Exhaustive behavioral profile assay to detect genotype differences between wild-type, inflammasome-deficient, and Nlrp12 knock-out mice
Bibliographic record
Abstract
Technological advances in computer vision led to the development of various algorithms that are designed to analyze human and animal behavior. We have used an algorithm based on hidden Markov model to monitor the behavior of two different knock-out mice and a healthy control. The goal of this study was to detect behavioral changes in mice with single gene deletion in different inflammatory molecular pathways. Mice with elevated inflammatory activity were compared to wild-type and to mice that lack an inflammasome, a multiprotein complex that processes main inflammatory cytokines, IL-1 beta and IL-18. Importantly, there were no previous reports of any behavioral abnormalities in these mice. We used 10 days continuous recording analysis of 34 behavioral activities. Resulting data were analyzed using R programming. Within the dataset, we found a large number of statistically significant correlations and therefore used factor analysis and hierarchical clustering to reduce the dimensionality of the data that resulted in the 6 factors and 6 clusters. We found that 3 factors and 4 clusters were significantly different between three groups of mice. In conclusion, by using computerized video assessment method paired with R, we have found profound differences where differences could not have been detected by a naked eye. This method allows for fast, accurate, sensitive, and unbiased evaluating of multiparametric behavioral arrays in biomedical research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".