A microbe-derived treatment to help inhibit white-nose syndrome in hibernating North American bats
Bibliographic record
Abstract
Pseudogymnoascus destructans (P.destructans) is known to be the causative agent of White-Nose Syndrome in hibernating North American bats.To date, this disease has caused largescale mortality in bat populations present in 25 US states and 5 Canadian provinces.White Nose Syndrome is associated with a decrease in fat reserves and a substantial loss in water and electrolytes.This disturbance in normal metabolism leads to frequent arousal periods during hibernation.While fighting against the disease, exhaustion of compensatory mechanisms leads to mortality.Probiotics and microbe-derived treatments are the likely solution for managing White Nose Syndrome since introducing foreign antifungals can affect an already sensitive cave environment.This study examines the inhibitory effect of one Penicillium spp.isolate on P. destructans.Sanger sequencing and NCBI BLAST confirmed the identity of the Penicillium spp.The isolate was identified to be Penicillium herquei.Using pairwise testing plates, the fungus has been shown to inhibit the growth of P. destructans over the course of two weeks.The growth curve of the isolate was tracked by measuring the dry mass and the absorbance of different liquid cultures over 10 days.The inhibition could either be due to resource or interference competition.The cell-free liquid culture was added to fresh media to make up plates that were subsequently inoculated with P. destructans.These plates had no to little growth which showed that the presence of the P. herquei is not crucial to the inhibition and that there is no resource competition between the two fungi.This indicates that the isolate probably secretes an inhibitory compound.Plates inoculated with the isolate were extracted with solvents of different polarities and then analyzed using a quadrupole time-of-flight mass spectrometer to determine the mass-to-charge ratio and the retention time of the inhibitory compound.April 26, 2023 I would also like to thank my thesis reader, Dr. David Chiasson, who was also my mentor for more than a year and a half.I started in Dr. Chiasson's lab with no prior research experience.I owe everything I know about microbiology and genetics to you, and I am forever grateful for recruiting me as a brand-new first-year student.Thank you for your guidance throughout my university life and for providing valuable feedback throughout my
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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".