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Record W2118280486 · doi:10.1081/cbi-100102109

pH HOMEOSTASIS OF THE CIRCADIAN SPORULATION RHYTHM IN CLOCK MUTANTS OF<i>NEUROSPORA CRASSA</i>

2000· article· en· W2118280486 on OpenAlexfundno aff
Peter Ruoff, Amrollah Behzadi, Marianne Hauglid, Merete Vinsjevik, Harald Havås

Bibliographic record

VenueChronobiology International · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsnot available
FundersFonds de Recherche du Québec - Santé
KeywordsNeurospora crassaExtracellularMutantNeurosporaBiologyWild typeCircadian rhythmHomeostasisSporeBiochemistryIntracellularLimitingCell biologyBotanyEndocrinologyGene

Abstract

fetched live from OpenAlex

The influence of environmental (extracellular) pH on the sporulation rhythm in Neurospora crassa was investigated for wild-type (frq+) and the mutants chr, frq1, frq7, and frq8. In all mutants, including wild type, the growth rate was found to be influenced strongly by extracellular pH in the range 4-9. On the other hand, for the same pH range, the period length of the sporulation rhythm is little influenced in wild type, chr, and frq1. A loss of pH homeostasis of the period, however, was observed in the mutants frq7 and frq8, which also are known to have lost temperature compensation. Concerning the influence of extracellular pH on growth rates, a clear correspondence between growth rates and the concentration of available H2PO4- ion has been found, indicating that the uptake of H2PO4- may be a limiting factor for growth under our experimental conditions. The loss of pH compensation in the frq7 and frq8 mutants may be related to less easily degradable FRQ7,8 proteins when compared with wild-type FRQ. Results from recent model considerations and experimental results predict that, with increasing extra-and intracellular pH, the FRQ7 protein degradation increases and should lead to shorter period lengths.

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 score0.024
Threshold uncertainty score0.350

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 head0.005
GPT teacher head0.224
Teacher spread0.219 · 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

Citations27
Published2000
Admission routes1
Has abstractyes

Explore more

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