is suitable for testing animals that have undergone surgery or experimental interventions that may have affected motor or neural function, such as stroke or traumatic brain injury.

Author Spotlight: Investigating the Impact of Aging on Hippocampal-Dependent Spatial Learning

Here, we present a protocol for the active place avoidance test, a hippocampus-dependent spatial learning paradigm designed for rodents. Altering key parameters allows for re-testing of animals before and after treatments or over time.

Hippocampus-dependent spatial learning in rodents has been tested using a variety of methods. These include the Morris water maze (MWM), Y-maze, and novel ...

Extraction and Quantification of Volatile Sex Pheromone Responses in C. elegans

Volatile Sex Pheromone Extraction and Chemoattraction Assay in Caenorhabditis elegans

Chemical communication is vital in organismal health, reproduction, and overall well-being. Understanding the molecular pathways, neural processes, and computations governing these signals remains an active area of research. The nematode Caenorhabditis elegans provides a powerful model for studying these processes as it produces a volatile sex pheromone. This pheromone is synthesized by virgin females or sperm-depleted hermaphrodites and serves as an attractant for males.
This protocol describes a detailed method for isolating the volatile sex pheromone from several C. elegans strains (WT strain N2, daf-22, and fog-2) and C. remanei. We also provide a protocol for quantifying the male chemotaxis response to the volatile sex pheromone. Our analysis utilizes measurements such as chemotaxis index (C.I.), arrival time (A.T.), and a trajectory plot to compare male responses under various conditions accurately. This method allows for robust comparisons between males of different genetic backgrounds or developmental stages. Furthermore, the experimental setup outlined here is adaptable to investigating other chemoattraction chemicals.

Author Spotlight: Examining Volatile Sex Pheromone Influence on Male C. elegans Behavior

This protocol establishes methods for extracting and quantifying responses to the volatile sex pheromone in C. elegans, providing tools to study chemical communication and navigation trajectory.

Chemical communication is vital in organismal health, reproduction, and overall well-being. Understanding the molecular pathways, neural processes, and ...