Bibliographic record
Abstract
Mycotoxins are fungal secondary metabolites that contaminate a wide range of agricultural commodities worldwide.Ochratoxin A (OTA) is of particular interest as it is one of the most abundant food-contaminating mycotoxins.Produced by numerous fungal species from the Aspergillus and Penicillium genera, research suggests that OTA may be teratogenic as well as immunotoxic to humans and carcinogenic to rodents.In addition to its toxicity, OTA is a stable molecule that can resist most food processing and does not result in any visible damage to crops, therefore careful testing is required of all crop samples prior to processing.Aptamers are single-stranded oligonucleotides, typically DNA or RNA, that are capable of specifically interacting with high affinity to a desired target.Recently, several groups have developed aptamers for OTA.We have aimed to understand the secondary structures of these aptamers using a DNase I assay.It is also useful for future applications of these aptamers, to compare their affinities using the same analytical techniques and conditions.The affinity of each aptamer to OTA has been tested using a DNase I assay, as well as a magnetic bead affinity assay.We found that using K d affinity methods to compare aptamers was unreliable and instead developed several direct competitive affinity tests.Initial results appear promising, however further testing is required.Once the optimal conditions are found, the optimal aptamer can be chosen for use in existing antibody technology.This can be done to develop useful sensors and detection methods, such as the preparation of a lateral flow assay for cost efficient, onsite detection of OTA.The production of these novel detection platforms will allow rapid detection of this problematic mycotoxin, iii reducing the amount of grain waste and the cost of detection as well as ultimately reducing the exposure of mycotoxins to humans.v Kerry, you have been my biggest supporter throughout all of my years at Carleton.You have always believed in me no matter what, and it has really helped me get through rough times and that means so much!Carleton will always remind me of you.Emily, you are such a kind and caring person and you have been there for me so many times over the past two years.Thank you for always listening when I needed it most.You are going to do great things, and I can't wait to see that happen.Erin, girl...I can't even ha.Thank you for all of the help you have given me, both in the lab, and out of it!You are so brilliant, I swear you sparkle... with glitter!Thank you for having faith in me when I didn't have faith in myself.I will miss our late night science dates, trips to St. Hubert's, and our photo shoots (but basically I will miss your hair).Daffy, thank you for always engaging me in my endless cat loving conversations (and only "cageing me a few times ha")!Monica, thank you for your help on so many occasions!Your love of organic chemistry, while it is still unthinkable to me, is something I admire!Nadine, your optimism and outlook on life truly inspire me!Thank you for all of your encouragement and help over the last year!McKenzie, my lab baby
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".