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Record W2162781148 · doi:10.1111/gbi.12088

3‐D analysis of bacterial cell‐(iron)mineral aggregates formed during Fe(<scp>II</scp>) oxidation by the nitrate‐reducing <i>Acidovorax sp</i>. strain BoFeN1 using complementary microscopy tomography approaches

2014· article· en· W2162781148 on OpenAlexfundno aff
G. Schmid, Fabian Zeitvogel, Likai Hao, Pablo Ingino, Matthias Floetenmeyer, York‐Dieter Stierhof, Birgit Schroeppel, Claus Burkhardt, Andreas Kappler, Martin Obst

Bibliographic record

VenueGeobiology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesCanadian Institutes of Health ResearchDeutsche Forschungsgemeinschaft
KeywordsChemistryTransmission electron microscopyScanning electron microscopeMineralChemical engineeringMaterials scienceNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

The formation of cell-(iron)mineral aggregates as a consequence of bacterial iron oxidation is an environmentally widespread process with a number of implications for processes such as sorption and coprecipitation of contaminants and nutrients. Whereas the overall appearance of such aggregates is easily accessible using 2-D microscopy techniques, the 3-D and internal structure remain obscure. In this study, we examined the 3-D structure of cell-(iron)mineral aggregates formed during Fe(II) oxidation by the nitrate-reducing Acidovorax sp. strain BoFeN1 using a combination of advanced 3-D microscopy techniques. We obtained 3-D structural and chemical information on different cellular encrustation patterns at high spatial resolution (4-200 nm, depending on the method): more specifically, (1) cells free of iron minerals, (2) periplasm filled with iron minerals, (3) spike- or platelet-shaped iron mineral structures, (4) bulky structures on the cell surface, (5) extracellular iron mineral shell structures, (6) cells with iron mineral filled cytoplasm, and (7) agglomerations of extracellular globular structures. In addition to structural information, chemical nanotomography suggests a dominant role of extracellular polymeric substances (EPS) in controlling the formation of cell-(iron)mineral aggregates. Furthermore, samples in their hydrated state showed cell-(iron)mineral aggregates in pristine conditions free of preparation (i.e., drying/dehydration) artifacts. All these results were obtained using 3-D microscopy techniques such as focused ion beam (FIB)/scanning electron microscopy (SEM) tomography, transmission electron microscopy (TEM) tomography, scanning transmission (soft) X-ray microscopy (STXM) tomography, and confocal laser scanning microscopy (CLSM). It turned out that, due to the various different contrast mechanisms of the individual approaches, and due to the required sample preparation steps, only the combination of these techniques was able to provide a comprehensive understanding of structure and composition of the various Fe-precipitates and their association with bacterial cells and EPS.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.207
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations55
Published2014
Admission routes1
Has abstractyes

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