Control of selective mRNA translation over the torpor-arousal cycle of thirteen-lined ground squirrels
Bibliographic record
Abstract
Mammalian hibernation is an interesting adaptation that allows many capable animals like the thirteen-lined ground squirrel to endure the winter months on a low or absent food supply.Metabolic suppression during hibernation is facilitated by diverse biochemical mechanisms including the global shut-down of energy expensive processes like transcription and translation, the use of post-translational modifications to regulate protein activity, and differential gene/protein expression of essential protein products. Differential protein expression can occur during hibernation, but it remains incompletelyunderstood how transcripts are "chosen" to be translated instead of stored or degraded.This thesis explores two mechanisms that may regulate differential gene and protein expression during hibernation, including RNA-binding protein (RBP) stabilization and transport of transcripts, and translation machinery activation.Notably, RBPs and cap-dependent translation factors were upregulated over the torpor-arousal cycle.These results suggest a possible role for proteins that regulate mRNA stability and enhance translation during metabolic suppression.freedom to investigate the story that is my thesis, having confidence in my ability to take on several side projects and writing tasks on top of my thesis, and for subtly hinting when it was a good time to stop working on arduous projects, but giving me the time to make the decision to quit them on my own.My time in your lab has made me a more confident and deliberate scientist, mentor, and writer, and I am extremely grateful for the opportunities that you have supported me in pursuing.I'd also like to thank Jan Storey for meticulously editing this thesis as well as the manuscripts that I was able to publish (and not be embarrassed by) over the past 2 years.Although I have learned so much from your comments, I know I have a long way to go until my writing is as precise and flawless as yours.Thank you for being such an excellent mentor and role model for my writing and editing.Thanks to all Storey lab members for your academic support throughout the years.It has been fun debating everything from biochemistry theory to news stories, and I have learned so much from you all.Special thanks to Bryan Luu for continuing to answer my questions throughout my MSc.The example you have set in terms of your leadership, integrity, and scientific knowledgebase has taught me to push my boundaries in order to put my potential to the test and how to become a better scientist.Last but not least, I'd like to thank my "common-law husband", Ryan Deslauriers, for listening to me read out my writing for hours-on-end of editing, giving me feedback on my practice presentations before major conferences, and easing
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".