Energy metabolism and cytochrome oxidase activity: linking metabolism to gene expression
Bibliographic record
Abstract
Modification of mitochondrial content demands the synthesis of hundreds of proteins encoded by nuclear and mitochondrial genomes. The responsibility for coordination of this process falls to nuclear-encoded master regulators of transcription. DNA-binding proteins and coactivators integrate information from energy-sensing pathways and hormones to alter mitochondrial gene expression. In mammals, the signaling cascade for mitochondrial biogenesis can be described as follows: hormonal signals and energetic information are sensed by protein-modifying enzymes that in turn regulate the post-translational modification of transcription factors. Once activated, transcription-factor complexes form on promoter elements of many of the nuclear-encoded mitochondrial genes, recruiting proteins that remodel chromatin and initiate transcription. One master regulator in mammals, PGC-1α, is well studied because of its role in determining the metabolic phenotype of muscles, but also due to its importance in mitochondria-related metabolic diseases. However, relatively little is known about the role of this pathway in other vertebrates. These uncertainties raise broader questions about the evolutionary origins of the pathway and its role in generating the diversity in muscle metabolic phenotypes seen in nature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".