Bibliographic record
Abstract
Pollution is, unfortunately, a permanent fixture of many environments. One particularly concerning pollutant is bisphenol A (BPA), a chemical that leaches out of plastic and can harm animals by mimicking the sex hormone oestrogen. Fortunately, BPA is being replaced increasingly in our plastics by supposedly ‘safer’ alternatives, such as bisphenol S (BPS) and bisphenol F (BPF). However, there are concerns that bisphenol pollutants can affect how animals respond to changing environments. How pollution and climate change – arguably humanity's two greatest legacies on Earth – interact remains a mystery. Nicholas Wu and Frank Seebacher from The University of Sydney, Australia, set out to understand how exposure to these common, physiology-modifying pollutants affected how fish performed when they turned up the heat.Wu and Seebacher exposed zebrafish to an environmentally relevant level of BPA (30 μg l–1), or one of its alternatives, in chilly (18oC) and warm (28oC) water. Then they checked how fast the fish could swim at both temperatures to get a sense of how the bisphenols interfered with the fish's ability to perform in changing environments. The BPA-exposed fish swam fine, but the fish exposed to the other bisphenols couldn't keep up: BPF and BPS tanked the top speed that all of the fish could attain. In short, it turned out that the ‘safer’ BPAs are not always less toxic.To investigate how BPF or BPS exposure slowed the fish down, the researchers turned their attention to two metabolic enzymes linked to performance: citrate synthase and lactate dehydrogenase. Citrate synthase exists in the mitochondria, which provide power to all tissues. Animals with more citrate synthase have more mitochondria and more metabolic power for exercise. Lactate dehydrogenase is also involved in exercise and sometimes in adaptation to warm or cold environmentsHow the bisphenols affected citrate synthase activity depended on both a fish's previous experience (if they were warm or cold when exposed to the chemicals) and the temperate at which they did their swim test, highlighting how pollution could hinder fish coping with the temperature fluctuations that occur naturally in the environment. Wu and Seebacher found that when the fish were swimming in warm (28oC) water, BPA increased their citrate synthase activity, but the enzyme was less active in the fish that had been transferred from cold to hot water. When the duo compared the effects of BPF on the enzyme, they found the opposite: higher citrate synthase activity in the fish from cold water and lower in the warm ones. In contrast, BPS reduced the enzyme's activity regardless of temperature. As the researchers had found when testing the fish's swimming abilities, BPS and BPF were just as, if not more, disruptive than the compound they were created to replace, despite their ‘less toxic’ reputation.The bisphenols also impacted lactate dehydrogenase activity, but the effect depended somewhat on temperature. BPA decreased lactate dehydrogenase activity in cold fish, but the plastic increased the enzyme's activity in the warm fish, following a similar pattern as citrate synthase. BPF and BPS exposure reduced lactate dehydrogenase activities in all cases except for the fish from and swimming in warm water after a BPF exposure, which saw an increase in lactate dehydrogenase activity instead. Just like with citrate synthase, BPS and BPF were just as, if not more, metabolically disruptive than the infamous BPA.Pollutants such as BPA and its replacements are common in many habitats and affect many physiological processes, such as metabolism, which are important for dealing with environmental challenges. Though fish exposed to bisphenols can still obtain the same top speed, they have a lot of idiosyncratic differences under the hood that limit their ability to rise to thermal challenges and beat the heat of climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".