The project described was supported by Institutional Development Award (IDeA) grants from the National Institute of General Medical Sciences of the National Institutes of Health (5P20GM103427 and 1U54GM115458). The UNK Undergraduate Research Fellows Program and the UNMC Medical Student Summer Research Program.

1. Fly husbandry
<ol>
	<li>Choose the appropriate fly stock/line for the investigation. 
	NOTE: To illustrate the assay, we used the transient receptor potential TRPA1 knockout stock and its genetic control, the w1118 stock.</li>
	<li>Maintain the fly stocks under consistent conditions as appropriate. To follow this protocol, house stocks at 25 &#730;C under a 12:12 diurnal cycle on standard food (cornmeal, molasses, and torula yeast).</li>
	<li>Separate males and females with light anesthesia. 
	NOTE: Depending on the experiment, virgin or mated flies might be used.
	<ol>
		<li>Allow adult males and females (separately for both stocks) to mate and oviposit for 5 days. Clear the adults from the bottles.</li>
		<li>As adults begin to eclose several days later, clear the bottles again. After 2 days, separate the males and females using light ether anesthesia and allow them to mature separately at a density of 25 flies per vial.</li>
	</ol>
	</li>
</ol>
2. Pretreatment
<ol>
	<li>Apply the pretreatment or independent variable that will define the experimental groups (genetic, environmental, pharmacologic, or otherwise).
	<ol>
		<li>To follow this protocol, separate the adults by sex, and after 5 days, pretreat half of the adults by immersing a sealed vial containing the organisms in a water bath at 37 &#730;C for 1 h. Keep the controls in the incubator at 25 &#730;C. Allow the flies to recover for 24 h after pretreatment, prior to the heat tolerance assay.</li>
	</ol>
	</li>
</ol>
3. DAM2 system setup
<ol>
	<li>Load the flies into the DAM2 monitor tubes and cap the tubes at both ends with cotton. If the flies are to be assessed for heat tolerance immediately, do not use any anesthesia while loading the organisms into the monitor tubes to avoid any potential confounding effects associated with anesthesia exposure. Instead, aspirate individual flies from the holding vials into the monitor tubes.</li>
	<li>Put the assay tubes into the activity monitors, taking note as to which slot numbers the different groups are loaded. 
	NOTE: These may be randomized depending on the experiment if location within the monitor might be considered a confounding variable.</li>
	<li>DAM2 data acquisition software 
	NOTE: The DAM2 system and software are described thoroughly in the DAMSystem3 Software Data Sheet available on the company&#39;s website (see the Table of Materials). Refer to the DAM2 instructions for specific troubleshooting and general functionality. Here we provide guidance for using the system in the context of our heat tolerance assay.
	<ol>
		<li>The DAM2 system monitors the number of times an individual fly within each tube breaks an infrared beam. The data acquisition software then indexes and resets this count every defined time interval. Under preferences, select the reading interval to be used in the assay. We have found a 15 s reading interval to achieve a good balance between resolution and reading errors. 
		NOTE: It is recommended that the reading interval be no shorter in seconds than the number of monitors being used. A shorter reading interval will give better temporal resolution to the assay but will also potentially increase the instances of reading errors. The software can fall behind while trying to index the readings. In addition, a newer, faster computer goes a long way in preventing reading errors.</li>
		<li>While considering reading errors, ensure that the computer being used to run the DAM2 data acquisition software is set to never sleep or hibernate to not interrupt the assay. Further, ensure that auto-updates (including any institution-controlled network updates) are set to off while collecting data.</li>
		<li>Check that all monitors are connected and communicating with the software. Confirm that each monitor&#39;s status is green under the current data tab. 
		NOTE: See the DAMSystem3 Software Datasheet for details on what problems different color codes refer to.</li>
		<li>The data acquisition software will automatically write the activity counts to text files in the Data folder within the system files. To make data analysis easier, delete any text files within this Data folder prior to starting the data acquisition software for any assay. 
		NOTE: The text files will automatically populate, so it is permissible to remove them from this folder to clear out old data without impeding the program.</li>
	</ol>
	</li>
</ol>
4. Heat tolerance assay
<ol>
	<li>Load the monitors into the assay incubator.</li>
	<li>Start the acquisition software and let the software index for a defined length of time (e.g., 40 indexes or 10 min if the reading interval is set to 15 s) before applying the heat stress. 
	NOTE: This allows for some acclimation to the tubes and recovery from the movement of the monitors while setting them up. Especially if activity during the assay is being analyzed, this acclimation time will establish a baseline activity prior to the induction of heat stress. Depending on the specific nature of the assay being performed, this acclimation phase could be skipped, with the monitors placed directly into the incubator already at the stress temperature.</li>
	<li>If performing a static (TKD) assay, set the incubator to ramp as quickly as possible to the set noxious temperature after the period of acclimation. The response variable will be operationalized as the last recorded time of movement (i.e., last non-zero index) to estimate TKD.
	<ol>
		<li>If performing a dynamic assay, determine the rate at which the temperature will increase after the acclimation period. Here the response variable measured is technically time again. The time at which the last movement is recorded will coincide with a temperature (CTmax), depending on the rate of ramping.</li>
		<li>Monitor activity counts in real time on the DAM system display in the acquisition software or directly examine the text files in the data folder in the DAMSystem3 program files. Copy the text files and open the copy as opposed to the original file within the data folder to avoid causing any problems with live data recording.</li>
		<li>After no movement is seen in any of the flies for several minutes, stop the acquisition software. 
		NOTE: With our assays, we have found that a few to several minutes of inactivity is indicative of physiological collapse due to heat stress similar to that seen in the classic manual TKD assays. This is, however, dependent on the spontaneous escape behavior. As such, consider possible confounding variables if the specific treatment in the investigation might alter this behavioral response. Further, depending on the specific parameters of the assay (e.g., temperature, treatment), the length of the assay will obviously vary.</li>
	</ol>
	</li>
</ol>
5. Data organization and analysis
NOTE: See Supplemental Video S1 for a walkthrough of how to download the executable application from the GitHub, as well basic functionality of the software.
<ol>
	<li>Once the data has been acquired, scan the text files for errors using the referenced software (see the Table of Materials) and select specific start and stop points for binning activity data. If a 10 min acclimation interval was implemented, start the binning 10 min into the activity recording. See the discussion for further details about binning.</li>
	<li>Open the HoTDAM! analysis software and import the scanned monitor data files by clicking File | load monitor data. 
	NOTE: The software and executable application are available from GitHub (https://github.com/MatthewR47/HoTDAM) or from the company&#8217;s website within&#160;Analysis Software section. Note that the executable application is only compatible with Windows operating systems at this time.</li>
	<li>Add group designations to indicate which treatment groups correspond to which cell within the DAM2 monitors. 
	NOTE: The layout of the software interface corresponds to the layout of the monitors.</li>
	<li>Click Start Multi-Group Definition to pull up a dialog box to add a group designation that will be applied to multiple cells. Once the group designation is accepted, click on the cells to apply the group designation, then click on Stop Multi-Group Designation. 
	NOTE: The software organizes and exports DAMSystem3 data to .csv files to be analyzed in statistics software.</li>
	<li>Export the TKD (i.e., the last non-zero index) for each fly within the monitors to a .csv file by clicking File | Export Knockdown Data | Export All Monitors or Export Selected Monitors. TKD for each fly will be organized in the output by group designation.</li>
	<li>Export activity data for each fly by clicking on File | Export Activity Data | Export All Monitors or Export Selected Monitors to a .csv file, keeping only the timestamp and count data, making the data file easier to work with, while also assigning designated group labels for each fly. 
	NOTE: The first several columns in the DAMSystem3 data files correspond to internal data for the monitors (e.g., light monitoring or error messages) during acquisition. Count data are in columns 11-42 (see the DamSystem3 Data Sheet for details). Exporting the activity data with the analysis software will remove all columns but the timestamp and the count data columns.</li>
	<li>If performing a CTmax experiment, use the TKD to determine the CTmax by using the rate of temperature ramping. 
	<img alt="Equation 1" src="/files/ftp_upload/67814/67814eq01.jpg" style="margin: auto;" /> 
	NOTE: The analysis software was written in an object-oriented manner in C# (source code available on GitHub;&#160;https://github.com/MatthewR47/HoTDAM) such that aspects of the program can be easily modified to allow for customization to meet specific purposes.</li>
</ol>
6. Statistics
NOTE: Many different tests can be used to analyze the TKD data, depending on the specifics of the experimental setup.
<ol>
	<li>Use survival analysis (e.g., Cox regression, Kaplan-Meier) to conceptualize knockdown as an event&#160;that each fly within the assay will experience. 
	NOTE: For a detailed review, see Bradburn&#39;s and Clark&#39;s 4-paper series discussing survival analysis and its implementation5,6,7,8.</li>
	<li>Use ANOVA to assess group differences and compare modalities, especially when validating the automated assay for specific conditions.</li>
	<li>Analyze the TKD data using Kaplan-Meier survival analysis. Split the file by stock such that the analysis is performed separately (e.g., for TRPA1 and w1118 stocks lines). Choose the time variable as TKD (in min), knockdown as the event, pretreatment as the factor, and sex as the strata.</li>
	<li>Perform the log-rank, Breslow, and Tarone-Ware tests to compare pretreatment for each stratum (i.e., sex), for illustrative purposes showing several methods.</li>
	<li>Construct survival plots.</li>
</ol>

High-Throughput Heat Tolerance Measurement in Drosophila Using DAM2 Systems

<ol>
	<li>Cowles, R. B., Bogert, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+preliminary+study+of+the+thermal+requirements+of+desert+reptiles.">A preliminary study of the thermal requirements of desert reptiles.</a> Bull Am Mus Nat Hist. 83, 261-296 (1944).</li><li>Lutterschmidt, W. I., Hutchison, V. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+critical+thermal+maximum:+history+and+critique.">The critical thermal maximum: history and critique.</a> Can J Zool. 75, 1561-1574 (1997).</li><li>J&#248;rgensen, L. B., Malte, H., Overgaard, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+to+assess+Drosophila+heat+tolerance:+Unifying+static+and+dynamic+tolerance+assays+to+predict+heat+distribution+limits.">How to assess Drosophila heat tolerance: Unifying static and dynamic tolerance assays to predict heat distribution limits.</a> Funct Ecol. 33 (4), 629-642 (2019).</li><li>Rokusek, B., 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=HoTDAM!+An+easy-to-use+automated+assay+expands+the+inducible+thermotolerance+phenotype+in+Drosophila+melanogaster:+Heat+hardening+reduces+motility.">HoTDAM! An easy-to-use automated assay expands the inducible thermotolerance phenotype in Drosophila melanogaster: Heat hardening reduces motility.</a> Comp Biochem Physiol A Mol Integr Physiol. 286, 111522 (2023).</li><li>Bradburn, M. J., Clark, T. G., Love, S. B., Altman, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Survival+analysis+part+III:+Multivariate+data+analysis+-+Choosing+a+model+and+assessing+its+adequacy+and+fit.">Survival analysis part III: Multivariate data analysis - Choosing a model and assessing its adequacy and fit.</a> Br J Cancer. 89 (4), 605-611 (2003).</li><li>Bradburn, M. J., Clark, T. G., Love, S. B., Altman, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Survival+analysis+part+II:+Multivariate+data+analysis-+An+introduction+to+concepts+and+methods.">Survival analysis part II: Multivariate data analysis- An introduction to concepts and methods.</a> Br J Cancer. 89 (3), 431-436 (2003).</li><li>Clark, T. G., Bradburn, M. J., Love, S. B., Altman, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Survival+analysis+part+IV:+Further+concepts+and+methods+in+survival+analysis.">Survival analysis part IV: Further concepts and methods in survival analysis.</a> Br J Cancer. 89 (5), 781-786 (2003).</li><li>Clark, T. G., Bradburn, M. J., Love, S. B., Altman, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Survival+analysis+part+I:+Basic+concepts+and+first+analyses.">Survival analysis part I: Basic concepts and first analyses.</a> Br J Cancer. 89 (2), 232-238 (2003).</li><li>Hazra, A., Gogtay, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biostatistics+series+module+9:+survival+analysis.">Biostatistics series module 9: survival analysis.</a> Indian J Dermatol. 62 (3), 251-257 (2017).</li><li>Stevenson, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+relative+importance+of+behavioral+and+physiological+adjustments+controlling+body+temperature+in+terrestrial+ectotherms.">The relative importance of behavioral and physiological adjustments controlling body temperature in terrestrial ectotherms.</a> Am Nat. 126 (3), 362-386 (1985).</li><li>Grigg, G. C., Beard, L. A., Augee, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+evolution+of+endothermy+and+its+diversity+in+mammals+and+birds.">The evolution of endothermy and its diversity in mammals and birds.</a> Physiol Biochem Zool. 77 (6), 982-997 (2004).</li><li>Soto-Padilla, 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=Thermosensory+perception+regulates+speed+of+movement+in+response+to+temperature+changes+in+Drosophila+melanogaster.">Thermosensory perception regulates speed of movement in response to temperature changes in Drosophila melanogaster.</a> J Exp Biol. 221 (10), jeb174151 (2018).</li><li>Hoffmann, A. A., S&#248;rensen, J. G., Loeschcke, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adaptation+of+Drosophila+to+temperature+extremes:+bringing+together+quantitative+and+molecular+approaches.">Adaptation of Drosophila to temperature extremes: bringing together quantitative and molecular approaches.</a> J Therm Biol. 28 (3), 175-216 (2003).</li><li>Terblanche, J. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ecologically+relevant+measures+of+tolerance+to+potentially+lethal+temperatures.">Ecologically relevant measures of tolerance to potentially lethal temperatures.</a> J Exp Biol. 214 (22), 3713-3725 (2011).</li><li>Rezende, E. L., Castaneda, L. E., Santos, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tolerance+landscapes+in+thermal+ecology.">Tolerance landscapes in thermal ecology.</a> Funct Ecol. 28 (4), 799-809 (2014).</li><li>MacLean, H. J., Hjort Hansen, J., S&#248;rensen, J. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validating+the+automation+of+different+measures+of+high+temperature+tolerance+of+small+terrestrial+insects.">Validating the automation of different measures of high temperature tolerance of small terrestrial insects.</a> J Insect Physiol. 137, 104362 (2022).</li><li>Gioia, A., Zars, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Thermotolerance+and+place+memory+in+adult+Drosophila+are+independent+of+natural+variation+at+the+foraging+locus.">Thermotolerance and place memory in adult Drosophila are independent of natural variation at the foraging locus.</a> J Comp Physiol A. 195, 777-782 (2009).</li><li>Kj&#230;rsgaard, 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=The+effect+of+developmental+temperature+fluctuation+on+wing+traits+and+stressed+locomotor+performance+in+Drosophila+melanogaster,+and+its+dependence+on+heterozygosity.">The effect of developmental temperature fluctuation on wing traits and stressed locomotor performance in Drosophila melanogaster, and its dependence on heterozygosity.</a> Evol Ecol Res. 14 (7), 803-819 (2012).</li><li>Bak, N. K., Rohde, P. D., Kristensen, T. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Strong+sex-dependent+effects+of+malnutrition+on+life-+and+healthspan+in+Drosophila+melanogaster.">Strong sex-dependent effects of malnutrition on life- and healthspan in Drosophila melanogaster.</a> Insects. 15 (1), 9 (2023).</li><li>Bettencourt, B. 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=Natural+variation+in+Drosophila+stressed+locomotion+meets+or+exceeds+variation+caused+by+Hsp70+mutation:+analysis+of+behavior+and+performance.">Natural variation in Drosophila stressed locomotion meets or exceeds variation caused by Hsp70 mutation: analysis of behavior and performance.</a> Behav Genet. 393, 306-320 (2009).</li><li>Kj&#230;rsgaard, 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=Locomotor+activity+of+Drosophila+melanogaster+in+high+temperature+environments:+plastic+and+evolutionary+responses.">Locomotor activity of Drosophila melanogaster in high temperature environments: plastic and evolutionary responses.</a> Clim Res. 43 (1-2), 127-134 (2010).</li></ol>

The authors declare that they have no conflicts of interest.

The heat tolerance assay method we describe here is versatile and scalable. We have previously published a validation study where we compared the HoTDAM! method to a classic, observation-based TKD assay and found the automated assay to give show the same general trend across several factors4 (Figure 3). In other words, in the same way as the classic manual TKD assay, the DAM2 automated assay was able to differentiate organisms by sex, assay temperature, hardening pret...

Ectotherms typically respond to heat stress with increased locomotor activity. This phenomenon has been apparent to researchers for decades, with the characteristic behavioral response described by Cowles and Bogert in 19441. They described how an organism under heat stress will first show increased locomotor movement. As the heat stress builds, short bursts of activity are interspersed with periods of inactivity. The temperature at which the organism can no longer show coordinated movement is the critical thermal maximum (CTmax). Muscular spams follow, and ultimately the organism collapses1,2. This collapse&#160;is difficult to define and represents something akin to &#34;heat rigor, coma, or death&#34;2. Here we will use the term physiological collapse&#160;to theoretically refer to this blurry endpoint of heat stress.
Drosophila melanogaster and other small insects have been valuable models to study heat stress. To estimate at least a portion of the complex collection of traits that constitute heat tolerance, many researchers have manually observed the time and the temperature at which physiological collapse occurs, representing the time to knockdown (TKD) and CTmax, respectively. While very well established, these manual assay methods suffer some drawbacks. An operational definition of physiological collapse can be difficult to establish and apply appropriately to all cases, especially when observers are less experienced. For example, at what point does the organism go from muscular spasms to collapse? The pattern of muscular spasms and seizure activity prior to collapse can be unpredictable and can complicate accurate observation2,3, threatening accuracy and precision. Meanwhile, the difficulty in observation also limits the number of organisms that can be assayed at one time, limiting scalability.
Since an increase in activity is a consistent response to heat and TKD and CTmax are ultimately the point at which activity ceases, we sought to employ the Drosophila Activity Monitors (DAM2) from TriKinetics to automate heat tolerance assays. We recently published a method for an automated assay, along with easy-to-use software, using the DAM2 system4. The assay was validated by comparing measures of heat tolerance in terms of TKD to a classic manual observation-based TKD assay across several factors. We also explored the locomotor activity component of TKD assays to further characterize the inducible thermotolerance phenotype. We named the assay and accompanying software HoTDAM! (Heat Tolerance assays using the Drosophila Activity Monitoring System). Here we provide a detailed description of the automated heat tolerance assay method using the DAM2 system and the HoTDAM! software. The assay is easy to use and is readily scalable to allow for the measurement of many organisms at one time.
In this manuscript, we performed TKD assays on thermosensory mutant flies (transient receptor potential ankyrin 1;&#160;TRPA1) and their genetic controls (white1118; w1118). These organisms were chosen to emphasize the importance of the characteristic increased activity seen during heat stress for the assay. Namely, TRPA1 organisms do not show this escape behavior, illustrating the intrinsic connection between conserved behavioral responses and estimates of heat tolerance, such as TKD. We performed the assay for both females and males, along with implementing a heat-hardening pretreatment. The representative results presented here are data from entirely new assays than those used in the original validation assays published previously.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>14 mL polystyrene test tubes</td>
			<td>Falcon</td>
			<td>352057</td>
			<td></td>
		</tr>
		<tr>
			<td>30 gallon fish tank</td>
			<td>Wal-mart</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>8 oz bottles</td>
			<td>Genesee</td>
			<td>32-129F</td>
			<td></td>
		</tr>
		<tr>
			<td>Constant Climate Chamber</td>
			<td>Memmert</td>
			<td>HPP750eco</td>
			<td></td>
		</tr>
		<tr>
			<td>cornmeal</td>
			<td>Lab Scientific</td>
			<td>FLY801010</td>
			<td></td>
		</tr>
		<tr>
			<td>DAM2&#160; Drosophila Activity Monitor</td>
			<td>TriKinetics</td>
			<td>DAM2</td>
			<td>(DAMSystem3 Data Sheet) https://www.trikinetics.com/Downloads/DAMSystem%20Price%20List%202024.7.pdf</td>
		</tr>
		<tr>
			<td>DAMSystem data acquisition software</td>
			<td>TriKinetics</td>
			<td></td>
			<td>free download</td>
		</tr>
		<tr>
			<td>Drosophila agar</td>
			<td>Lab Scientific</td>
			<td>FLY80201</td>
			<td></td>
		</tr>
		<tr>
			<td>ethanol</td>
			<td>Fisher Scientific</td>
			<td>BP82011</td>
			<td></td>
		</tr>
		<tr>
			<td>Ether</td>
			<td>Fisher Scientific</td>
			<td>E134-4</td>
			<td></td>
		</tr>
		<tr>
			<td>FileScan software</td>
			<td>TriKinetics</td>
			<td></td>
			<td>for scanning for text errors, binning data, and output</td>
		</tr>
		<tr>
			<td>FlyStuff Flugs for bottles</td>
			<td>Genesee</td>
			<td>49-100</td>
			<td></td>
		</tr>
		<tr>
			<td>FlyStuff Flugs for vials</td>
			<td>Genesee</td>
			<td>49-102</td>
			<td></td>
		</tr>
		<tr>
			<td>FlyStuff vials</td>
			<td>Genesee</td>
			<td>32-113RL</td>
			<td></td>
		</tr>
		<tr>
			<td>HoTDAM software</td>
			<td>Github or Trikinetics</td>
			<td></td>
			<td>https://github.com/MatthewR47/HoTDAM</td>
		</tr>
		<tr>
			<td>Immersion circulating heater</td>
			<td>PolyScience</td>
			<td>MX-CA11B</td>
			<td></td>
		</tr>
		<tr>
			<td>molasses</td>
			<td>Lab Scientific</td>
			<td>FLY80084</td>
			<td></td>
		</tr>
		<tr>
			<td>propionic acid</td>
			<td>Fisher Scientific</td>
			<td>A258-500</td>
			<td></td>
		</tr>
		<tr>
			<td>Pyrex Glass tubes 5 x 65 mm for DAM2</td>
			<td>TriKinetics</td>
			<td>PGT 5x65</td>
			<td>https://www.trikinetics.com/Downloads/DAMSystem%20Price%20List%202024.7.pdf</td>
		</tr>
		<tr>
			<td>small paint brush</td>
			<td>Wal-mart</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>SPSS Statistics</td>
			<td>IBM</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>tegosept</td>
			<td>Lab Scientific</td>
			<td>FLY55015</td>
			<td></td>
		</tr>
		<tr>
			<td>torula yeast</td>
			<td>MP Biomedicals</td>
			<td>290308505</td>
			<td></td>
		</tr>
		<tr>
			<td>TRPA1 mutant stock</td>
			<td>Bloomington Stock center</td>
			<td>26504</td>
			<td>w[1118]; TI{w[+mW.hs]=TI}TrpA1[1]</td>
		</tr>
		<tr>
			<td>w1118 stock</td>
			<td>Bloomington Stock center</td>
			<td>3605</td>
			<td></td>
		</tr>
	</tbody>
</table>

heat tolerance assays using the drosophila activity monitor system: a guide to an executable application for data analysis

The study of heat tolerance in&#8239;Drosophila melanogaster has been of particular interest to researchers for decades, with a common approach to assessing heat tolerance being to monitor the time to knockdown (TKD) after exposure to an elevated temperature. Classically, flies are housed in individual vials and placed inside a heated water bath. TKD is then monitored manually by researchers. While very well-established, there remain problems of subjectivity and consistent application of a tangible definition of cessation of all movement, including muscular spasms, when implementing these manual assays. We have developed a high-throughput method for automating heat tolerance assays using the TriKinetics Drosophila Activity Monitors (DAM2). To accompany the DAM2 system, we have written a program and created an easy-use executable to automatically read the last time of movement from the activity data generated. This script then writes to a .csv file the time to heat paralysis (TKD) for each fly. Our data show that this automated DAM2 method is consistent and reliable. Meanwhile, activity profiles created from the activity count data are of interest. These activity profiles can be compiled and have the potential to expand heat tolerance assays to include the relatively unstudied behavioral components of heat tolerance. This protocol will describe in detail how to use the DAM2 system and the HoTDAM! software to estimate heat tolerance in D. melanogaster.

The analyses for TRPA1 and w1118&#160;stocks were performed separately. Percentile TKD times and other descriptives can be found in Table 1.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td></td>
			<td></td>
			<td colspan="6">Percentilesa</td>
		</tr>
		<tr>
			<td>Sex</td>
			<td>Treatment</td>
			<td></td>
			<td colspan="2">25.00%</td>
			<td colspan="2">50.00%</td>
			<td colspa...

Heat Tolerance Assays Using the Drosophila Activity Monitor System: A Guide to an Executable Application for Data Analysis

This report describes a method for measuring adult Drosophila melanogaster time to knockdown using a Drosophila Activity Monitor (DAM2) in response to an air conduction heat stressor within an incubator chamber. The DAM2 measures activity by recording individual fly movements as they cross an infrared beam. Data analysis is facilitated by a novel executable file created by the authors.

The study of heat tolerance in&#8239;Drosophila melanogaster has been of particular interest to researchers for decades, with a common approach to ...

heat-tolerance-assays-using-drosophila-activity-monitor-system-guide

Research

JoVE Journal

Behavior

338 Views.  University of Nebraska at Kearney. This report describes a method for measuring adult Drosophila melanogaster time to knockdown using a Drosophila Activity Monitor (DAM2) in response to an air conduction heat stressor within an incubator chamber. The DAM2 measures activity by recording individual fly movements as they cross an infrared beam. Data analysis is facilitated by a novel executable file created by the authors.

Video: Heat Tolerance Assays Using the Drosophila Activity Monitor System: A Guide to an Executable Application for Data Analysis

Contextual and Cued Fear Conditioning Test Using a Video Analyzing System in Mice

The contextual and cued fear conditioning test is one of the behavioral tests that assesses the ability of mice to learn and remember an association between environmental cues and aversive experiences. In this test, mice are placed into a conditioning chamber and are given parings of a conditioned stimulus (an auditory cue) and an aversive unconditioned stimulus (an electric footshock). After a delay time, the mice are exposed to the same conditioning chamber and a differently shaped chamber with presentation of the auditory cue. Freezing behavior during the test is measured as an index of fear memory. To analyze the behavior automatically, we have developed a video analyzing system using the ImageFZ application software program, which is available as a free download at http://www.mouse-phenotype.org/. Here, to show the details of our protocol, we demonstrate our procedure for the contextual and cued fear conditioning test in C57BL/6J mice using the ImageFZ system. In addition, we validated our protocol and the video analyzing system performance by comparing freezing time measured by the ImageFZ system or a photobeam-based computer measurement system with that scored by a human observer. As shown in our representative results, the data obtained by ImageFZ were similar to those analyzed by a human observer, indicating that the behavioral analysis using the ImageFZ system is highly reliable. The present movie article provides detailed information regarding the test procedures and will promote understanding of the experimental situation.

This article presents a protocol for a contextual and cued fear conditioning test using a video analyzing system to assess fear learning and memory in mice.

The contextual and cued fear conditioning test is one of the behavioral tests that assesses the ability of mice to learn and remember an association ...

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Adolescence is a critical developmental period where the early symptoms of schizophrenia frequently emerge. First-degree relatives of people with schizophrenia who are at familial high risk (FHR) may show similar cognitive and emotional changes. However, the neurological changes underlying the emergence of these symptoms remain unclear. This study sought to identify differences in frontal, striatal, and limbic regions in children and adolescents with FHR using functional magnetic resonance imaging. Groups of 21 children and adolescents at FHR and 21 healthy controls completed an emotional oddball task that relied on selective attention and the suppression of task-irrelevant emotional information. The standard oddball task was modified to include aversive and neutral distractors in order to examine potential group differences in both emotional and executive processing. This task was designed specifically to allow for children and adolescents to complete by keeping the difficulty and emotional image content age-appropriate. Furthermore, we demonstrate a technique for suitable fMRI registration for children and adolescent participants. This paradigm may also be applied in future studies to measure changes in neural activity in other populations with hypothesized developmental changes in executive and emotional processing.

This paper describes how to use the emotional oddball task and fMRI to measure brain activation in children and adolescents at familial high risk for schizophrenia (FHR).&#160;FMRI was used to measure differences in fronto-striato-limbic regions during an emotional oddball task. Children with FHR exhibited abnormal functional activation during adolescence.

Adolescence is a critical developmental period where the early symptoms of schizophrenia frequently emerge. First-degree relatives of people with ...

Behavioral and Locomotor Measurements Using an Open Field Activity Monitoring System for Skeletal Muscle Diseases

The open field activity monitoring system comprehensively assesses locomotor and behavioral activity levels of mice. It is a useful tool for assessing locomotive impairment in animal models of neuromuscular disease and efficacy of therapeutic drugs that may improve locomotion and/or muscle function. The open field activity measurement provides a different measure than muscle strength, which is commonly assessed by grip strength measurements. It can also show how drugs may affect other body systems as well when used with additional outcome measures. In addition, measures such as total distance traveled mirror the 6 min walk test, a clinical trial outcome measure. However, open field activity monitoring is also associated with significant challenges: Open field activity measurements vary according to animal strain, age, sex, and circadian rhythm. In addition, room temperature, humidity, lighting, noise, and even odor can affect assessment outcomes. Overall, this manuscript provides a well-tested and standardized open field activity SOP for preclinical trials in animal models of neuromuscular diseases. We provide a discussion of important considerations, typical results, data analysis, and detail the strengths and weaknesses of open field testing. In addition, we provide recommendations for optimal study design when using open field activity in a preclinical trial.

Open field activity levels are used to assess locomotive and behavioral activity levels. This protocol provides a well-designed, standardized protocol to use in preclinical trials for neuromuscular disorders.

The open field activity monitoring system comprehensively assesses locomotor and behavioral activity levels of mice. It is a useful tool for assessing ...

Assessment of Murine Exercise Endurance Without the Use of a Shock Grid: An Alternative to Forced Exercise

Using laboratory mouse models, the molecular pathways responsible for the metabolic benefits of endurance exercise are beginning to be defined. The most common method for assessing exercise endurance in mice utilizes forced running on a motorized treadmill equipped with a shock grid. Animals who quit running are pushed by the moving treadmill belt onto a grid that delivers an electric foot shock; to escape the negative stimulus, the mice return to running on the belt. However, avoidance behavior and psychological stress due to use of a shock apparatus can interfere with quantitation of running endurance, as well as confound measurements of postexercise serum hormone and cytokine levels. Here, we demonstrate and validate a refined method to measure running endurance in na&#239;ve C57BL/6 laboratory mice on a motorized treadmill without utilizing a shock grid. When mice are preacclimated to the treadmill, they run voluntarily with gait speeds specific to each mouse. Use of the shock grid is replaced by gentle encouragement by a human operator using a tongue depressor, coupled with sensitivity to the voluntary willingness to run on the part of the mouse. Clear endpoints for quantifying running time-to-exhaustion for each mouse are defined and reflected in behavioral signs of exhaustion such as splayed posture and labored breathing. This method is a humane refinement which also decreases the confounding effects of stress on experimental parameters.

A method to assess exercise endurance in laboratory mice without the use of a shock grid is demonstrated. This method is a humane refinement that can decrease the confounding effects of stress on experimental parameters.

Using laboratory mouse models, the molecular pathways responsible for the metabolic benefits of endurance exercise are beginning to be defined. The most ...

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 ...

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Functional Near Infrared Spectroscopy (fNIRS) is a neuroimaging technique that uses near-infrared light to monitor brain activity. Based on neurovascular coupling, fNIRS is able to measure the haemoglobin concentration changes secondary to neuronal activity. Compared to other neuroimaging techniques, fNIRS represents a good compromise in terms of spatial and temporal resolution. Moreover, it is portable, lightweight, less sensitive to motion artifacts and does not impose significant physical restraints. It is therefore appropriate to monitor a wide range of cognitive tasks (e.g., auditory, gait analysis, social interaction) and different age populations (e.g., new-borns, adults, elderly people). The recent development of fiberless fNIRS devices has opened the way to new applications in neuroscience research. This represents a unique opportunity to study functional activity during real-world tests, which can be more sensitive and accurate in assessing cognitive function and dysfunction than lab-based tests. This study explored the use of fiberless fNIRS to monitor brain activity during a real-world prospective memory task. This protocol is performed outside the lab and brain haemoglobin concentration changes are continuously measured over the prefrontal cortex while the subject walks around in order to accomplish several different tasks.

Monitoring brain activity outside the lab without physical constraints presents methodological challenges. A fiberless, wearable functional Near Infrared Spectroscopy (fNIRS) system was used to measure brain activity during an ecological prospective memory task. It was demonstrated that this system could be used to monitor brain activity during non-lab based experiments.

Functional Near Infrared Spectroscopy (fNIRS) is a neuroimaging technique that uses near-infrared light to monitor brain activity. Based on neurovascular ...

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Human pain models are useful in the assessing the analgesic effect of drugs, providing information about a drug&#39;s pharmacology and identify potentially suitable therapeutic populations. The need to use a comprehensive battery of pain models is highlighted by studies whereby only a single pain model, thought to relate to the clinical situation, demonstrates lack of efficacy. No single experimental model can mimic the complex nature of clinical pain. The integrated, multi-modal pain task&#160;battery presented here encompasses the electrical stimulation task, pressure stimulation task, cold pressor task, the UVB inflammatory model which includes a thermal task and a paradigm for inhibitory conditioned pain modulation. These human pain models have been tested for predicative validity and reliability both in their own right and in combination, and can be used repeatedly, quickly, in short succession, with minimum burden for the subject and with a modest quantity of equipment. This allows a drug to be fully characterized and profiled for analgesic effect which is especially useful for drugs with a novel or untested mechanism of action.

Human pain models are valuable tools used to assess the analgesic potential of novel compounds and predict their clinical efficacy, especially when used in an integrated manner. Although implementation of these models is complex, with proper execution, the pain models described in this protocol can provide predictive and reliable results.

Human pain models are useful in the assessing the analgesic effect of drugs, providing information about a drug&#39;s pharmacology and identify ...

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis (AFEA) software. Additionally, we demonstrate a method for extracting relevant emotional data output and plotting the emotional response of a population over a specified time frame. By time pairing each participant's treatment response to a control stimulus (baseline), the overall emotional response over time and across multiple participants can be quantified. AFEA is a prospective analytical tool for assessing unbiased response to food and beverages. At present, most research has mainly focused on beverages. Methodologies and analyses have not yet been standardized for the application of AFEA to beverages and foods; however, a consistent standard methodology is needed. Optimizing video capture procedures and resulting video quality aids in a successful collection of emotional response to foods. Furthermore, the methodology of data analysis is novel for extracting the pertinent data relevant to the emotional response. The combinations of video capture optimization and data analysis will aid in standardizing the protocol for automated facial expression analysis and interpretation of emotional response data.

A protocol for capturing and statistically analyzing emotional response of a population to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis software is described.

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial ...

Behavioral Disturbances: An Innovative Approach to Monitor the Modulatory Effects of a Nutraceutical Diet

In dogs, diets are often used to modulate behavioral disturbances related to chronic anxiety and stress caused by intense and restless activity. However, the traditional ways to monitor behavioral changes in dogs are complicated and not efficient. In the current clinical evaluation, a new, simple monitoring system was used to assess the effectiveness of a specific diet in positively modulating the intense and restless activity of 24 dogs of different ages and breeds. This protocol describes how to easily and rapidly evaluate improvement in a set of symptoms related to generalized anxiety by using a specific sensor, a mobile phone app, a wireless router, and a computer. The results showed that dogs treated with specific diets showed significant improvement in the times spent active and at rest after 10 days (p &lt; 0.01 and p &lt; 0.05, respectively). These dogs also showed an overall significant improvement in clinical and behavioral symptoms. A specific sensor, along with its related hardware, was demonstrated to successfully monitor behavioral changes relating to movement in dogs.

We report a simple approach to evaluate the effectiveness of a specific diet in positively modulating the daily activity and clinical and behavioral symptoms of dogs with evident behavioral disturbances.

In dogs, diets are often used to modulate behavioral disturbances related to chronic anxiety and stress caused by intense and restless activity. However, ...

An Adjustable High-Definition Imaging System for Behavioral Studies of Drosophila Adults

Drosophila melanogaster is a very powerful model in biological research, but a bad model for photography or videography. This paper describes a simple but effective method to observe and document the behavior or morphology of flies. Flies were placed in a translucent observation chamber c.a. &#216;15 x 5mm (no food inside) or &#216;15 x 12 mm (with an 8 mm-high piece of food inside). After covering with an ultraviolet (UV)/clear filter with high light transmittance, the chamber was placed under a 5-50x zoom stereo microscope, and mini light-emitting diode (LED) video lights were placed on both sides of the microscope to illuminate the chamber to obtain uniform, soft, bright, and nearly shadow-free light. Then, a compact digital camera with 3-5x optical zoom, which can record 1080 P high-definition or higher resolution video (at a frame rate of &#8805;30 fps), was connected to the eyepiece of microscope through a bracket, and photographs or videos were taken through the eyepiece. By adjusting the zoom knob of the zoom stereo microscope, it was very easy to track the flies and take panoramic or detailed close-up images as needed, while the camera recorded everything under the microscope. Because the flies can stay at any position in the chamber, they can be observed and recorded from all directions. The photographs or videos taken are of good image quality. This method can be used both for scientific research and teaching.

An Adjustable High-Definition Imaging System for Behavioral Studies of Drosophila Adults

This protocol describes how to make a simple adult Drosophila behavior observation chamber, and how to take high-definition photographs/videos of the morphology or behavior of different types of fruit flies in the observation chamber through relatively simple and affordable methods.

Drosophila melanogaster is a very powerful model in biological research, but a bad model for photography or videography. This paper describes a simple but ...