Bibliographic record
Abstract
This thesis applies x-ray diffraction to measure he membrane structure of lipopolysaccharides and to develop a better model of a LPS bacterial melilbrane that can be used for biophysical research on antibiotics that attack cell membranes. \iVe ha'e Inodified the Physics department x-ray machine for use 3.'3 a thin film diffractometer, and have lesigned a new temperature and relative humidity controlled sample cell.\Ve tested the sample eel: by measuring the one-dimensional electron density profiles of bilayers of pope with 0%, 1%, 1G :VcJ, and 100% by weight lipo-polysaccharide from Pse'udo'lTwna aeTuginosa. Background VVe now know that traditional p,ntibiotics ,I,re losing their effectiveness against ever-evolving bacteria. This is because traditional antibiotic: work against specific targets within the bacterial cell, and with genetic mutations over time, themtibiotic no longer works. One possible solution are antimicrobial peptides. These are short proteins that are part of the immune systems of many animals, and some of them attack bacteria directly at the membrane of the cell, causing the bacterium to rupture and die. Since the membranes of most bacteria share common structural features, and these featuret, are unlikely to evolve very much, these peptides should effectively kill many types of bacteria wi Lhout much evolved resistance. But why do these peptides kill bacterial cel: '3 , but not the cells of the host animal? For gramnegative bacteria, the most likely reason is that t Ileir outer membrane is made of lipopolysaccharides (LPS), which is very different from an animal :;ell membrane. Up to now, what we knovv about how these peptides work was likely done with r !10spholipid models of animal cell membranes, and not with the more complex lipopolysa,echaricies, If we want to make better pepticies, ones that we can use to fight all types of infection, we need a more accurate molecular picture of how they \vork. This will hopefully be one step forward to the ( esign of better treatments for bacterial infections.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".