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The underlying factors that lead to specific strains within a species to emerge as human pathogens remain mostly enigmatic. The diarrheal disease cholera is caused by strains from a phylogenetically confined group within theVibrio choleraespecies, the pandemic cholera group (PCG), making it an ideal model system to tackle this puzzling phenomenon. Comprehensive analyses of over 1,840V. choleraegenomes, including environmental isolates from this study, reveal that the species consists of eleven groups, with the PCG belonging to the largest and located within a lineage shared with environmental strains. This hierarchical classification provided us with a framework to unravel the ecoevolutionary dynamics of the genetic determinants associated with the emergence of toxigenicV. cholerae. Our analyses indicate that this phenomenon is largely dependent on the acquisition of unique modular gene clusters and allelic variations that confer a competitive advantage during intestinal colonization. We determined that certain PCG-associated alleles are essential for successful colonization whereas others provide a nonlinear competitive advantage, acting as a critical bottleneck that clarifies the isolated emergence of PCG. For instance, toxigenic strains encoding non-PCG alleles of a)tcpFor b) a sextuple allelic exchange mutant for genestcpA,toxT,VC0176,VC1791,rfbT,andompU, lose their ability to colonize the intestine. Interestingly, these alleles do not play a role in the colonization of newly established model environmental reservoirs. Our study uncovers the evolutionary roots of toxigenicV. choleraeoffering a tractable approach for investigating the emergence of pathogenic clones within an environmental population.
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This content will become publicly available on June 17, 2026
Alsatian: Optimizing Model Search for Deep Transfer Learning
Transfer learning is an effective technique for tuning a deep learning model when training data or computational resources are limited. Instead of training a new model from scratch, the parameters of an existing base model are adjusted for the new task. The accuracy of such a fine-tuned model depends on the suitability of the base model chosen. Model search automates the selection of such a base model by evaluating the suitability of candidate models for a specific task. This entails inference with each candidate model on task-specific data. With thousands of models available through model stores, the computational cost of model search is a major bottleneck for efficient transfer learning. In this work, we presentAlsatian, a novel model search system. Based on the observation that many candidate models overlap to a significant extent and following a careful bottleneck analysis, we propose optimization techniques that are applicable to many model search frameworks. These optimizations include: (i) splitting models into individual blocks that can be shared across models, (ii) caching of intermediate inference results and model blocks, and (iii) selecting a beneficial search order for models to maximize sharing of cached results. In our evaluation on state-of-the-art deep learning models from computer vision and natural language processing, we show thatAlsatianoutperforms baselines by up to 14x.
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- PAR ID:
- 10618186
- Publisher / Repository:
- SIGMOD 2025
- Date Published:
- Journal Name:
- Proceedings of the ACM on Management of Data
- Volume:
- 3
- Issue:
- 3
- ISSN:
- 2836-6573
- Page Range / eLocation ID:
- 1 to 27
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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