Name: integration of animal behavioral assessment and convolutional neural network to study wasabi-alcohol taste-smell interaction
Uploaded: 2024-08-16T13:40:00.000+00:00
Duration: 6 min 19 s
Description: The commercial wasabi pastes commonly used for food preparation contain a homologous compound of chemosensory isothiocyanates (ITCs) that elicit an ...

Z. Cai would like to acknowledge the financial support from the Kwok Chung Bo Fun Charitable Fund for the establishment of the Kwok Yat Wai Endowed Chair of Environmental and Biological Analysis.

In this study, two 7-week-old ICR male mice weighing between 17-25 g were utilized for the animal behavioral assessment. All housing and experimental procedures were approved by the Hong Kong Baptist University Committee on the Use of Human and Animal Subjects in Teaching and Research. The animal room was maintained at a temperature of 25 &#176;C and a room humidity of 40%-70% on a 12-h light-dark cycle.
1. Cage design
<ol>
	<li>Prepare acrylonitrile butadiene styrene bricks in 3 different dimensions for cage construction: 8 mm x 8 mm x 2 mm, 16 mm x 16 mm x 6 mm, and 32 mm x 16 mm x 6 mm.</li>
	<li>Prepare an acrylonitrile butadiene styrene plate (312 mm x 147 mm x 2 mm) as the cage base.</li>
	<li>Prepare a 239 mm x 107 mm non-transparent acrylic plate with a thickness of 2 mm to be used as the bottom plate.</li>
	<li>Prepare a 239 mm x 107 mm transparent acrylic plate with a thickness of 5 mm to be used as the top plate.</li>
	<li>Prepare a 107 mm x 50 mm transparent acrylic plate with a thickness of 7 mm to be used as the terminal plate.</li>
	<li>Construct 2 opaque side walls by stacking bricks to a height of 54 mm.</li>
	<li>Embed the acrylic plates into the acrylonitrile butadiene styrene-based cage, as illustrated in Figure 1A.</li>
	<li>Prepare a chows&#39; chamber that is constructed by five 90 mm x 50 mm transparent acrylic plates with a thickness of 2 mm, as illustrated in Figure 1B. Among the 5 transparent acrylic plates, use 2 plates for the sides, 1 plate for the top, 1 plate for the bottom, and 1 plate for the terminal.</li>
	<li>Prepare a 60 mm x 50 mm 2 mm transparent acrylic plate as a chow introduction plate, and place it in the chow&#39;s chamber.</li>
</ol>
2. Animal behavioral assessment
<ol>
	<li>House littermates of 2 7-week-old ICR male mice together in a regular cage.</li>
	<li>Provide the littermates of 2 mice with free access to food pellets and tap water for a 1-week adaptation period.</li>
	<li>After 1 week, introduce the littermates of 2 mice with a bottle of ethanol (~40% v/v). 
	NOTE: Make sure that they are only allowed to sniff or inhale the provided aqueous ethanol on an ad libitum basis while drinking is restricted.</li>
	<li>Conduct behavioral experiments using the 9-10-week-old mouse model and the transparent cubicle cage that is depicted in Figure 1A.</li>
	<li>Disassemble all the acrylic plates and acrylonitrile butadiene styrene plates and clean them thoroughly. Start by rinsing them with ultrapure water at least 3 times and then dry them using paper towels. Next, spray them with 75% ethanol, followed by cleaning them with lens paper. Finally, allow them to air dry for at least 15 min.</li>
	<li>Weigh the mice and record their body weights before each replication of the behavioral experiment.</li>
	<li>Freshly prepare a mixture of wasabi and peanut butter by weighing 1 g of commercial wasabi and 4.5 g of peanut butter. Mix them in a zip plastic bag. 
	NOTE: Due to the volatility of the isothiocyanates in wasabi, it is important to store the commercial wasabi in a -20 &#176;C freezer.</li>
	<li>Weigh and provide either two 0.5 g pastes of peanut butter or a mixture of wasabi and peanut butter on the chow introduction plate, as illustrated in Figure 1B, C.</li>
	<li>Place the prepared chow introduction plate in the chows&#39; chamber, as illustrated in Figure 1B, C, to allow the 2 mice to have ad libitum access to food during each video recording session.</li>
	<li>Fill the groove underneath with 30 mL of liquid, either pure water or liquor (~42% v/v ethanol), to facilitate concurrent inhalation, as indicated in Figure 1B, C.</li>
	<li>Begin recording using the cameras on 2 smartphones placed on the phone stands at each terminal. 
	NOTE: The specifications of the videos are as follows: frame width, 1920; frame height, 1080; data rate, 20745 kbps; frame rate, 30.00 frames per second (fps).</li>
	<li>Carefully place the 2 trained littermates of mice into the designed animal behavioral study platform from the top and promptly secure the cage with the top plate. 
	NOTE: Make sure this step is completed within 15 s.</li>
	<li>Record each video for 2-3 min. 
	NOTE: Make sure the entire duration of the experiment, from the preparation of the peanut butter-wasabi mixture to the completion of the video recording, is limited to 5 min.</li>
	<li>Repeat the whole experiment 3 times. 
	NOTE: Make sure each replicate of the experiment is separated for at least 6 h.</li>
	<li>Mimic different scenarios. 
	NOTE: For example, in this work, a pair of mice was used in 4 groups with 4 different scenarios that were mimicked by using the experimental setting described above. These scenarios include Scenario A for the background study, Scenario B for the positive control study, Scenario C for the wasabi-alcohol taste-smell interaction study, and Scenario D for the negative control study. Table 1 provides a summary of these scenarios.</li>
</ol>
3. Image recognition
Similar to many studies on image processing23,24,25, a classification model was attained by training a CNN. The script for DL operations was written in Python v.3.10 on Jupyter Notebook (anaconda3). It is available on the following GitHub repository: git@github.com:TommyNHL/imageRecognitionJove.git. To construct and train the CNN, open-source libraries were used, including, numpy v.1.21.5, seaborn v.0.11.2, matplotlib v.3.5.2, cv2 v.4.6.0, sklearn v.1.0.2, tensorflow v.2.11.0, and keras v.2.11.0. These libraries provided the necessary tools and functionality to develop and train CNN for image recognition.
<ol>
	<li>Export a series of video frame images from the collected video clips to generate a data set for model training by using the provided Jupyter Notebook, named Step1_ExtractingAndSavingVideoFrameImages.ipynb.</li>
	<li>Only select the images with at least 1 mouse consuming the provided paste. Examples of the selected images are provided in Supplementary Figure 1, Supplementary Figure 2, Supplementary Figure 3, Supplementary Figure 4, Supplementary Figure 5, Supplementary Figure 6, and Supplementary Figure 7.</li>
	<li>Perform data augmentation by horizontally flipping the generated images by implementing the script provided in the Jupyter Notebook, named Step2_DataAugmentation.ipynb.</li>
	<li>Reserve the image data from each second replicate for external independent CNN model validation. Use the images from each first and third replicates for internal model training and testing.</li>
	<li>Preprocess the image data used in CNN modeling by running the script in the Jupyter Notebook, named Step3_CNNmodeling_TrainTest.ipynb, including image resizing, black color conversion, and image signal normalization.</li>
	<li>Split the training materials into internal training and testing datasets in an 8:2 fashion randomly.</li>
	<li>Initialize the architecture for CNN. Design the number of outputs of CNN based on the number of scenarios to be examined. 
	NOTE: For example, in this study, the neural network was designated to classify 3 classes. Make sure the script for handling data imbalance on class weight is compiled.</li>
	<li>Find the hyperparameter combination that yields minimal loss on the internal test samples for CNN construction.</li>
	<li>Adopt the optimal hyperparameters combination for constructing CNN architecture.</li>
	<li>Open the provided Jupyter Notebooks Step4_CNNmodel_ExternalValOriginal.ipynb and Step5_CNNmodel_ExternalValFlipped.ipynb. Validate the attained model using the independent (original and flipped) images from the second replicate of the animal behavioral experiment.</li>
	<li>Deploy the attained and validated model for classifying the video frame images generated from the experimental group using Jupyter Notebook Step6_CNNmodel_Application.ipynb. 
	NOTE: For example, it is scenario C in this work.</li>
</ol>
4. Manual mouse grimace scale scoring
NOTE: To validate the insights provided by the CNN model prediction, another method previously developed and validated by Langford et al. was applied16. This method involves scoring the MGS based on the 5 specific mouse facial action units (AUs): orbital tightening, nose bulge, cheek bulge, ears tightening outward, and whisker change. Each AU is assigned a score of 0, 1, or 2, indicating the absence, moderate presence, or obvious presence of the AU, respectively. This scoring system allows for the quantification and scaling of each AU to assess the level of nociception or discomfort experienced by the mice.
<ol>
	<li>Capture 3 video frame images of the littermates ingesting the paste for each video clip. Ensure that each frame is separated by at least 3 seconds.</li>
	<li>Blind code and randomly reorder images from different classes of scenarios in sequence by using the provided template file named &#34;shuffleSlides.pptm&#34; (Supplementary File 1) and running the embedded Macro code.</li>
	<li>Invite at least 10 panelists to score the sample images.</li>
	<li>Train the panelists to score image samples using the MGS. Provide the panelists with training materials that include the original article regarding MGS and its manual15,16.</li>
	<li>Calculate the MGS score of each animal subject in a captured frame by averaging all the score points of the corresponding 5 facial AUs. Present the MGS score as the mean&#160;&#177; standard error of measurement (SEM).</li>
	<li>Determine whether statistically significant differences exist among different classes of scenarios by one-way analysis of variance (ANOVA) with Bonferroni&#39;s multiple comparison post-hoc test. 
	NOTE: A value of P &#60; 0.05 is considered statistically significant.</li>
</ol>

Alcohol Diminishes Wasabi Reactions: Behavioral and AI Analysis in Mice

<ol>
	<li>Isshiki, K., Tokuoka, K., Mori, R., Chiba, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preliminary+examination+of+allyl+isothiocyanate+vapor+for+food+preservation.">Preliminary examination of allyl isothiocyanate vapor for food preservation.</a> Biosci Biotechnol Biochem. 56 (9), 1476-1477 (1992).</li><li>Li, X., Wen, Z., Bohnert, H. J., Schuler, M. A., Kushad, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Myrosinase+in+horseradish+(Armoracia+rusticana)+root:+Isolation+of+a+full-length+cDNA+and+its+heterologous+expression+in+Spodoptera+frugiperda+insect+cells.">Myrosinase in horseradish (Armoracia rusticana) root: Isolation of a full-length cDNA and its heterologous expression in Spodoptera frugiperda insect cells.</a> Plant Sci. 172 (6), 1095-1102 (2007).</li><li>Depree, J. A., Howard, T. M., Savage, G. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flavour+and+pharmaceutical+properties+of+the+volatile+sulphur+compounds+of+Wasabi+(Wasabia+japonica).">Flavour and pharmaceutical properties of the volatile sulphur compounds of Wasabi (Wasabia japonica).</a> Food Res Int. 31 (5), 329-337 (1998).</li><li>Hu, S. Q., Wei, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Study+on+extraction+of+wasabi+plant+material+bio-activity+substances+and+anti-cancer+activities.">Study on extraction of wasabi plant material bio-activity substances and anti-cancer activities.</a> Adv Mat Res. 690 - 693, 1395-1399 (2013).</li><li>Lee, H. -. K., Kim, D. -. H., Kim, Y. -. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quality+characteristics+and+allyl+isothiocyanate+contents+of+commercial+wasabi+paste+products.">Quality characteristics and allyl isothiocyanate contents of commercial wasabi paste products.</a> J Food Hyg Saf. 31 (6), 426-431 (2016).</li><li>Bacon, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Wine,+wasabi+and+weight+loss:+Examining+taste+in+food+writing.">Wine, wasabi and weight loss: Examining taste in food writing.</a> Food Cult Soc. 17 (2), 225-243 (2014).</li><li>Fleming, P. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+contribution+of+qualitative+behavioural+assessment+to+appraisal+of+livestock+welfare.">The contribution of qualitative behavioural assessment to appraisal of livestock welfare.</a> Anim Prod Sci. 56, 1569-1578 (2016).</li><li>Shi, X., 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=Behavioral+assessment+of+sensory,+motor,+emotion,+and+cognition+in+rodent+models+of+intracerebral+hemorrhage.">Behavioral assessment of sensory, motor, emotion, and cognition in rodent models of intracerebral hemorrhage.</a> Front Neurol. 12, 667511 (2021).</li><li>Stevenson, R. J., Prescott, J., Boakes, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Confusing+tastes+and+smells:+How+odours+can+influence+the+perception+of+sweet+and+sour+tastes.">Confusing tastes and smells: How odours can influence the perception of sweet and sour tastes.</a> Chem Senses. 24 (6), 627-635 (1999).</li><li>Pfeiffer, J. C., Hollowood, T. A., Hort, J., Taylor, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Temporal+synchrony+and+integration+of+sub-threshold+taste+and+smell+signals.">Temporal synchrony and integration of sub-threshold taste and smell signals.</a> Chem Senses. 30 (7), 539-545 (2005).</li><li>Simons, C. T., Klein, A. H., Carstens, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chemogenic+subqualities+of+mouthfeel.">Chemogenic subqualities of mouthfeel.</a> Chem Senses. 44 (5), 281-288 (2019).</li><li>Andrade, E. L., Luiz, A. P., Ferreira, J., Calixto, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pronociceptive+response+elicited+by+TRPA1+receptor+activation+in+mice.">Pronociceptive response elicited by TRPA1 receptor activation in mice.</a> Neuroscience. 152 (2), 511-520 (2008).</li><li>Palazzo, E., Marabese, I., Gargano, F., Guida, F., Belardo, C., Maione, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+for+evaluating+sensory,+affective+and+cognitive+disorders+in+neuropathic+rodents.">Methods for evaluating sensory, affective and cognitive disorders in neuropathic rodents.</a> Curr Neuropharmacol. 19 (6), 736-746 (2020).</li><li>Topley, M., Crotty, A. M., Boyle, A., Peller, J., Kawaja, M., Hendry, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+motor+and+sensory+neuron+populations+in+a+mouse+median+nerve+injury+model.">Evaluation of motor and sensory neuron populations in a mouse median nerve injury model.</a> J Neurosci Methods. 396, 109937 (2023).</li><li>Langford, D. 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=.">.</a> Mouse Grimace Scale (MGS): The Manual. , (2015).</li><li>Langford, D. 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=Coding+of+facial+expressions+of+pain+in+the+laboratory+mouse.">Coding of facial expressions of pain in the laboratory mouse.</a> Nat Methods. 7 (6), 447-449 (2010).</li><li>Liu, H., Fang, S., Zhang, Z., Li, D., Lin, K., Wang, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MFDNet:+Collaborative+poses+perception+and+matrix+Fisher+distribution+for+head+pose+estimation.">MFDNet: Collaborative poses perception and matrix Fisher distribution for head pose estimation.</a> IEEE Trans Multimedia. 24, 2449-2460 (2022).</li><li>Liu, T., Wang, J., Yang, B., Wang, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NGDNet:+Nonuniform+Gaussian-label+distribution+learning+for+infrared+head+pose+estimation+and+on-task+behavior+understanding+in+the+classroom.">NGDNet: Nonuniform Gaussian-label distribution learning for infrared head pose estimation and on-task behavior understanding in the classroom.</a> Neurocomputing. 436, 210-220 (2021).</li><li>Liu, T., Liu, H., Yang, B., Zhang, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=LDCNet:+Limb+direction+cues-aware+network+for+flexible+human+pose+estimation+in+industrial+behavioral+biometrics+systems.">LDCNet: Limb direction cues-aware network for flexible human pose estimation in industrial behavioral biometrics systems.</a> IEEE Trans Industr Inform. 20 (6), 8068-8078 (2023).</li><li>Grant, E. P., 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=What+can+the+quantitative+and+qualitative+behavioural+assessment+of+videos+of+sheep+moving+through+an+autonomous+data+capture+system+tell+us+about+welfare.">What can the quantitative and qualitative behavioural assessment of videos of sheep moving through an autonomous data capture system tell us about welfare.</a> Appl Anim Behav Sci. 208, 31-39 (2018).</li><li>Vidal, A., Jha, S., Hassler, S., Price, T., Busso, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Face+detection+and+grimace+scale+prediction+of+white+furred+mice.">Face detection and grimace scale prediction of white furred mice.</a> Mach Learn Appl. 8, 100312 (2022).</li><li>Zylka, M. 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=Development+and+validation+of+Painface,+A+software+platform+that+simplifies+and+standardizes+mouse+grimace+analyses.">Development and validation of Painface, A software platform that simplifies and standardizes mouse grimace analyses.</a> J Pain. 24 (4), 35-36 (2023).</li><li>Liu, H., Zhang, C., Deng, Y., Liu, T., Zhang, Z., Li, Y. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Orientation+cues-aware+facial+relationship+representation+for+head+pose+estimation+via+Transformer.">Orientation cues-aware facial relationship representation for head pose estimation via Transformer.</a> IEEE Trans Image Process. 32, 6289-6302 (2023).</li><li>Liu, H., Liu, T., Chen, Y., Zhang, Z., Li, Y. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EHPE:+Skeleton+cues-based+Gaussian+coordinate+encoding+for+efficient+human+pose+estimation.">EHPE: Skeleton cues-based Gaussian coordinate encoding for efficient human pose estimation.</a> IEEE Trans Multimedia. , (2022).</li><li>Liu, H., 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=TransIFC:+Invariant+cues-aware+feature+concentration+learning+for+efficient+fine-grained+bird+image+classification.">TransIFC: Invariant cues-aware feature concentration learning for efficient fine-grained bird image classification.</a> IEEE Trans Multimedia. , (2023).</li><li>Akkaya, I. B., Halici, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mouse+face+tracking+using+convolutional+neural+networks.">Mouse face tracking using convolutional neural networks.</a> IET Comput Vis. 12 (2), 153-161 (2018).</li><li>Andresen, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Towards+a+fully+automated+surveillance+of+well-being+status+in+laboratory+mice+using+deep+learning:+Starting+with+facial+expression+analysis.">Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expression analysis.</a> PLoS One. 15 (4), e0228059 (2020).</li><li>Ernst, L., 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=Improvement+of+the+mouse+grimace+scale+set-up+for+implementing+a+semi-automated+Mouse+Grimace+Scale+scoring+(Part+1).">Improvement of the mouse grimace scale set-up for implementing a semi-automated Mouse Grimace Scale scoring (Part 1).</a> Lab Anim. 54 (1), 83-91 (2020).</li><li>Tuttle, A. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+deep+neural+network+to+assess+spontaneous+pain+from+mouse+facial+expressions.">A deep neural network to assess spontaneous pain from mouse facial expressions.</a> Mol Pain. 14, 1744806918763658 (2018).</li><li>Lencioni, G. C., de Sousa, R. V., de Souza Sardinha, E. J., Corr&#234;a, R. R., Zanella, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pain+assessment+in+horses+using+automatic+facial+expression+recognition+through+deep+learning-based+modeling.">Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling.</a> PLoS One. 16 (10), e0258672 (2021).</li><li>Steagall, P. V., Monteiro, B. P., Marangoni, S., Moussa, M., Sauti&#233;, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fully+automated+deep+learning+models+with+smartphone+applicability+for+prediction+of+pain+using+the+Feline+Grimace+Scale.">Fully automated deep learning models with smartphone applicability for prediction of pain using the Feline Grimace Scale.</a> Sci Rep. 13, 21584 (2023).</li></ol>

The authors declare that there are no conflicts of interest.

The proposed method for studying taste-smell interaction in this work is based on the original method of behavioral coding for facial expression of pain in mice, which was developed by Langford et al.16. Several recently published articles have introduced CNN for automatic mouse face tracking and subsequent MGS scoring21,26,27,28. Applying CNNs offers an advantage over t...

Wasabia japonica, commonly known as wasabi, has gained recognition in food preparation1,2. The intense sensory experience it elicits upon consumption, characterized by tearing up, sneezing, or coughing, is well-known. This distinctive pungency of wasabi can be attributed to a homologous compound of chemosensory isothiocyanates (ITCs). They are volatile organosulfur phytochemicals that can be categorized into &#969;-alkenyl and &#969;-methylthioalkyl isothiocyanates3. Among these compounds, allyl isothiocyanate (AITC) is the most predominated natural ITC product found in plants belonging to the Cruciferae family, such as horseradish and mustard4. Commercial wasabi pastes are commonly prepared from horseradish, making AITC a chemical marker used for quality control of these commercial products5.
Pairing dietary alcoholic beverages with wasabi-infused dishes can be considered an example of cultural disposition6. Subjectively, this combination may complement the spiciness and heat between wasabi and the spirit, enhancing the overall culinary experience. Animal qualitative behavioral assessment (QBA) is a comprehensive whole-animal methodological approach that examines behavioral changes in subjects in response to short-term or long-term external stimuli using numerical terms7. This method encompasses pain tests, motor tests, learning and memory tests, as well as emotion tests specifically designed for rodent models8. However, studies investigating the synergistic sensory evaluation of gustation together with olfaction remain scarce in the literature until now9,10. Most of the studies on chemesthetic sensation are confined to examining individual food and beverage consumption separately11. Consequently, there is a dearth of research on the taste-smell interaction involving the act of sniffing liquor while consuming wasabi.
As the wasabi-induced stinging sensation is believed to be a form of nociception12, animal behavioral assessments are well-suited for evaluating the nociceptive sensory responses in rodent animals8,13,14. A method for assessing nociception in mice, known as the mouse grimace scale (MGS) scoring was developed by Langford et al.15,16. This behavioral study method is a pain-related assessment approach, relying on the analysis of facial expressions exhibited by the experimental mice. The experimental setup is straightforward, involving a transparent cage and 2 cameras for video recording. By incorporating advanced technologies17,18,19 for automatic data capture, quantitative and qualitative behavioral measures can be obtained, enhancing animal welfare during behavioral monitoring20. Consequently, the MGS has the potential to be applied in studying the effects of various external stimuli on animals in an uninterrupted and ad libitum manner. However, the scoring process only involves selecting a few (less than 10) video frame images for evaluation by the panelists, and prior training is necessary. Scoring a large number of sample images can be labor-intensive. To overcome this time-consuming challenge, several studies have employed machine learning techniques for predicting the MGS score21,22. Yet, it is important to note that the MGS is a continuous measure. Therefore, a multi-class classification model would be more suitable for evaluating a logical and categorical problem, such as determining whether the images of mice simultaneously ingesting wasabi and sniffing liquor resemble those of normal mice.
In this study, a methodology for investigating the taste-smell interaction in mice was proposed. This methodology combines animal behavioral studies with a convolutional neural network (CNN) to analyze the facial expressions of the mouse subjects. Two mice were observed thrice under normal behavioral conditions, during the experience of wasabi-induced nociception and while sniffing liquor in a specifically designed cage. The facial expressions of the mice were video-recorded, and the generated frame images were utilized to optimize the architecture of a deep learning (DL) model. The model was then validated using an independent image dataset and deployed to classify the images acquired from the experimental group. To determine the extent of wasabi pungency suppression when the mice simultaneously sniffed liquor during wasabi consumption, the insights provided by artificial intelligence were further corroborated through cross-validation with another data analysis method, the MGS scoring16.

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integration of animal behavioral assessment and convolutional neural network to study wasabi-alcohol taste-smell interaction

The commercial wasabi pastes commonly used for food preparation contain a homologous compound of chemosensory isothiocyanates (ITCs) that elicit an irritating sensation upon consumption. The impact of sniffing dietary alcoholic beverages on the sensation of wasabi spiciness has never been studied. While most sensory evaluation studies focus on individual food and beverages separately, there is a lack of research on the olfactory study of sniffing liquor while consuming wasabi. Here, a methodology is developed that combines the use of an animal behavioral study and a convolutional neural network to analyze the facial expressions of mice when they simultaneously sniff liquor and consume wasabi. The results demonstrate that the trained and validated deep learning model recognizes 29% of the images depicting co-treatment of wasabi and alcohol belonging to the class of the wasabi-negative liquor-positive group without the need for prior training materials filtering. Statistical analysis of mouse grimace scale scores obtained from the selected video frame images reveals a significant difference (P &lt; 0.01) between the presence and absence of liquor. This finding suggests that dietary alcoholic beverages might have a diminishing effect on the wasabi-elicited reactions in mice. This combinatory methodology holds potential for individual ITC compound screening and sensory analyses of spirit components in the future. However, further study is required to investigate the underlying mechanism of alcohol-induced suppression of wasabi pungency.

The main objective of this study is to establish a robust framework for investigating the taste-smell interaction in mice. This framework incorporates the use of artificial intelligence and QBA to develop a predictive classification model. Additionally, the insights obtained from DL are cross-validated with a quantitative MGS assessment for an internal independent analysis. The primary application of this methodology is to examine the extent of suppression of the wasabi-invoked nociception when mice sniff dietary alcohol...

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

This article describes a set of methods for measuring the suppressive ability of sniffing alcoholic beverages on the wasabi-elicited stinging sensation.

The commercial wasabi pastes commonly used for food preparation contain a homologous compound of chemosensory isothiocyanates (ITCs) that elicit an ...

integration-animal-behavioral-assessment-convolutional-neural-network

Research

JoVE Journal

Behavior

353 Views.  Hong Kong Baptist University. We willen zien of het ruiken van alcoholische dranken de verbranding van wasabi kan verlichten. Dus ontwikkelden we een methode die gedragsstudie bij dieren en deep learning omvatte om deze smaak-geurinteractie te onderzoeken. Door gebruik te maken van ons gespecialiseerde kooiontwerp voor het verzamelen van gedragsgegevens en een robuust deep learning-model, ontdekten we dat 29% van de afbeeldingen van gelijktijdige behandeling van wasabi en alcohol werden g...

Video: Auteur in de schijnwerpers: onderzoek naar de interactie tussen smaak en geur - alcoholgeuren gebruiken om wasabi-verbranding te verlichten

Examination of Rapid Dopamine Dynamics with Fast Scan Cyclic Voltammetry During Intra-oral Tastant Administration in Awake Rats

Rapid, phasic dopamine (DA) release in the mammalian brain plays a critical role in reward processing, reinforcement learning, and motivational control. Fast scan cyclic voltammetry (FSCV) is an electrochemical technique with high spatial and temporal (sub-second) resolution that has been utilized to examine phasic DA release in several types of preparations. In vitro experiments in single-cells and brain slices and in vivo experiments in anesthetized rodents have been used to identify mechanisms that mediate dopamine release and uptake under normal conditions and in disease models. Over the last 20 years, in vivo FSCV experiments in awake, freely moving rodents have also provided insight of dopaminergic mechanisms in reward processing and reward learning. One major advantage of the awake, freely moving preparation is the ability to examine rapid DA fluctuations that are time-locked to specific behavioral events or to reward or cue presentation. However, one limitation of combined behavior and voltammetry experiments is the difficulty of dissociating DA effects that are specific to primary rewarding or aversive stimuli from co-occurring DA fluctuations that mediate reward-directed or other motor behaviors. Here, we describe a combined method using in vivo FSCV and intra-oral infusion in an awake rat to directly investigate DA responses to oral tastants. In these experiments, oral tastants are infused directly to the palate of the rat &#8211; bypassing reward-directed behavior and voluntary drinking behavior &#8211; allowing for direct examination of DA responses to tastant stimuli.

Rapid fluctuations in extracellular dopamine (DA) mediate both reward processing and motivated behavior in mammals. This manuscript describes the combined use of fast scan cyclic voltammetry (FSCV) and intra-oral tastant administration to determine how tastants alter rapid dopamine release in awake, freely moving rats.

Rapid, phasic dopamine (DA) release in the mammalian brain plays a critical role in reward processing, reinforcement learning, and motivational control. ...

A Method for Evaluating the Reinforcing Properties of Ethanol in Rats without Water Deprivation, Saccharin Fading or Extended Access Training

Operant oral self-administration methods are commonly used to study the reinforcing properties of ethanol in animals. However, the standard methods require saccharin/sucrose fading, water deprivation and/or extended training to initiate operant responding in rats. This paper describes a novel and efficient method to quickly initiate operant responding for ethanol that is convenient for experimenters and does not require water deprivation or saccharin/sucrose fading, thus eliminating the potential confound of using sweeteners in ethanol operant self-administration studies. With this method, Wistar rats typically acquire and maintain self-administration of a 20% ethanol solution in less than two weeks of training. Furthermore, blood ethanol concentrations and rewards are positively correlated for a 30 min self-administration session. Moreover, naltrexone, an FDA-approved medication for alcohol dependence that has been shown to suppress ethanol self-administration in rodents, dose-dependently decreases alcohol intake and motivation to consume alcohol for rats self-administering 20% ethanol, thus validating the use of this new method to study the reinforcing properties of alcohol in rats.

This protocol describes a novel and efficient method to quickly initiate operant responding for ethanol in rats that, contrary to standard methods, does not require water deprivation or saccharin/sucrose fading to initiate responding.

Operant oral self-administration methods are commonly used to study the reinforcing properties of ethanol in animals. However, the standard methods ...

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage

Multiple factors&#8212;such as aging and genes&#8212;are frequently associated with cognitive decline. Genetically modified mouse models of cognitive decline, such as Alzheimer&#39;s disease (AD), have become a promising tool to elucidate the underlying mechanisms and promote the therapeutic advances. An important step is the validation and characterization of expected behavioral abnormality in the models, in the case of AD, cognitive decline. The long-term behavioral investigations of laboratory animals to study the effect of aging demand substantial efforts from researchers. The IntelliCage system is a high-throughput and cost-effective test battery for mice that eliminates the need for daily human handling. Here, we describe how the system is utilized in the long-term phenotyping of a genetic Alzheimer&#39;s disease model, specifically focusing on the cognitive functions. The experiment employs repeated battery of tests that assess spatial learning and executive functions. This cost-effective age-dependent phenotyping allows us to identify the transient and/or permanent effects of genes on various cognitive aspects.

This paper describes a protocol for cognitive assessments for genetic models of the Alzheimer&#39;s disease using the IntelliCage system, which is a high throughput automated behavioral monitoring system with operant conditioning.

Multiple factors&#8212;such as aging and genes&#8212;are frequently associated with cognitive decline. Genetically modified mouse models of cognitive ...

Sampling and Analysis of Animal Scent Signals

We have developed an effective methodology for sampling and analysis of odor signals, by using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry, to understand how they may be used in animal communication. This technique allows the semi-quantitative analysis of the volatile components of odor secretions by enabling the separation and tentative identification of the components in the sample, followed by the analysis of peak area ratios to look for trends that could signify compounds that may be involved in signaling. The key strengths of this current approach are the range of sample types that can be analyzed; the lack of need for any complex sample preparation or extractions; the ability to separate and analyze the components of a mixture; the identification of the components detected; and the capability to provide semi-quantitative and potentially quantitative information on the components detected. The main limitation to the methodology relates to the samples themselves. Since the components of specific interest are volatile, and these could easily be lost, or their concentrations altered, it is important that the samples are stored and transported appropriately after their collection. This also means that sample storage and transport conditions are relatively costly. This method can be applied to a variety of samples (including urine, feces, hair and scent-gland odor secretions). These odors consist of complex mixtures, occurring in a range of matrices, and thus necessitate the use of techniques to separate the individual components and extract the compounds of biological interest.

We have developed an effective methodology for sampling and analysis of odor signals in order to understand how they may be used in animal communication. Particularly, we use headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry to analyze the volatile components of animal odors and scent-markings.

We have developed an effective methodology for sampling and analysis of odor signals, by using headspace solid-phase microextraction coupled with gas ...

Assessment of Mouse Judgment Bias through an Olfactory Digging Task

Judgment biases (JB) are differences in the way that individuals in positive and negative affective/emotional states interpret ambiguous information. This phenomenon has long been observed in humans, with individuals in positive states responding to ambiguity 'optimistically' and those in negative states instead showing 'pessimism'. Researchers aiming to assess animal affect have taken advantage of these differential responses, developing tasks to assess judgment bias as an indicator of affective state. These tasks are becoming increasingly popular across diverse species and fields of research. However, for laboratory mice, the most widely used vertebrates in research and a species heavily relied upon to model affective disorders, only one JB task has been successfully validated as sensitive to changes in affective state. Here, we provide a detailed description of this novel murine JB task, and evidence of its sensitivity to mouse affect. Though refinements are still necessary, assessment of mouse JB opens the door for answering both practical questions regarding mouse welfare, and fundamental questions about the impact of affective state in translational research.

This article provides a detailed description of a novel mouse judgment bias protocol. Evidence of this olfactory digging task's sensitivity to affective state is also demonstrated and its utility across diverse research fields is discussed.

Judgment biases (JB) are differences in the way that individuals in positive and negative affective/emotional states interpret ambiguous information. This ...

New Methods to Study Gustatory Coding

The sense of taste allows animals to detect chemicals in the environment, giving rise to behaviors critical for survival. When Gustatory Receptor Neurons (GRNs) detect tastant molecules, they encode information about the identity and concentration of the tastant as patterns of electrical activity that then propagate to follower neurons in the brain. These patterns constitute internal representations of the tastant, which then allow the animal to select actions and form memories. The use of relatively simple animal models has been a powerful tool to study basic principles in sensory coding. Here, we propose three new methods to study gustatory coding using the moth Manduca sexta. First, we present a dissection procedure for exposing the maxillary nerves and the subesophageal zone (SEZ), allowing recording of the activity of GRNs from their axons. Second, we describe the use of extracellular electrodes to record the activity of multiple GRNs by placing tetrode wires directly into the maxillary nerve. Third, we present a new system for delivering and monitoring, with high temporal precision, pulses of different tastants. These methods allow the characterization of neuronal responses in vivo directly from GRNs before, during and after tastants are delivered. We provide examples of voltage traces recorded from multiple GRNs, and present an example of how a spike sorting technique can be applied to the data to identify the responses of individual neurons. Finally, to validate our recording approach, we compare extracellular recordings obtained from GRNs with tetrodes to intracellular recordings obtained with sharp glass electrodes.

We present three new methods to study gustatory coding. Using a simple animal, the moth Manduca sexta (Manduca), we describe a dissection protocol, the use of extracellular tetrodes to record the activity of multiple gustatory receptor neurons, and a system for delivering and monitoring precisely timed pulses of tastants.

The sense of taste allows animals to detect chemicals in the environment, giving rise to behaviors critical for survival. When Gustatory Receptor Neurons ...

Neuroscience

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Fluorescent genetically encoded calcium indicators have contributed greatly to our understanding of neural dynamics from the level of individual neurons to entire brain circuits. However, neural responses may vary due to prior experience, internal states, or stochastic factors, thus generating the need for methods that can assess neural function across many individuals at once. Whereas most recording techniques examine a single animal at a time, we describe the use of wide-field microscopy to scale up neuronal recordings to dozens of Caenorhabditis elegans or other sub-millimeter-scale organisms at once. Open-source hardware and software allow great flexibility in programming fully automated experiments that control the intensity and timing of various stimulus types, including chemical, optical, mechanical, thermal, and electromagnetic stimuli. In particular, microfluidic flow devices provide precise, repeatable, and quantitative control of chemosensory stimuli with sub-second time resolution. The NeuroTracker semi-automated data analysis pipeline then extracts individual and population-wide neural responses to uncover functional changes in neural excitability and dynamics. This paper presents examples of measuring neuronal adaptation, temporal inhibition, and stimulus crosstalk. These techniques increase the precision and repeatability of stimulation, allow the exploration of population variability, and are generalizable to other dynamic fluorescent signals in small biosystems from cells and organoids to whole organisms and plants.

We present a method for the flexible chemical and multimodal stimulation and recording of simultaneous neural activity from many Caenorhabditis elegans worms. This method uses microfluidics, open-source hardware and software, and supervised automated data analysis to enable the measurement of neuronal phenomena such as adaptation, temporal inhibition, and stimulus crosstalk.

Fluorescent genetically encoded calcium indicators have contributed greatly to our understanding of neural dynamics from the level of individual neurons ...

Modeling Alcohol Use Disorder Using Chronic Intermittent Ethanol Vapor Exposure

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Alcohol use disorder (AUD) is a chronic alcohol-related disorder that typically presents as uncontrolled drinking and preoccupation with alcohol. A key component of AUD research is using translationally relevant preclinical models. Over the past several decades, a variety of animal models have been used to study AUD. One prominent model of AUD is the chronic intermittent ethanol vapor exposure (CIE) model, which is a well-established approach for inducing alcohol dependence in rodents through repeated cycles of ethanol exposure via inhalation. To model AUD in mice, the CIE exposure is paired with a voluntary two-bottle choice (2BC) of alcohol drinking and water to measure the escalation of alcohol drinking. The 2BC/CIE procedure involves alternating weeks of 2BC drinking and CIE, which repeat until the escalation of alcohol drinking is achieved. In the present study, we outline the procedures for performing 2BC/CIE, including the daily use of the CIE vapor chamber, and provide an example of escalated alcohol drinking in C57BL/6J mice using this approach.

Presented here is a protocol for the 2BC/CIE model of alcohol dependence in mice to study alcohol use disorder.

Alcohol use disorder (AUD) is a chronic alcohol-related disorder that typically presents as uncontrolled drinking and preoccupation with alcohol. A key ...

Effect of Odor on Brain Excitability Assessed by a Breath-Synchronized Olfactometer

Combining a Breath-Synchronized Olfactometer with Brain Simulation to Study the Impact of Odors on Corticospinal Excitability and Effective Connectivity

It is widely accepted that olfactory stimulation elicits motor behaviors, such as approaching pleasant odorants and avoiding unpleasant ones, in animals and humans. Recently, studies using electroencephalography and transcranial magnetic stimulation (TMS) have demonstrated a strong link between processing in the olfactory system and activity in the motor cortex in humans. To better understand the interactions between the olfactory and the motor systems and to overcome some of the previous methodological limitations, we developed a new method combining an olfactometer that synchronizes the random order presentation of odorants with different hedonic values and the TMS (single- and dual-coil) triggering with nasal breathing phases. This method allows probing the modulations of corticospinal excitability and effective ipsilateral connectivity between the dorsolateral prefrontal cortex and the primary motor cortex that could occur during pleasant and unpleasant odor perception. The application of this method will allow for objectively discriminating the pleasantness value of an odorant in a given participant, indicating the biological impact of the odorant on brain effective connectivity and excitability. In addition, this could pave the way for clinical investigations in patients with neurological or neuropsychiatric disorders who may exhibit&#160;odor hedonic alterations and maladaptive approach-avoidance behaviors.

Author Spotlight: Exploring Olfactory Influences on Corticospinal Excitability - Insights and Innovations in Neurological Research 

This paper describes using a breath-synchronized olfactometer to trigger single- and dual-coil transcranial magnetic stimulation (TMS) during odorant presentation synchronized to human nasal breathing. This combination allows us to objectively investigate how pleasant and unpleasant odors impact corticospinal excitability and brain-effective connectivity in a given individual.

It is widely accepted that olfactory stimulation elicits motor behaviors, such as approaching pleasant odorants and avoiding unpleasant ones, in animals ...

A Novel Method for Enhanced Analysis of Insect Taste Neuron Activity

Base Recording: A Technique for Analyzing Responses of Taste Neurons in Drosophila

Insects taste the external world through taste hairs, or sensilla, that have pores at their tips. When a sensillum comes into contact with a potential food source, compounds from the food source enter through the pore and activate neurons within. For over 50 years, these responses have been recorded using a technique called tip recording. However, this method has major limitations, including the inability to measure neural activity before or after stimulus contact and the requirement for tastants to be soluble in aqueous solutions. We describe here a technique that we call base recording, which overcomes these limitations. Base recording allows the measurement of taste neuron activity before, during, and after the stimulus. Thus, it allows extensive analysis of OFF responses that occur after a taste stimulus. It can be used to study hydrophobic compounds such as long-chain pheromones that have very low solubility in water. In summary, base recording offers the advantages of single-sensillum electrophysiology as a means of measuring neuronal activity - high spatial and temporal resolution, without the need for genetic tools - and overcomes key limitations of the traditional tip recording technique.

Author Spotlight: Advances in Chemoreception &#8211; From Insect Odor Receptors to Non-Coding RNAs

A rarely used method of electrophysiological recording, base recording, allows analysis of features of taste coding that cannot be examined by conventional recording methods. Base recording also allows the analysis of taste responses to hydrophobic stimuli that cannot be studied using traditional electrophysiological methods.

Insects taste the external world through taste hairs, or sensilla, that have pores at their tips. When a sensillum comes into contact with a potential ...

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

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