Power calculations for larval zebrafish in light-dark transition test for developmental neurotoxicity
Bibliographic record
Abstract
The link between environmental chemical exposures and neurodevelopmental disorders such as autism and attention-deficit/hyperactivity disorder underscores the need to develop efficient developmental neurotoxicity (DNT) assays for chemical evaluation. The zebrafish Light-Dark Transition Test (LDTT) assesses changes in zebrafish larval behavioral responses to chemical exposure by recording their distance moved under alternating light and dark conditions. To gain confidence in classifying a chemical as having a DNT effect for the LDTT assay, it is important to determine the minimum sample size to obtain a robust behavioral response. We calculated statistical power under common models based on LDTT data collected from four laboratories using standard protocol parameters, where each 96-well plate contained 5–7 test concentrations and 12–16 vehicle control wells (1 larva/well). Power calculations were conducted to identify concentration effects using t-tests, analysis of variance (ANOVA), and repeated measures ANOVA (RMANOVA), with data from four endpoints: Total Distance, Movement Similarity, Distance Change, and Distance Shift. The tests showed the highest power for the Movement Similarity and Distance Change endpoints, which had the lowest intra- and inter-laboratory variability, resulting in a smaller necessary sample size to estimate dose effects. The use of these endpoints more than doubled the power of the statistical tests for the Total Distance endpoints using the same sample size and typically required between 8-32 samples to achieve 80% power at a 20% effect size. This work demonstrates that the LDTT can be improved for detecting DNT effects by careful consideration of endpoint selection, data transformation, and type of statistical test. • New light-dark transition endpoints streamline zebrafish neurodevelopment research • Statistical power differs across zebrafish endpoints and light-dark phases • Data normalization by vehicle control per plate minimizes inter-lab variations • Normalized data doubled statistical power in the study protocol • 8-32 samples needed per dose group for 80% power at 20% effect size
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".