Rearing and Experimental Uses of <i>Daphnia</i>: Controlling Animal Nutrition and Assessing Lifespan and Life‐History Traits
Bibliographic record
Abstract
Caloric restriction has been found to extend the lifespan of many organisms including mammals and other vertebrates. With lifespans exceeding months to years, age-related experiments involving fish and mammals can be overtly costly, both in terms of time and funding. The freshwater crustacean, Daphnia, has a relatively short lifespan (∼50 to 100 days), which makes it a cost-effective alternative animal model for longevity and aging studies. Besides age-specific mortality, there are a suite of physiological responses connected to "healthspan" that can be tracked as these animals age including growth, reproduction, and metabolic rates. These responses can be complemented by assessment of molecular and cellular processes connected to aging and health. Lifespan and metabolism of this model organism is responsive to long studied modulators of aging, such as rearing temperature and nutritional manipulation, but also pharmacological agents that target aging, e.g., rapamycin, which adds to its usefulness as a model organism. Here we describe how to culture Daphnia for aging experiments including maintaining laboratory populations of Daphnia mothers, growing algal food, and manipulating nutrition of these animals. In addition, we provide methods for tracking common physiological and longevity responses of Daphnia. This protocol provides researchers planning to use this model organism with methods to establish and maintain Daphnia populations and to standardize their experimental approaches. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Culturing algae for Daphnia food Basic Protocol 2: General methods for culturing Daphnia Basic Protocol 3: Standardizing and controlling nutrition for experimental Daphnia Basic Protocol 4: Monitoring Daphnia lifespan Basic Protocol 5: Evaluating Daphnia health: Heart rate and respiration, body mass and growth rates, and reproduction.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".