NIH's Consortium on Molecular Transducers of Physical Activity (MoTrPAC)
Bibliographic record
Abstract
Physical activity is beneficial to human health and well being across the lifespan. Regular physical activity brings about numerous health benefits, however most of the current studies are correlative and the molecular mechanisms that are the basis for these beneficial effects remain obscure. The NIH Common Fund initiated the Molecular Transducers of Physical Activity Consortium (MoTrPAC) in December 2016 by issuing 19 grants to 37 Principal Investigators from 23 institutions. The goals of MoTrPAC are to: Aim 1: Assemble a comprehensive map of the molecular changes that occur in response to exercise and provide insights into how they are altered by age, sex, body composition and fitness level. Aim 2: Develop a user‐friendly database to facilitate investigator‐initiated studies and catalyze the field of physical activity research whereby researchers can develop hypotheses exploring novel mechanisms by which physical activity improves or preserves health. MoTrPAC is a large discovery project that will explore and document molecular signatures/markers of the exercise response to physical activity in humans and animals. The human studies are a multi‐center clinical trial cohort of people from 10–80 years of age. Animal studies have been conducted and tissues harvested from 6 and 18‐month old F344 rats before and at seven time points post acute exercise. Other rats underwent a training regime ranging from one to eight weeks and tissues harvested 48 hours after the last bought. Three of the animal tissues harvested are the same as those harvested from human subjects. A total of 18 tissues were harvested from rats. Multiple state of art and omics platforms including genomic, transcriptomic, epigenomic, proteomic and metabolomics will be employed to collect extensive data sets that will serve as a basis for future investigator initiated studies for the research community to advance our understanding of the molecular mechanisms of exercises effects. This presentation will describe various components of MoTrPAC consortium and provide information on its current activities. Support or Funding Information NIH Common Fund; Office of the NIH Director This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.017 |
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".