The Life and Times of Parasites: Rhythms in Strategies for Within-host Survival and Between-host Transmission
Bibliographic record
Abstract
Biological rhythms are thought to have evolved to enable organisms to organize their activities according to the earth's predictable cycles, but quantifying the fitness advantages of rhythms is challenging and data revealing their costs and benefits are scarce. More difficult still is explaining why parasites that live exclusively within the bodies of other organisms have biological rhythms. Rhythms exist in the development and traits of parasites, in host immune responses, and in disease susceptibility. This raises the possibility that timing matters for how hosts and parasites interact and, consequently, for the severity and transmission of diseases. Here, we take an evolutionary ecological perspective to examine why parasites exhibit biological rhythms and how their rhythms are regulated. Specifically, we examine the adaptive significance (evolutionary costs and benefits) of rhythms for parasites and explore to what extent interactions between hosts and parasites can drive rhythms in infections. That parasites with altered rhythms can evade the effects of control interventions underscores the urgent need to understand how and why parasites exhibit biological rhythms. Thus, we contend that examining the roles of biological rhythms in disease offers innovative approaches to improve health and opens up a new arena for studying host-parasite (and host-parasite-vector) coevolution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".