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Abstract Hydrogen sulfide is a toxic gas that disrupts numerous biological processes, including energy production in the mitochondria, yet fish in thePoecilia mexicanaspecies complex have independently evolved sulfide tolerance several times. Despite clear evidence for convergence at the phenotypic level in these fishes, it is unclear if the repeated evolution of hydrogen sulfide tolerance is the result of similar genomic changes. To address this gap, we used a targeted capture approach to sequence genes associated with sulfide processes and toxicity from five sulfidic and five nonsulfidic populations in the species complex. By comparing sequence variation in candidate genes to a reference set, we identified similar population structure and differentiation, suggesting that patterns of variation in most genes associated with sulfide processes and toxicity are due to demographic history and not selection. But the presence of tree discordance for a subset of genes suggests that several loci are evolving divergently between ecotypes. We identified two differentiation outlier genes that are associated with sulfide detoxification in the mitochondria that have signatures of selection in all five sulfidic populations. Further investigation into these regions identified long, shared haplotypes among sulfidic populations. Together, these results reveal that selection on standing genetic variation in putatively adaptive genes may be driving phenotypic convergence in this species complex.
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This content will become publicly available on February 25, 2026
Learning Set Functions with Implicit Differentiation
A recent work introduces the problem of learning set functions from data generated by a so-called optimal subset oracle. Their approach approximates the underlying utility function with an energy-based model, whose parameters are estimated via mean-field variational inference. This approximation reduces to fixed point iterations; however, as the number of iterations increases, automatic differentiation quickly becomes computationally prohibitive due to the size of the Jacobians that are stacked during backpropagation. We address this challenge with implicit differentiation and examine the convergence conditions for the fixed-point iterations. We empirically demonstrate the efficiency of our method on synthetic and real-world subset selection applications including product recommendation, set anomaly detection and compound selection tasks.
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- Award ID(s):
- 1750539
- PAR ID:
- 10578253
- Publisher / Repository:
- Association for the Advancement of Artificial Intelligence (AAAI)
- Date Published:
- Format(s):
- Medium: X
- Location:
- The 39th Annual AAAI Conference on Artificial Intelligence, Philadelphia, PA
- Sponsoring Org:
- National Science Foundation
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