Bibliographic record
Abstract
This thesis applies x-ray diffraction to measure he membrane structure of lipopolysaccharides and \nto develop a better model of a LPS bacterial melilbrane that can be used for biophysical research on \nantibiotics that attack cell membranes. \\iVe ha'e Inodified the Physics department x-ray machine \nfor use 3.'3 a thin film diffractometer, and have lesigned a new temperature and relative humidity \ncontrolled sample cell.\\Ve tested the sample eel: by measuring the one-dimensional electron density \nprofiles of bilayers of pope with 0%, 1%, 1G :VcJ, and 100% by weight lipo-polysaccharide from \nPse'udo'lTwna aeTuginosa. \nBackground \nVVe now know that traditional p,ntibiotics ,I,re losing their effectiveness against ever-evolving \nbacteria. This is because traditional antibiotic: work against specific targets within the bacterial \ncell, and with genetic mutations over time, themtibiotic no longer works. \nOne possible solution are antimicrobial peptides. These are short proteins that are part of the \nimmune systems of many animals, and some of them attack bacteria directly at the membrane of \nthe cell, causing the bacterium to rupture and die. Since the membranes of most bacteria share \ncommon structural features, and these featuret, are unlikely to evolve very much, these peptides \nshould effectively kill many types of bacteria wi Lhout much evolved resistance. \nBut why do these peptides kill bacterial cel: '3 , but not the cells of the host animal? For gramnegative \nbacteria, the most likely reason is that t Ileir outer membrane is made of lipopolysaccharides \n(LPS), which is very different from an animal :;ell membrane. Up to now, what we knovv about \nhow these peptides work was likely done with r !10spholipid models of animal cell membranes, and \nnot with the more complex lipopolysa,echaricies, If we want to make better pepticies, ones that we \ncan use to fight all types of infection, we need a more accurate molecular picture of how they \\vork. \nThis 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".