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Results for “EMC algorithm”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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4 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Solving protein structure from sparse serial microcrystal diffraction data at a storage-ring synchrotron source

In recent years, the success of serial femtosecond crystallography and the paucity of beamtime at X-ray free-electron lasers have motivated the development of serial microcrystallography experiments at storage-ring synchrotron sources. However, especially at storage-ring sources, if a crystal is too small it will have suffered significant radiation damage before diffracting a sufficient number of X-rays into Bragg peaks for peak-indexing software to determine the crystal orientation. As a consequence, the data frames of small crystals often cannot be indexed and are discarded. Introduced here is a method based on the expand–maximize–compress (EMC) algorithm to solve protein structures, specifically from data frames for which indexing methods fail because too few X-rays are diffracted into Bragg peaks. The method is demonstrated on a real serial microcrystallography data set whose signals are too weak to be indexed by conventional methods. In spite of the daunting background scatter from the sample-delivery medium, it was still possible to solve the protein structure at 2.1 Å resolution. The ability of the EMC algorithm to analyze weak data frames will help to reduce sample consumption. Furthermore, it will also allow serial microcrystallography to be performed with crystals that are otherwise too small to be feasibly analyzed at storage-ring sources.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Unsupervised learning approaches to characterizing heterogeneous samples using X-ray single-particle imaging

One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. Proposed here are two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA), provides a rough classification which is essentially parameter free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs), can generate 3D structures of the objects at any point in the structural landscape. Both these methods are implemented in combination with the noise-tolerant expand–maximize–compress (EMC) algorithm and its utility is demonstrated by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern. Both discrete structural classes and continuous deformations are recovered. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility of moving beyond the study of homogeneous sample sets to addressing open questions on topics such as nanocrystal growth and dynamics, as well as phase transitions which have not been externally triggered.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Electron postmastectomy chest wall plus comprehensive nodal irradiation technique using Electron Monte Carlo dose algorithm

For left-sided postmastectomy patients requiring chest wall plus comprehensive nodal irradiation, sometimes traditional techniques such as partial wide tangents or electron/tangents combination are not feasible due to abnormal chest wall contour or heart position or unusually wide excision scar. We developed electron chest wall irradiation technique using Electron Monte Carlo (EMC) dose algorithm that will achieve heart sparing with acceptable ipsilateral lung dose, minimal contralateral lung, and breast dose. Ten left-sided postmastectomy patients with very challenging anatomy were selected for this dosimetry study. The en face electron fields were designed from a single isocenter and gantry angle with different energy beams using different cutouts that matched on the skin. Smaller energy was used in the central thin chest wall part and higher energy in the medial internal mammary nodes (IMN) area, superior part of the thick chest wall, and/or axillary nodal area. The electron fields were matched to the photon supraclavicular field in the superior region. Daily field junctions were used to feather the match lines between all the fields. Electron field dose calculations were done with Monte Carlo. Five patients' chest wall fields were planned with 6/9MeVcombination, 1 with 6/12 MeV, 2 with 9/12 MeV, 1 with 9/16 MeV, and 1 with 6/9/12 MeV. Institutional criteria of prescription dose of 50 Gy for target volumes and normal tissue dose were met with this technique in spite of the challenging anatomy. Mean heart dose averaged 3.0 Gy ± 0.8 Gy. For ipsilateral lung, V20Gy and V5Gy averaged 33.2% ± 4.5% and 64.6% ± 9.6%, respectively. For contralateral lung, V5Gy averaged 5.1% ± 5.0%. For contralateral breast, V5Gy averaged 3.3% ± 3.1%. The electron chest wall irradiation technique using EMC dose algorithm can provide adequate dose coverage to the chest wall, IMNs, and/or axillary nodes while achieving heart sparing with acceptable ipsilateral lung dose, minimal contralateral lung, and breast dose.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Studying the hadron structure with PANDA and CLAS using machine learning techniques

The hadron spectroscopy and structure are currently very active fields of research to study the non-perturbative regime of quantum chronodynamics. The first one studies the complex structure of excited hadrons by looking at their decay products, while the latter uses lepton scattering on nucleons. Both methods require reconstruction algorithms with great efficiency and good particle identification and background rejection rates. This work aims to provide these by either improving the existing methods or developing new ones. The first part of this document presents a feasibility study of a predicted hybrid charmonium state for the PANDA experiment. Lattice QCD calculations predict the ground state hybrid charmonium to be a spin exotic with quantum numbers of JP C = 1?+ at a mass of around 4.3 GeV with a width to be around 20 MeV. A machine learning based data analysis scheme is proposed to further improve the signal efficiency and the background reduction, alongside with improvements of the analysis software (PandaRoot), that are vital for this study. These improvements include a reworked clustering algorithm for the electromagnetic calorimeter (EMC) and an optimized monte carlo matching for neutral particles. The second part of this document is about studying the proton structure. A multidimensional study of the structure function ratio Fsin(?)LU /FUU has been performed for K±, based on the measurement of beam-spin asymmetries. It uses the high statistics data recorded with the CLAS12 spectrometer at Jefferson Laboratory. Fsin(?)LU is a twist-3 quantity that provides information about the quark gluon correlations in the proton. This document will present for the first time a simultaneous analysis of two kaon channels over a large kinematic range of z, xB , PT and Q2 with virtualities Q2 ranging from 1 GeV2 up to 8 GeV2 using machine learning techniques for improved particle identification.

Kripko, Aron↗