Evidence map›Paper›PMID 40874843›Full record

ArticleeLife2025

DeePosit, an AI-based tool for detecting mouse urine and fecal depositions from thermal video clips of behavioral experiments.

David Peles, Shai Netser, Natalie Ray, Taghreed Suliman, Shlomo Wagner

Abstract read
In one paragraph

Article in eLife, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

David PelesSagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel.ORCID https://orcid.org/0009-0003-5543-1676
Shai NetserSagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel.ORCID https://orcid.org/0000-0003-4176-1124
Natalie RaySagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel.
Taghreed SulimanSagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel.ORCID https://orcid.org/0009-0009-4177-5391
Shlomo WagnerSagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel.ORCID https://orcid.org/0000-0002-7618-0752

Funding

Congressionally Directed Medical Research Programs AR210005German Research Foundation GR 3619/16-1German Research Foundation SH 752/2-1ISF-NSFC joint research program 3459/20Israel Science Foundation 1361/17Israel Science Foundation 2220/22Ministry of Health of Israel 3-18380Ministry of Science, Technology and Space of Israel 3-12068United States-Israel Binational Science Foundation 2019186
6 · The paper itself

Abstract

In many mammals, including rodents, social interactions are often accompanied by active urination (micturition), which is considered a mechanism for spatial scent marking. Urine and fecal deposits contain a variety of chemosensory signals that convey information about the individual's identity, genetic strain, social rank, and physiological or hormonal state. Furthermore, scent marking has been shown to be influenced by the social context and by the individual's internal state and experience. Therefore, analyzing scent-marking behavior during social interactions can provide valuable insight into the structure of mammalian social interactions in health and disease. However, conducting such analyses has been hindered by several technical challenges. For example, the widely used void spot assay lacks temporal resolution and is prone to artifacts, such as urine smearing. To solve these issues, recent studies employed thermal imaging for the spatio-temporal analysis of urination activity. However, this method involved manual analysis, which is time-consuming and susceptible to observer bias. Moreover, defecation activity was hardly analyzed by previous studies. In the present study, we integrate thermal imaging with an open-source algorithm based on a transformer-based video classifier for automatic detection and classification of urine and fecal deposits made by male and female mice during various social behavior assays. Our results reveal distinct dynamics of urination and defecation in a test-, strain-, and sex-dependent manner, indicating two separate processes of scent marking in mice. We validate this algorithm, termed by us DeePosit, and show that its accuracy is comparable to that of a human annotator and that it is efficient in various setups and conditions. Thus, the method and tools introduced here enable efficient and unbiased automatic spatio-temporal analysis of scent-marking behavior in the context of behavioral experiments in small rodents.

Indexed as

Artificial IntelligenceBehavior, AnimalFecesUrineAnimalsDefecationFemaleMaleMiceOdorantsSocial BehaviorUrinationVideo Recordingfecal depositionmachine learningmicturitionmouseneurosciencesocial behaviorthermal imagingurination

Identifiers

PMID40874843
PMCPMC12393880

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.