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Record W2321966964 · doi:10.1139/w2012-093

Effect of 300 mT static and 50 Hz 0.1 mT extremely low frequency magnetic fields on<i>Tuber borchii</i>mycelium

2012· article· en· W2321966964 on OpenAlexvenueno aff
Lucia Potenza, Roberta Saltarelli, Emanuela Polidori, Paola Ceccaroli, Antonella Amicucci, Sabrina Donati Zeppa, Alessandra Zambonelli, Vilberto Stocchi

Bibliographic record

VenueCanadian Journal of Microbiology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsnot available
FundersMinistero dell'Ambiente e della Tutela del Territorio e del Mare
KeywordsHyphaMyceliumDehydrogenaseHexokinaseBiologyGene expressionIn vitroGeneEnzymeBiochemistryChemistryGlycolysisHorticultureBotany

Abstract

fetched live from OpenAlex

The present work aimed to investigate whether exposure to static magnetic field (SMF) and extremely low frequency magnetic field (ELF-MF) can induce biomolecular changes on Tuber borchii hyphal growth. Tuber borchii mycelium was exposed for 1 h for 3 consecutive days to a SMF of 300 mT or an ELF-MF of 0.1 mT 50 Hz. Gene expression and biochemical analyses were performed. In mycelia exposed to ELF-MF, some genes involved in hyphal growth, investigated using quantitative real-time polymerase chain reaction, were upregulated, and the activity of many glycolytic enzymes was increased. On the contrary, no differences were observed in gene expression after exposure to SMF treatment, and only the activities of glucose 6-phosphate dehydrogenase and hexokinase increased. The data herein presented suggest that the electromagnetic field can act as an environmental factor in promoting hyphal growth and can be used for applicative purposes, such as the set up of new in vitro cultivation techniques.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.003
GPT teacher head0.199
Teacher spread0.196 · 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 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

Citations21
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of MicrobiologySame topicMagnetic and Electromagnetic EffectsFrench-language works237,207