Isolation-Induced Ultrasonic Vocalization in Environmental and Genetic Mice Models of Autism
Bibliographic record
Abstract
Studies in rodent models suggest that calls emitted by isolated pups serve as an early behavioral manifestation of communication deficits and autistic like behavior. Previous studies in our labs showed that gestational exposure to the pesticide chlorpyrifos (CPF) and the Mthfr-knock-out mice are associated with impaired social preference and restricted or repetitive behavior. To extend these studies, we examine how pup communication via ultrasonic vocalizations is altered in these ASD models. We implemented an unsupervised hierarchical clustering method based on the spectral properties of the syllables in order to exploit syllable classification to homogeneous categories while avoiding over-categorization. Comparative exploration of the spectral and temporal aspects of syllables emitted by pups in two ASD models point to the following: (1) Most clusters showed a significant effect of the ASD factor on the start and end frequencies and bandwidth and (2) The highest percent change due to the ASD factor was on the bandwidth and duration. In addition, we found sex differences in the spectral and temporal properties of the calls in both control groups as well as an interaction between sex and the gene/environment factor. Considering the basal differences in the characteristics of syllables emitted by pups of the C57Bl/6 and Balb/c strains used as a background in the two models, we suggest that the above spectral-temporal parameters start frequency, bandwidth, and duration are the most sensitive USV features that may represent developmental changes in ASD models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 source (direct Gemma or distilled Codex), 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".