Neurogenomic and behavioral principles shape freezing dynamics and synergistic performance in Drosophila melanogaster
Bibliographic record
Abstract
Collective behavior plays a vital role in detecting and evading predators, yet its neural and genetic underpinnings remain poorly understood. In Drosophila melanogaster, visual cues from conspecifics can alleviate freezing responses to threatening stimuli. Using a large-scale behavioral experiment combined with GWAS, we identify key loci, including Ptp99A and kirre, which are involved in visual neuron development and may influence visual responsiveness to conspecifics. Single-cell transcriptomics and functional assays confirm the modulatory roles of Ptp99A in gene expression in visual neurons and behavior. Furthermore, mixed-strain groups show enhanced freezing behavior compared to homogeneous groups, demonstrating a "diversity effect" where genetic diversity within groups induces flexible behavioral changes. Animal-computer interaction experiments using predatory spiders validate that variation in freezing durations among interactive individuals improves antipredator behavioral performance in fly groups. Agent-based simulations further support the hypothesis that behavioral synchronization among genetically diverse individuals improves group-level performance. We introduce genome-wide higher-level association study to find loci whose genetic diversity correlates with diversity effect, highlighting the potential roles of neuronal diversity. These findings demonstrate how genetic diversity fosters synergistic responses to threats, offering insights into the neural and genomic mechanisms underlying collective behavior in non-eusocial insects.
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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 | 0.000 |
| 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) | 0.001 | 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".