Bibliographic record
Abstract
Magnetotactic bacteria (MTB) are ubiquitous in aquatic environments. They biomineralize membrane-bound magnetic nanoparticles (magnetosomes), which are magnetically single-domain, single crystals of either magnetite, Fe3O4, or greigite, Fe3S4. The chain is a strong magnetic dipole, which aligns the cell with the earth’s magnetic field (magnetotaxis) and, together with chemical signatures (aerotaxis), is believed to increase the efficiency of the organism in finding an optical oxygen concentration in the sediments. As the simplest organisms, in which biomineralization occurs, magnetotactic bacteria serve as an ideal model to study biomineralization mechanism. In this research, soft X-ray STXM (scanning transmission X-ray microscopy) was used to characterize the chemistry and magnetism of magnetotactic bacteria (MTB) on an individual cell and an individual magnetosome basis. Two types of MTB, Candidatus Magnetovibrio blakemorei strain MV-1 and multicellular magnetotoactic prokaryotes (MMPs) were studied in this project. In addition, ptychography technique, which does not rely on X-ray optics and holds promise for imaging with wavelength-limited resolution, is used to study biomineralization and magnetism of MTB cells. A spatial resolution of 7 nm below 1000 eV is achieved with ptychography, which is the highest in the soft X-ray region so far. Precursor-like and immature magnetosomes in intact MV-1 cells with ptychography are observed and a model for the pathway of magnetosome biomineralization for MV-1 is proposed. Our results demonstrate ptychography offers a superior means to characterize the chemical and magnetic properties of magnetotactic bacteria at the individual magnetosome level.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".