This work was supported by a special launch fund from Soochow University and the National Science Foundation of China (NSFC) (82171414). We thank Prof. Chunfeng Liu&#39;s lab members for their discussion and comments.

W1118 adult flies and third instar larvae were used for the present study.
1. Experimental preparation
NOTE: An open-field arena for Drosophila locomotion tracking is made withacolorless and odorless silica gel.
<ol>
	<li>Mix reagent A and reagent B at a ratio of 1:10, according to the manufacturer&#39;s instructions for the silica kit (see Table of Materials). Ensure that sodium bicarbonate is added to the mixture by stirring until the color changes to white. Transfer the mixture to a clean Petri dish and place it in an oven at 40 &#176;C for drying for 48 h.</li>
	<li>Set the HD camera (see Table of Materials) on a tripod, adjusting it so that the camera lens is perpendicular to the surface of the silica arena. Adjusting the focal length and the apertures of the camera, ensure that the camera is focused on the surface of the silica and that the display is adequately illuminated. The experimental setup is illustrated in Figure 1.</li>
	<li>Transfer a fly into the open-field arena to record a continuous video of at least 61 s. 
	NOTE: Considering the sluggish nature of larvae, a video recording time of more than 10 min is recommended.
	<ol>
		<li>Open the video with Fiji, drag the progress bar to the initial frame, and tacitly approve. Choose the whole body of the fly using the &#34;freehand selection&#34; tool (Figure 2B,C).</li>
		<li>Click image &#62; adjust &#62; brightness &#38; contrast to adjust the white balance until the gray value of the selected area approaches the broad background (Figure 2D-F). 
		&#8203;NOTE: Background homogenization of the first frame enables the software to distinguish the background without any objects and create a contrast when a fly is present, thus allowing the software to track it.</li>
	</ol>
	</li>
	<li>Perform the entire experiment in a testing environment set at 25 &#176;C and 60% relative humidity, in an area that is quiet and devoid of exposure to bright light.</li>
</ol>
2. Video recording and preprocessing
<ol>
	<li>After a short period of anesthesia using95%carbon dioxide (CO2), transfer a fly to the open-filed arena and press the record button on the camera application to start video recording. 
	NOTE: To minimize the effect of the anesthetic on locomotion, allow the flies to recover for 10 min before initiating video recording. Cool-anesthetizing by chilling is also recommended.
	<ol>
		<li>Once the flies recover from the anesthesia, put the arena dish containing the fly under the camera and shake the plate quickly from side to side to ensure that the fly is in motion when the recording begins.</li>
	</ol>
	</li>
	<li>Upon completion of the recording, press the stop button to terminate the video recording. 
	NOTE: Ensure that the video recording time slightly exceeds the destination tracking time by a small margin. In addition, to improve the experimental efficiency, it is possible to track multiple flies spontaneously. This depends on the resolution of the camera to enable a high-quality video crop.</li>
	<li>Convert the recorded videos into AVI format with MJPEG encoding, so they can be opened and analyzed using Fiji. Meanwhile, set the frames per second (fps) rate of the video to 15 fps for adult flies and 12 fps for larvae.</li>
</ol>
3. Video analysis
<ol>
	<li>Open the video that has been transformed with &#34;use virtual stack&#34; and &#34;convert to grayscale&#34;, two options in the popup window when opening the video with Fiji (Figure 2A).</li>
	<li>Make a blank first frame, as mentioned above.</li>
	<li>Obtain a processing window by using the &#34;set active image&#34; tool of the AnimalTracker plugin and create a tracking area that circles the arena in the original video window using the &#34;oval&#34; tool (Figure 3A).</li>
	<li>Set the filters (Figure 3A,3) and the parameters of the two filters (Figure 4A-G) for the first blank frame in the processing window. Then, select the next frame in the original video window, and choose the filtered surface of the processing window (Figure 5A-C). 
	NOTE: The filtering step serves to decrease image noise and/or remove the background, thus making it simpler to separate the foreground from the background in the binarization of the frames.</li>
	<li>Once a filtered processing window is selected, turn the tracked fly with a red profile covered in the processing window by using the &#34;set threshold&#34; tool (Figure 3A,4, Figure 5D-E, and Figure 6A).</li>
	<li>Use the &#34;set blob-detector&#34; to let the computer recognize the fly with a red profile covered in the processing window (Figure 3A,5 and Figure 6B).</li>
	<li>Set frame 901 as the last frame for the adult fly, calculated by the video&#39;s recording duration and fps (Figure 3A,6, Figure 6C). 
	NOTE: The following experiment with larvae has been tracked for 10 min, so frame 7200 is set as the last frame.</li>
	<li>Use the &#34;show blobs&#34; tool to present a tracking rectangle in the original video window (Figure 3A,7 and Figure 6D,E). Then, start the tracking and export the tracking file after the monitoring is completed (Figure 3A,8,9 and Figure 7A,B).</li>
</ol>
4. Tracking file analysis
<ol>
	<li>Load the track and zone files using the Animal tracker &#62; Tracking analyzer plugin (Figure 8A).</li>
	<li>Select the desired index using zone settings and alter the parameter settings (Figure 8). Calculate the time of the frame interval using the frame rate. 
	NOTE: In this condition, the frame rate is 15 fps, and the frame interval is approximately 0.067 s, which is the default setting (Figure 8D).</li>
	<li>Produce the quantitative analysis charts using the spreadsheet software and GraphPad Prism after being analyzed in tracking analyzer (Figure 9).</li>
</ol>
5. Analysis per frame
<ol>
	<li>Perform speed analysis per frame interval. Analyze the track file without Fiji if more detailed research is needed.
	<ol>
		<li>Open the track file, copy all coordinates to Microsoft Office Excel, and split the cells using the space key. 
		NOTE: For example, once the file has been divided into &#34;C&#34; and &#34;D&#34; columns, the speed of Drosophila per frame interval is calculated by the formula SQRT((C5-C4)^2+(D5-D4)^2), which is shown in the &#34;E&#34; column (Figure 10A). The data in column &#34;E&#34; indicates the number of pixels that the fly moved between two frames, with the first frame not being considered. Select all calculated results and insert a line chart to exhibit an intuitive fly movement speed per frame interval, with a peak on the line chart (Figure 10B).</li>
	</ol>
	</li>
	<li>Calculate the immobility time per frame interval. After the file has been split into &#34;C&#34; and &#34;D&#34; columns, calculate the immobility status of Drosophila per frame interval using the formula IF(SQRT((C6-C5)^2+(D6-D5)^2) &#60;20, 0, 1), which is shown in the &#34;E&#34; column. (Figure 10C). 
	NOTE: Unlike speed analysis, the results of the first frame were defined. Flies that moved fewer than 20 pixels were considered immobile and recorded as &#34;0&#34; in column &#34;E&#34;.
	<ol>
		<li>Select all calculated results and insert a column chart to visually exhibit the immobility time by the margin of the whole column chart (Figure 10D).</li>
	</ol>
	</li>
	<li>Ensure that the angle of direction changes. 
	NOTE: The angle of direction change analysis represents the flies&#39; direction choice. Once the file has been split into &#34;C&#34; and &#34;D&#34; columns, the angle of direction change is calculated by the formula ACOS(((SQRT((C7-C6)^2+(D7-D6)^2))^2+(SQRT((C6-C5)^2+(D6-D5)^2))^2-(SQRT((C7-C5)^2+(D7-D5)^2))^2)/(2*SQRT((C6-C5)^2+(D6-D5)^2))*(SQRT((C7-C6)^2+(D7-D6)^2)))*180/PI(), which is presented in the &#34;E&#34; column (Figure 10E). The calculated results indicates the angle between three coordinates.
	<ol>
		<li>Select all calculated results and insert a scatter diagram to illustrate the angle of direction change of the flies&#39; movement (Figure 10F).</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Ham, S. 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=Loss+of+UCHL1+rescues+the+defects+related+to+Parkinson's+disease+by+suppressing+glycolysis.">Loss of UCHL1 rescues the defects related to Parkinson's disease by suppressing glycolysis.</a> Science Advances. 7 (28), (2021).</li><li>Algarve, T. D., Assmann, C. E., Aigaki, T., da Cruz, I. B. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Parental+and+preimaginal+exposure+to+methylmercury+disrupts+locomotor+activity+and+circadian+rhythm+of+adult+Drosophila+melanogaster.">Parental and preimaginal exposure to methylmercury disrupts locomotor activity and circadian rhythm of adult Drosophila melanogaster.</a> Drug and Chemical Toxicology. 43 (3), 255-265 (2020).</li><li>Jones, M. A., Grotewiel, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Drosophila+as+a+model+for+age-related+impairment+in+locomotor+and+other+behaviors.">Drosophila as a model for age-related impairment in locomotor and other behaviors.</a> Experimental Gerontology. 46 (5), 320-325 (2011).</li><li>Yamaguchi, M., Yoshida, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Drosophila+as+a+model+organism.">Drosophila as a model organism.</a> Advances in Experimental Medicine and Biology. 1076, 1-10 (2018).</li><li>Rothenfluh, A., Heberlein, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Drugs,+files,+and+videotape:+the+effects+of+ethanol+and+cocaine+on+Drosophila+locomotion.">Drugs, files, and videotape: the effects of ethanol and cocaine on Drosophila locomotion.</a> Current Opinion in Neurobiology. 12 (6), 639-645 (2002).</li><li>Tsuda, L., Lim, Y. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Alzheimer's+disease+model+system+using+Drosophila.">Alzheimer's disease model system using Drosophila.</a> Advances in Experimental Medicine and Biology. 1076, 25-40 (2018).</li><li>Dung, V. M., Thao, D. T. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Parkinson's+disease+model.">Parkinson's disease model.</a> Advances in Experimental Medicine and Biology. 1076, 41-61 (2018).</li><li>Liguori, F., Amadio, S., Volonte, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fly+for+ALS:+Drosophila+modeling+on+the+route+to+amyotrophic+lateral+sclerosis+modifiers.">Fly for ALS: Drosophila modeling on the route to amyotrophic lateral sclerosis modifiers.</a> Cellular and Molecular Life Sciences. 78 (17-18), 6143-6160 (2021).</li><li>Cao, W., 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=An+automated+rapid+iterative+negative+geotaxis+assay+for+analyzing+adult+climbing+behavior+in+a+Drosophila+model+of+neurodegeneration.">An automated rapid iterative negative geotaxis assay for analyzing adult climbing behavior in a Drosophila model of neurodegeneration.</a> Journal of Visualized Experiments. (127), 56507 (2017).</li><li>Eidhof, I., 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=High-throughput+analysis+of+locomotor+behavior+in+the+Drosophila+island+assay.">High-throughput analysis of locomotor behavior in the Drosophila island assay.</a> Journal of Visualized Experiments. (129), 55892 (2017).</li><li>Scaplen, K. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+real-time+quantification+of+group+locomotor+activity+in+Drosophila+melanogaster.">Automated real-time quantification of group locomotor activity in Drosophila melanogaster.</a> Scientific Reports. 9 (1), 4427 (2019).</li><li>Werkhoven, Z., Rohrsen, C., Qin, C., Brembs, B., de Bivort, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MARGO+(Massively+Automated+Real-time+GUI+for+Object-tracking),+a+platform+for+high-throughput+ethology.">MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology.</a> PLoS One. 14 (11), e0224243 (2019).</li><li>Gargano, J. W., Martin, I., Bhandari, P., Grotewiel, M. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+iterative+negative+geotaxis+(RING):+a+new+method+for+assessing+age-related+locomotor+decline+in+Drosophila.">Rapid iterative negative geotaxis (RING): a new method for assessing age-related locomotor decline in Drosophila.</a> Experimental Gerontology. 40 (5), 386-395 (2005).</li><li>Cichewicz, K., Hirsh, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ShinyR-DAM:+a+program+analyzing+Drosophila+activity,+sleep+and+circadian+rhythms.">ShinyR-DAM: a program analyzing Drosophila activity, sleep and circadian rhythms.</a> Communications Biology. 1, 25 (2018).</li><li>McParland, A. L., Follansbee, T. L., Ganter, G. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measurement+of+larval+activity+in+the+Drosophila+activity+monitor.">Measurement of larval activity in the Drosophila activity monitor.</a> Journal of Visualized Experiments. 98, e52684 (2015).</li><li>Walter, T., Couzin, I. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TRex,+a+fast+multi-animal+tracking+system+with+markerless+identification,+and+2D+estimation+of+posture+and+visual+fields.">TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields.</a> eLife. 10, (2021).</li><li>Maia Chagas, A., Prieto-Godino, L. L., Arrenberg, A. B., Baden, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+&#8364;100+lab:+A+3D-printable+open-source+platform+for+fluorescence+microscopy,+optogenetics,+and+accurate+temperature+control+during+behaviour+of+zebrafish,+Drosophila,+and+Caenorhabditis+elegans.">The &#8364;100 lab: A 3D-printable open-source platform for fluorescence microscopy, optogenetics, and accurate temperature control during behaviour of zebrafish, Drosophila, and Caenorhabditis elegans.</a> PLoS Biology. 15 (7), e2002702 (2017).</li><li>Nichols, C. D., Becnel, J., Pandey, U. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+to+assay+Drosophila+behavior.">Methods to assay Drosophila behavior.</a> Journal of Visualized Experiments. (61), (2012).</li><li>Xiao, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+to+assay+the+behavior+of+Drosophila+melanogaster+for+toxicity+study.">Methods to assay the behavior of Drosophila melanogaster for toxicity study.</a> Methods in Molecular Biology. 2326, 47-54 (2021).</li><li>Schindelin, 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=Fiji:+an+open-source+platform+for+biological-image+analysis.">Fiji: an open-source platform for biological-image analysis.</a> Nature Methods. 9 (7), 676-682 (2012).</li><li>Johnson, M. E., Bobrovskaya, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+update+on+the+rotenone+models+of+Parkinson's+disease:+their+ability+to+reproduce+the+features+of+clinical+disease+and+model+gene-environment+interactions.">An update on the rotenone models of Parkinson's disease: their ability to reproduce the features of clinical disease and model gene-environment interactions.</a> Neurotoxicology. 46, 101-116 (2015).</li><li>Coulom, H., Birman, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chronic+exposure+to+rotenone+models+sporadic+Parkinson's+disease+in+Drosophila+melanogaster.">Chronic exposure to rotenone models sporadic Parkinson's disease in Drosophila melanogaster.</a> The Journal of Neuroscience. 24 (48), 10993-10998 (2004).</li><li>Kumar, P. P., Bawani, S. S., Anandhi, D. U., Prashanth, K. 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=Rotenone+mediated+developmental+toxicity+in+Drosophila+melanogaster.">Rotenone mediated developmental toxicity in Drosophila melanogaster.</a> Environmental Toxicology and Pharmacology. 93, 103892 (2022).</li><li>Gulyas, M., Bencsik, N., Pusztai, S., Liliom, H., Schlett, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AnimalTracker:+an+ImageJ-based+tracking+API+to+create+a+customized+behaviour+analyser+program.">AnimalTracker: an ImageJ-based tracking API to create a customized behaviour analyser program.</a> Neuroinformatics. 14 (4), 479-481 (2016).</li><li>Qu, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EasyFlyTracker:+a+simple+video+tracking+Python+package+for+analyzing+adult+Drosophila+locomotor+and+sleep+activity+to+facilitate+revealing+the+effect+of+psychiatric+drugs.">EasyFlyTracker: a simple video tracking Python package for analyzing adult Drosophila locomotor and sleep activity to facilitate revealing the effect of psychiatric drugs.</a> Frontiers in Behavioral Neuroscience. 15, 809665 (2022).</li><li>Yarwais, Z. H., Najmalddin, H. O., Omar, Z. J., Mohammed, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+data+collection+of+Drosophila+movement+behaviour+assays+using+computer+vision+in+Python.">Automated data collection of Drosophila movement behaviour assays using computer vision in Python.</a> International Journal of Innovative Approaches in Science Research. 4 (1), 15-22 (2020).</li></ol>

The authors declare that they have no competing financial interests.

We have designed a method, based on the open-source material AnimalTracker API compatible with the Fiji image processing program, that can enable researchers to systematically evaluate locomotor activity by tracking both adult and individual larval flies. AnimalTracke is a tool written in Java that can be easily integrated into existing databases or other tools to facilitate the analysis of application-designed animal-tracking behavior24. Upon a frame-by-frame analysis by a software calculation formula that quantifies the locomotor activity of the adults and larvae, several parameters, including movement speed, distance traveled, immobility, and angle of direction changes can be flexibly analyzed. These parameters, which represent different aspects of behavioral locomotion, can be plotted to illustrate locomotor changes over time. Additionally, by creating a graphical user interface, providing detailed documentation on its usage, and an application programming interface, we aim to make this method&#160;accessible to researchers who lack programming experience and experienced users creating custom experimental paradigms.
To verify that the method can accurately monitor behavior, locomotor testing of adult flies and larvae treated with rotenone, as well as a comparison of their motor activity to that of control flies fed with the drug&#39;s solvent, has been carried out. The Fiji software, with its plugins, is used to analyze the pixel coordinates of each frame in the video recording of the movement, allowing for calculation of the speed, distance, and other parameters of the experimental flies. We observed a significant decrease in distance traveled over time in rotenone administration (Figure 11), which is consistent with reported results23. Meanwhile, the ascending movement speed and abnormal direction chosen have been observed in drug-fed groups, to help illustrate more details of the behavioral deficiency in flies. Given the success in detecting adult flies&#39; locomotor activity, we then sought to evaluate the mobility of larvae (Figure 12). Compared to the control, the results of the tracking larvae fed with rotenone were significantly impaired, paralleling those of adult flies fed with drugs. Experiments with the adults and larvae fed with rotenone suggest that this method can accurately record the reduction of flies that produced locomotor deficits compared to controls.&#160;This report has successfully demonstrated applications of the current method in quantifying and analyzing locomotor ability and other facets of behavior defects of fruit flies in testing models or pharmacological research in animals.
To ensure that the video and tracking analysis yields successful and reproducible results, it is recommended to adhere to the following guidelines. First, for the choice of video frame rate, we recommend converting the recorded video to a format of 15 frames per second (fps). This can not only maintain good motion tracking, but also avoid the slowness of computer analysis caused by vast amounts of data. By improving the video frame rate, the motion trajectory analysis gets more detailed. Secondly, the parameters in the formula can also be adjusted to suit the corresponding experimental scheme when analyzing the static motion between every two frames. For larvae locomotor monitoring, it is essential to use silica gel rather than agar, as the solidified silica gel is tight and the larvae cannot get into it. Moreover, silica gel is transparent and can be dyed by adding color substance to produce optimal background, facilitating desired optical effects that enhance the image quality.
Animal tracking systems are being advanced to provide comprehensive solutions for the etiology, neuroscience, and behavioral genetics communities. Table 1 provides a comparison of the features of multiple tracking programs currently available10,11,12,16,17,25,26. This approach is extremely cost-effective, straightforward to learn, and precise in measuring locomotor behavior, without requiring costly software and equipment. There is no doubt that this method can be conveniently extended to other Drosophila-like animal models, and even to larger animals such as rats and mice. The structure of the AnimalTracker API can be extended with ease through independent ImageJ applications or plugins, offering a broad array of useful toolkits for custom behavior research and analysis24. Nevertheless, this study has certain restrictions. Since a single fly is placed in an open-field arena for image recording and video tracking is conducted individually, this method is inefficient and time-consuming. We have attempted to expand the capacity for recording multiple arenas simultaneously, allowing for up to six individual recordings. It is theoretically possible to record a greater number of Drosophila simultaneously; this depends on the size of the arena and the configuration of the camera. Alternatively, if users wish to extend to recording grouped Drosophila, it is recommended to consider the limited number of single records and a configuration of sufficient quality to identify collisions and overlaps between the flies. Improvements in the testing efficiency by machine learning were not considered in the study, since an affordable and compatible approach has not been figured out that can be integrated with the current system to visually distinguish identities and track models accurately.
In summary, the method described here develops and validates an efficient and straightforward approach based on free, open-source software, designed to reduce time consumption and refine experimental techniques for quantitatively indicating and analyzing Drosophila locomotion in the larvae and adult stages. Through systematic analysis, this method can help us understand how the animal&#39;s velocity changes over time during movement, as well as the characteristics of directional selection. Thus, the incorporation of open-source software into commonly used digital devices provides a reliable way to test locomotor activity in various fly models. This might be useful for evaluating physiological and pathological locomotor outputs in testing neurodegenerative disease models derived from pharmacological treatment and transgenic modification in Drosophila as well as other animals.

Drosophila melanogaster provides an excellent model organism for investigating cellular and molecular functions in neuronal disease models created by gene modification1, drug treatment2, and senescence3. The high conservation of biological pathways, physical properties, and disease-associated homolog genes between humans and Drosophila makes the fruit fly an ideal mimic from the molecular to the behavioral level4. In many disease models, behavioral deficiency is an important index, providing a helpful model for various human neuropathies5,6. Drosophila is now used to study multiple human diseases, neurodevelopment, and neurodegenerative diseases such as Parkinson&#39;s disease and amyotrophic lateral sclerosis7,8. Detecting the motor ability of the disease models is crucial for understanding the pathogenic progress and may provide a phenotypic correlation to the molecular mechanisms underlying the disease process.
Recently, commercially available software tools and cost-effective programs have been developed for Drosophila locomotor detection strategies, such as high-throughput testing in grouped flies9,10 and measuring locomotion in real-time11,12. One such conventional approach is rapid interactive negative geotaxis (RING), also called the climbing assay, which includes multiple channels that allow for a large fly population with the same gender and age to be contained, reducing variation while data collecting9,13. Another pre-testing method for analyzing locomotor behavior is TriKinetics Drosophila activity monitor (DAM), a device that uses multiple beams to detect fly activity movement within a thin glass tube14. The device records position continuously, which represents automated locomotion by calculating the beam-crossings to study the activity and circadian rhythm of flies over a longer period of time15. Although these methods have been widely used in analyzing behavioral defects in fruit flies to determine changes in behavioral locomotion, they always require special testing equipment or complex analysis processes, and restrict their application in some models with a limited, simple device. Animal-tracing group-based strategies for testing the adult Drosophila, such as FlyGrAM11 and the Drosophila island assay10, implement social recruitment and individual tracking in a predefined area. Nevertheless, social individual restriction in defied areas might have a negative effect on identifications in the images, caused by the collision or overlapping of flies. Even though some open-source materials-based methods, such as TRex16, MARGO12, and FlyPi17, have an emergency, they can fast-track trace the flies with flexible usage in behavioral testing. These testing approaches are associated with elaborate experimental apparatus installations, special software requirements, or professional computer languages. For larvae, measuring the total distance traveled across the number of grid border lines per unit of time18, or rough counting the body wall contractions for individuals manually19, are the predominant methods for assessing their locomotor ability. Due to the lack of precision in equipment or devices and analysis methods, some behavioral locomotion of larvae might escape detection, making it difficult to accurately assess behavioral movement, especially fine movement15.
The present developed method utilizes the AnimalTracker application programming interface (API), compatible with the Fiji (ImageJ) image processing program, to systematically evaluate the locomotor activity of both adult and larval flies by analyzing their tracking behavior from high-definition (HD) videos. Fiji is an open-source software ImageJ distribution that can combine robust software libraries with numerous scripting languages, resulting in rapid prototyping of image processing algorithms, making it popular among biologists for its image analysis capabilities20. In the current approach, Fiji&#39;s integration into the AnimalTracker API is exploited to develop a unique Drosophila behavioral assay with personalized algorithm insertion, and provides a useful step for detailed documentation and tutorials to support robust analytical capabilities of locomotor behavior (Figure 1). To circumvent the complication of objective identifications in the images caused by the collision or overlapping of flies, each arena is restricted to hosting only one fly. Upon assessing the tracking precision of the approach, it was implemented to trace and quantify the locomotor movements of Drosophila that were administered with the toxic drug rotenone, which is generally used for animal models of Parkinson&#39;s disease, ultimately discovering locomotion impairment in the drug treatment21. This methodology, which employs open-source and free software, does not necessitate high-cost instrumentation, and can precisely and reproducibly analyze Drosophila behavioral locomotion.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Animal tracker</td>
			<td>Hungarian Brain Research Program</td>
			<td>version: 1.7</td>
			<td>pfficial website: http://animaltracker.elte.hu/main/downloads</td>
		</tr>
		<tr>
			<td>Camera software</td>
			<td>Microsoft</td>
			<td>version: 2021.105.10.0</td>
			<td>built-in windows 10 system</td>
		</tr>
		<tr>
			<td>Computer</td>
			<td>DELL</td>
			<td>Vostro-14-5480</td>
			<td>a comupter running win 10 system is available</td>
		</tr>
		<tr>
			<td>Drosophila carbon dioxide anesthesia workstation</td>
			<td>Wu han Yihong technology</td>
			<td>#YHDFPCO2-018</td>
			<td>official website: http://www.yhkjwh.com/</td>
		</tr>
		<tr>
			<td>Fiji software</td>
			<td>Fiji team</td>
			<td>version: 1.53v</td>
			<td>official website: https://fiji.sc/</td>
		</tr>
		<tr>
			<td>Format factory software</td>
			<td>Pcfreetime</td>
			<td>version: X64 5.4.5</td>
			<td>official website: http://www.pcfreetime.com/formatfactory/CN/index.html</td>
		</tr>
		<tr>
			<td>Graph pad prism</td>
			<td>GraphPad Software</td>
			<td>version: 8.0.2</td>
			<td>official website: https://www.graphpad-prism.cn</td>
		</tr>
		<tr>
			<td>Hight definition camera</td>
			<td>TTQ</td>
			<td>Jingwang2 (HD1080P F1.6 6-60mm)</td>
			<td>official website: http://www.ttq100.com/product_show.php?id=35</td>
		</tr>
		<tr>
			<td>Office software</td>
			<td>Microsoft</td>
			<td>version: office 2019</td>
			<td>official website: https://www.microsoftstore.com.cn/software/office</td>
		</tr>
		<tr>
			<td>Petri dish</td>
			<td>Bkman</td>
			<td>110301003</td>
			<td>size: 60 mm</td>
		</tr>
		<tr>
			<td>Silica gel</td>
			<td>DOW</td>
			<td>SYLGARD 184 Silicone Elastomer Kit</td>
			<td>Mix well according to the instructions</td>
		</tr>
		<tr>
			<td>Sodium bicarbonate</td>
			<td>Macklin</td>
			<td>#144-55-8</td>
			<td>Mix well with silica gel</td>
		</tr>
	</tbody>
</table>

a simple technique to assay locomotor activity in drosophila

Drosophila melanogaster is an ideal model organism for studying various diseases due to its abundance of advanced genetic manipulation techniques and diverse behavioral features. Identifying behavioral deficiency in animal models is a crucial measure of disease severity, for example, in neurodegenerative diseases where patients often experience impairments in motor function. However, with the availability of various systems to track and assess motor deficits in fly models, such as drug-treated or transgenic individuals, an economical and user-friendly system for precise evaluation from multiple angles is still lacking. A method based on the AnimalTracker application programming interface (API) is developed here, which is compatible with the Fiji image processing program, to systematically evaluate the movement activities of both adult and larval individuals from recorded video, thus allowing for the analysis of their tracking behavior. This method requires only a high-definition camera and a computer peripheral hardware integration to record and analyze behavior, making it an affordable and effective approach for screening fly models with transgenic or environmental behavioral deficiencies. Examples of behavioral tests using pharmacologically treated flies are given to show how the techniques can detect behavioral changes in both adult flies and larvae in a highly repeatable manner.

Watch this Scientific Journal Video about A Simple Technique to Assay Locomotor Activity in Drosophila at JoVE.com

A Simple Technique to Assay Locomotor Activity in Drosophila

The present protocol assesses the locomotor activity of Drosophila by tracking and analyzing the movement of flies in a hand-made arena using open-source software Fiji, compatible with plugins to segment pixels of each frame based on high-definition video recording to calculate parameters of speed, distance, etc.

A Simple Technique to Assay Locomotor Activity in Drosophila

Drosophila melanogaster is an ideal model organism for studying various diseases due to its abundance of advanced genetic manipulation techniques and ...

a-simple-technique-to-assay-locomotor-activity-in-drosophila

Nanyang Technological University

Research

JoVE Journal

Behavior

2.8K Views.  Soochow University. The present protocol assesses the locomotor activity of Drosophila by tracking and analyzing the movement of flies in a hand-made arena using open-source software Fiji, compatible with plugins to segment pixels of each frame based on high-definition video recording to calculate parameters of speed, distance, etc.

Video: A Simple Technique to Assay Locomotor Activity in Drosophila

Mouse Short- and Long-term Locomotor Activity Analyzed by Video Tracking Software

Locomotor activity (LMA) is a simple and easily performed measurement of behavior in mice and other rodents. Improvements in video tracking software (VTS) have allowed it to be coupled to LMA testing, dramatically improving specificity and sensitivity when compared to the line crossings method with manual scoring. In addition, VTS enables high-throughput experimentation. While similar to automated video tracking used for the open field test (OFT), LMA testing is unique in that it allows mice to remain in their home cage and does not utilize the anxiogenic stimulus of bright lighting during the active phase of the light-dark cycle. Traditionally, LMA has been used for short periods of time (mins), while longer movement studies (hrs-days) have often used implanted transmitters and biotelemetry. With the option of real-time tracking, long-, like short-term LMA testing, can now be conducted using videography. Long-term LMA testing requires a specialized, but easily constructed, cage so that food and water (which is usually positioned on the cage top) does not obstruct videography. Importantly, videography and VTS allows for the quantification of parameters, such as path of mouse movement, that are difficult or unfeasible to measure with line crossing and/or biotelemetry. In sum, LMA testing coupled to VTS affords a more complete description of mouse movement and the ability to examine locomotion over an extended period of time.

Locomotor activity (LMA) is a simple and easily performed measurement of behavior in mice. Coupling of video tracking software (VTS) and LMA allows for the improvement of specificity and sensitivity, especially when compared with the manual, line crossing method of LMA analysis. Additionally VTS allows long-term tracking of mouse LMA.

Locomotor  activity (LMA) is a simple and easily performed measurement of behavior in mice  and other rodents. Improvements in video tracking software ...

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

A set of behavioral tasks for assessing perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) is presented here with the goal of uncovering rhythm disorders, such as beat deafness. Beat deafness is characterized by poor performance in perceiving durations in auditory rhythmic patterns or poor synchronization of movement with auditory rhythms (e.g., with musical beats). These tasks include the synchronization of finger tapping to the beat of simple and complex auditory stimuli and the detection of rhythmic irregularities (anisochrony detection task) embedded in the same stimuli. These tests, which are easy to administer, include an assessment of both perceptual and sensorimotor timing abilities under different conditions (e.g., beat rates and types of auditory material) and are based on the same auditory stimuli, ranging from a simple metronome to a complex musical excerpt. The analysis of synchronized tapping data is performed with circular statistics, which provide reliable measures of synchronization accuracy (e.g., the difference between the timing of the taps and the timing of the pacing stimuli) and consistency. Circular statistics on tapping data are particularly well-suited for detecting individual differences in the general population. Synchronized tapping and anisochrony detection are sensitive measures for identifying profiles of rhythm disorders and have been used with success to uncover cases of poor synchronization with spared perceptual timing. This systematic assessment of perceptual and sensorimotor timing can be extended to populations of patients with brain damage, neurodegenerative diseases (e.g., Parkinson&#8217;s disease), and developmental disorders (e.g., Attention Deficit Hyperactivity Disorder).

Behavioral tasks that allow for the assessment of perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) are presented. Synchronization of finger tapping to the beat of an auditory stimuli and detecting rhythmic irregularities provides a means of uncovering rhythm disorders.

A set of behavioral tasks for assessing perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) is presented here ...

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 ...

Use of the Open Field Maze to Measure Locomotor and Anxiety-like Behavior in Mice

Animal models have proven to be invaluable to researchers trying to answer questions regarding the mechanisms of behavior. The Open Field Maze is one of the most commonly used platforms to measure behaviors in animal models. It is a fast and relatively easy test that provides a variety of behavioral information ranging from general ambulatory ability to data regarding the emotionality of the subject animal. As it relates to rodent models, the procedure allows the study of different strains of mice or rats both laboratory bred and wild-captured. The technique also readily lends itself to the investigation of different pharmacological compounds for anxiolytic or anxiogenic effects. Here, a protocol for use of the open field maze to describe mouse behaviors is detailed and a simple analysis of general locomotor ability and anxiety-related emotional behaviors between two strains of C57BL/6 mice is performed. Briefly, using the described protocol we show Wild Type mice exhibited significantly less anxiety related behaviors than did age-matched Knock Out mice while both strains exhibited similar ambulatory ability.

A protocol is provided to use an Open Field Maze to access general locomotor activity, anxiety and emotionality in a laboratory mouse model.

Animal models have proven to be invaluable to researchers trying to answer questions regarding the mechanisms of behavior. The Open Field Maze is one of ...

The Treadmill Fatigue Test: A Simple, High-throughput Assay of Fatigue-like Behavior for the Mouse

Fatigue is a prominent symptom in many diseases and disorders and reduces quality of life for many people. The lack of clear pathogenesis and failure of current interventions to adequately treat fatigue in all patients leaves a need for new treatment options. Despite the therapeutic need and importance of preclinical research in helping identify promising novel treatments, few preclinical assays of fatigue are available. Moreover, the most common preclinical assay used to assess fatigue-like behavior, voluntary wheel running, is not suitable for use with some strains of mice, may not be sensitive to drugs that reduce fatigue, and has relatively low throughput. The current protocol describes a novel, non-voluntary preclinical assay of fatigue-like behavior, the treadmill fatigue test, and provides evidence of its efficacy in detecting fatigue-like behavior in mice treated with a chemotherapy drug known to cause fatigue in humans and fatigue-like behavior in animals. This assay may be a beneficial alternative to wheel running, as fatigue-like behavior and potential interventions can be assessed in a greater number of mice over a shorter time frame, thus permitting faster discovery of new therapeutic options.

Fatigue is a common, undertreated and frequently poorly-understood symptom in many diseases and disorders. New preclinical assays of fatigue may help to improve current understanding and future treatment of fatigue. To that end, the current protocol provides a novel means of measuring fatigue-like behavior in the mouse.

Fatigue is a prominent symptom in many diseases and disorders and reduces quality of life for many people. The lack of clear pathogenesis and failure of ...

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

Understanding the mechanisms underlying locomotor learning helps researchers and clinicians optimize gait retraining as part of motor rehabilitation. However, studying human locomotor learning can be challenging. During infancy and childhood, the neuromuscular system is quite immature, and it is unlikely that locomotor learning during early stages of development is governed by the same mechanisms as in adulthood. By the time humans reach maturity, they are so proficient at walking that it is difficult to come up with a sufficiently novel task to study de novo locomotor learning. The split-belt treadmill, which has two belts that can drive each leg at a different speed, enables the study of both short- (i.e., immediate) and long-term (i.e., over minutes-days; a form of motor learning) gait modifications in response to a novel change in the walking environment. Individuals can easily be screened for previous exposure to the split-belt treadmill, thus ensuring that all experimental participants have no (or equivalent) prior experience. This paper describes a typical split-belt treadmill adaptation protocol that incorporates testing methods to quantify locomotor learning and generalization of this learning to other walking contexts. A discussion of important considerations for designing split-belt treadmill experiments follows, including factors like treadmill belt speeds, rest breaks, and distractors. Additionally, potential but understudied confounding variables (e.g., arm movements, prior experience) are considered in the discussion.

We describe a protocol for investigating human locomotor adaptation using the split-belt treadmill, which has two belts that can drive each leg at a different speed. We specifically focus on a paradigm designed to test the generalization of adapted locomotor patterns to different walking contexts (e.g., gait speeds, walking environments).

Understanding the mechanisms underlying locomotor learning helps researchers and clinicians optimize gait retraining as part of motor rehabilitation. ...

A Method to Test the Effect of Environmental Cues on Mating Behavior in Drosophila melanogaster

An individual's sexual drive is influenced by genotype, experience and environmental conditions. How these factors interact to modulate sexual behaviors remains poorly understood. In Drosophila melanogaster, environmental cues, such as food availability, affect mating activity offering a tractable system to investigate the mechanisms modulating sexual behavior. In D. melanogaster, environmental cues are often sensed via the chemosensory gustatory and olfactory systems. Here, we present a method to test the effect of environmental chemical cues on mating behavior. The assay consists of a small mating arena containing food medium and a mating couple. The mating frequency for each couple is continuously monitored for 24 h. Here we present the applicability of this assay to test environmental compounds from an external source through a pressurized air system as well as manipulation of the environmental components directly in the mating arena. The use of a pressurized air system is especially useful to test the effect of very volatile compounds, while manipulating components directly in the mating arena can be of value to ascertain a compound's presence. This assay can be adapted to answer questions about the influence of genetic and environmental cues on mating behavior and fecundity as well as other male and female reproductive behaviors.

A Method to Test the Effect of Environmental Cues on Mating Behavior in Drosophila melanogaster

We demonstrate an assay to analyze the environmental and genetic cues that influence mating behavior in the fruit fly Drosophila melanogaster.

An individual's sexual drive is influenced by genotype, experience and environmental conditions. How these factors interact to modulate sexual behaviors ...

A Computational Method to Quantify Fly Circadian Activity

In most animals and plants, circadian clocks orchestrate behavioral and molecular processes and synchronize them to the daily light-dark cycle. Fundamental mechanisms that underlie this temporal control are widely studied using the fruit fly Drosophila melanogaster as a model organism. In flies, the clock is typically studied by analyzing multiday locomotor recording. Such a recording shows a complex bimodal pattern with two peaks of activity: a morning peak that happens around dawn, and an evening peak that happens around dusk. These two peaks together form a waveform that is very different from sinusoidal oscillations observed in clock genes, suggesting that mechanisms in addition to the clock have profound effects in producing the observed patterns in behavioral data. Here we provide instructions on using a recently developed computational method that mathematically describes temporal patterns in fly activity. The method fits activity data with a model waveform that consists of four exponential terms and nine independent parameters that fully describe the shape and size of the morning and evening peaks of activity. The extracted parameters can help elucidate the kinetic mechanisms of substrates that underlie the commonly observed bimodal activity patterns in fly locomotor rhythms.

A method to quantify the main temporal features seen in fly circadian locomotor rhythms is presented. The quantification is achieved by fitting fly activity with a multi-parametric model waveform. The model parameters describe the shape and size of the morning and evening peaks of daily activity.

In most animals and plants, circadian clocks orchestrate behavioral and molecular processes and synchronize them to the daily light-dark cycle. ...

Application of MultiColor FlpOut Technique to Study High Resolution Single Cell Morphologies and Cell Interactions of Glia in Drosophila

Cells display different morphologies and complex anatomical relationships. How do cells interact with their neighbors? Do the interactions differ between cell types or even within a given type? What kinds of spatial rules do they follow? The answers to such fundamental questions in vivo have been hampered so far by a lack of tools for high resolution single cell labeling. Here, a detailed protocol to target single cells with a MultiColor FlpOut (MCFO) technique is provided. This method relies on three differently tagged reporters (HA, FLAG and V5) under UAS control that are kept silent by a transcriptional terminator flanked by two FRT sites (FRT-stop-FRT). A heat shock pulse induces the expression of a heat shock-induced Flp recombinase, which randomly removes the FRT-stop-FRT cassettes in individual cells: expression occurs only in cells that also express a GAL4 driver. This leads to an array of differently colored cells of a given cell type that allows the visualization of individual cell morphologies at high resolution. As an example, the MCFO technique can be combined with specific glial GAL4 drivers to visualize the morphologies of the different glial subtypes in the adult Drosophila brain.

Application of MultiColor FlpOut Technique to Study High Resolution Single Cell Morphologies and Cell Interactions of Glia in Drosophila

Cells display different morphologies and establish a variety of interactions with their neighbors. This protocol describes how to reveal the morphology of single cells and to investigate cell-cell interaction by using the well-established Gal4/UAS expression system.

Cells display different morphologies and complex anatomical relationships. How do cells interact with their neighbors? Do the interactions differ between ...

A Simple and Low-cost Assay for Measuring Ambulation in Mouse Models of Muscular Dystrophy

Measuring functional outcomes in the treatment of muscular dystrophy is an essential aspect of preclinical testing. The assessment of voluntary ambulation in mouse models is a non-invasive and reproducible activity assay that is directly analogous to measures of patient ambulation such as the 6-minute walk test and related mobility scores. Many common methods for testing mouse ambulation speed and distance are based on the open field test, where an animal&#39;s free movement within an arena is measured over time. One major downside to this approach is that commercial software and equipment for high-resolution motion tracking is expensive and may require transferring mice to specialized facilities for testing. Here, we describe a low-cost, video-based system for measuring mouse ambulation that utilizes free and open-source software. Using this protocol, we demonstrate that voluntary ambulation in the dystrophin-null mdx mouse model for Duchenne muscular dystrophy (DMD) is decreased relative to wild-type mouse activity. In mdx mice expressing the utrophin transgene, these activity deficits are not observed and the total distance traveled is indistinguishable from wild-type mice. This method is effective for measuring changes in voluntary ambulation associated with dystrophic pathology, and provides a versatile platform that can be readily adapted to diverse research settings.

This protocol describes a flexible, low-cost system for measuring mouse ambulation in an open field activity assay. We show that a 6-minute ambulation assay based on this system detects a decrease in voluntary movement in mdx mice, and accurately distinguishes improvement in a muscle-specific rescue of these animals.

Measuring functional outcomes in the treatment of muscular dystrophy is an essential aspect of preclinical testing. The assessment of voluntary ambulation ...

A Simple Technique to Assay Locomotor Activity in Drosophila

Podsumowanie

Przeglądaj więcej filmów

Rozdziały w tym wideo

Mouse Short- and Long-term Locomotor Activity Analyzed by Video Tracking Software

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

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

Use of the Open Field Maze to Measure Locomotor and Anxiety-like Behavior in Mice

The Treadmill Fatigue Test: A Simple, High-throughput Assay of Fatigue-like Behavior for the Mouse

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

A Method to Test the Effect of Environmental Cues on Mating Behavior in Drosophila melanogaster

A Computational Method to Quantify Fly Circadian Activity

Application of MultiColor FlpOut Technique to Study High Resolution Single Cell Morphologies and Cell Interactions of Glia in Drosophila

A Simple and Low-cost Assay for Measuring Ambulation in Mouse Models of Muscular Dystrophy