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In situ detection of starch-hydrolyzing microorganisms in activated sludge

2008· article· en· W1962193666 on OpenAlexaff
Yun Xia, Yunhong Kong, Per Halkjær Nielsen

Bibliographic record

VenueFEMS Microbiology Ecology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsAgriculture and Agri-Food Canada
FundersTeknologi og Produktion, Det Frie Forskningsråd
KeywordsBiologyActivated sludgeActinobacteriaWastewaterSewage treatmentBacteriaStarchMicroorganismAmylasePolysaccharideFood scienceMicrobiologyBiochemistryEnzyme16S ribosomal RNA

Abstract

fetched live from OpenAlex

Polysaccharides constitute a significant part of the organic matter in domestic wastewater and their hydrolysis plays an important role in their transformation and nutrient removal in activated sludge wastewater treatment plants. However, there is no information available about the identity, ecophysiology, and abundance of starch-hydrolyzing organisms (SHOs) in these plants. In this study, fluorescence in situ enzyme staining with BODIPY fluorescein-labeled starch was applied and optimized to label SHOs expressing alpha-amylase in activated sludge plants. Fluorescence on the surface of bacteria expressing alpha-amylase activity was clearly visualized. In 11 full-scale nutrient-removing wastewater treatment plants examined, the morphotypes of the dominant SHOs were always cocci in clusters of tetrads, short rods in clusters, and some filamentous organisms. The SHOs were identified by combining in situ enzyme staining and FISH using a range of available oligonucleotide probes. All the SHOs observed were Actinobacteria, and most had the phenotype of polyphosphate-accumulating organisms closely related to the genus Tetrasphaera in the family Intrasporangiaceae. The SHOs were present in most of the wastewater treatment plants examined and comprised, in total, up to 11% of bacterial biovolume and thus formed an important part of the microbial communities.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.199
Teacher spread0.191 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations42
Published2008
Admission routes1
Has abstractyes

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