Crafting a Lower CC Lens for Reducing Cost and Specimen Damage in Cryo-EM
Notice bibliographique
Résumé
Single particle cryo-EM is poised to surpass x-ray crystallography as the most successful technique for resolving membrane protein structures, but the standard 300kV acceleration voltage of most commercially available cryomicroscopes contributes heavily to radiation damage and is highly expensive to maintain [1]. Recently published work by Russo et al., from the Medical Research Council Laboratory of Molecular Biology (MRC-LMB) in Cambridge, has advocated for a low (<2mm) chromatic aberration coefficient (CC) objective lens (OL) for use in a purpose-built 100kV cryo-TEM [1], which they determine to be the optimal electron energy for structural determination of many biological specimens in terms of maximizing information at minimal cost [2-4]. This previously unmet target specification requires a bespoke OL pole-piece designed to minimize CC. This necessitates the testing of a large multitude of candidate geometries in accordance with various parameters, which is greatly time consuming if done manually. By writing an evolutionary algorithm that defines and evaluates many geometries in parallel, using multiphysics and electron optics simulations, we have achieved the accelerated design and manufacture of such a pole-piece. The algorithm operates using the Dawninan theory of evolution, by creating an initial generation of candidates, evaluating their fitness with a customized cost function, then constructing a new generation using elitism (keeping the best-performing candidates from the previous generation), crossover (creating offspring geometries which mix the characteristics of two existing candidates), and mutation (randomly altering one or more nodes of an existing geometry). For each candidate in a generation, magnetic flux simulations are performed by COMSOL Multiphysics, which outputs comma separated values of flux data along the optical axis. This data is utilized by an electron optics simulation written in Julia, which calculates the lens aberrations and the back focal plane. Python then evaluates the candidate in accordance with the fitness function and ranks it against the rest of the candidates in that generation. It thereby categorizes the candidates into elites, mutatables and crossover parents, and accordingly creates the next generation of candidates to be tested. This is repeated until the generation quota is reached. A visual summary of the algorithmic process is displayed in Fig. 1. A dashboard of plots, generated for the fittest member of each generation, is shown in Fig. 2 for one sample candidate geometry. The algorithm generated a machinable pole-piece design with a simultaed CC nearing sub-1mm, a promising result. From this, a physical prototype of the final candidate geometry was developed and installed in a JEOL 1400 model TEM. It is currently undergoing testing by the Russo group in MRC-LMB, with results (experimental CC value and microscope images) expected by early summer. Future work will include improving the parallelization of the multiphysics simulations for increased efficiency, extending the functionality to more TEM models, and designing and evaluating new cost functions to accommodate for a wider range of applications (e.g. tomography, spectroscopy, in-situ microscopy etc.) [5]. (left) Illustration of the process used by the algorithm to create a new generation of candidates. The user can specify the proportions in which the generations are categorized. (right) flow chart outlining the algorithmic process. Dashboard for monitoring the geometry evolution. Main figure shows a cross-section of the current geometry, subplots show the candidate's magnetic flux profile and the tracking of various parameters, most importantly aberration coefficients, as the number of generations increases.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,027 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».