Sensitive quantification of dipicolinic acid from bacterial endospores in soils and sediments
Bibliographic record
Abstract
Summary Endospore‐forming bacteria make up an important and numerically significant component of microbial communities in a range of settings including soils, industry, hospitals and marine sediments extending into the deep subsurface. Bacterial endospores are non‐reproductive structures that protect DNA and improve cell survival during periods unfavourable for bacterial growth. An important determinant of endospores withstanding extreme environmental conditions is 2,6‐pyridine dicarboxylic acid (i.e. dipicolinic acid, or DPA), which contributes heat resistance. This study presents an improved HPLC‐fluorescence method for DPA quantification using a single 10‐min run with pre‐column Tb 3+ chelation. Relative to existing DPA quantification methods, specific improvements pertain to sensitivity, detection limit and range, as well as the development of new free DPA and spore‐specific DPA proxies. The method distinguishes DPA from intact and recently germinated spores, enabling responses to germinants in natural samples or experiments to be assessed in a new way. DPA‐based endospore quantification depends on accurate spore‐specific DPA contents, in particular, thermophilic spores are shown to have a higher DPA content, meaning that marine sediments with plentiful thermophilic spores may require spore number estimates to be revisited. This method has a wide range of potential applications for more accurately quantifying bacterial endospores in diverse environmental samples.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".