Acta Crystallographica Section A: Foundations and Advances最新文献_第10页

An invertible seven-dimensional Dirichlet cell characterization of lattices. 网格的可逆七维 Dirichlet 单元特征。

IF 1.9 4区材料科学

Acta Crystallographica Section A: Foundations and Advances Pub Date : 2023-07-01 Epub Date: 2023-06-20 DOI: 10.1107/S2053273323003121

Herbert J Bernstein, Lawrence C Andrews, Mario Xerri

{"title":"An invertible seven-dimensional Dirichlet cell characterization of lattices.","authors":"Herbert J Bernstein, Lawrence C Andrews, Mario Xerri","doi":"10.1107/S2053273323003121","DOIUrl":"10.1107/S2053273323003121","url":null,"abstract":"Characterization of crystallographic lattices is an important tool in structure solution, crystallographic database searches and clustering of diffraction images in serial crystallography. Characterization of lattices by Niggli-reduced cells (based on the three shortest non-coplanar lattice vectors) or by Delaunay-reduced cells (based on four non-coplanar vectors summing to zero and all meeting at obtuse or right angles) is commonly performed. The Niggli cell derives from Minkowski reduction. The Delaunay cell derives from Selling reduction. All are related to the Wigner-Seitz (or Dirichlet, or Voronoi) cell of the lattice, which consists of the points at least as close to a chosen lattice point as they are to any other lattice point. The three non-coplanar lattice vectors chosen are here called the Niggli-reduced cell edges. Starting from a Niggli-reduced cell, the Dirichlet cell is characterized by the planes determined by 13 lattice half-edges: the midpoints of the three Niggli cell edges, the six Niggli cell face-diagonals and the four body-diagonals, but seven of the lengths are sufficient: three edge lengths, the three shorter of each pair of face-diagonal lengths, and the shortest body-diagonal length. These seven are sufficient to recover the Niggli-reduced cell.","PeriodicalId":106,"journal":{"name":"Acta Crystallographica Section A: Foundations and Advances","volume":"79 Pt 4","pages":"369-380"},"PeriodicalIF":1.9,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10317136/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10126914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

André Authier (1932-2023). 安德烈·奥蒂尔（1932-2023）。

IF 1.8 4区材料科学

Acta Crystallographica Section A: Foundations and Advances Pub Date : 2023-07-01 DOI: 10.1107/S2053273323005120

Yves Epelboin

引用次数: 0

New benchmarks in the modelling of X-ray atomic form factors. x射线原子形状因子建模的新基准。

IF 1.8 4区材料科学

Acta Crystallographica Section A: Foundations and Advances Pub Date : 2023-07-01 DOI: 10.1107/S2053273323003996

Gunnar Thorkildsen

引用次数: 0

Uri Shmueli (1928-2023). 乌里·什穆埃利（1928-2023）。

IF 1.8 4区材料科学

Acta Crystallographica Section A: Foundations and Advances Pub Date : 2023-07-01 DOI: 10.1107/S2053273323005405

Carolyn P Brock

引用次数: 0

Efficient structure-factor modeling for crystals with multiple components. 多成分晶体的高效结构因子建模。

IF 1.9 4区材料科学

Acta Crystallographica Section A: Foundations and Advances Pub Date : 2023-07-01 Epub Date: 2023-06-20 DOI: 10.1107/S205327332300356X

Pavel V Afonine, Paul D Adams, Alexandre G Urzhumtsev

{"title":"Efficient structure-factor modeling for crystals with multiple components.","authors":"Pavel V Afonine, Paul D Adams, Alexandre G Urzhumtsev","doi":"10.1107/S205327332300356X","DOIUrl":"10.1107/S205327332300356X","url":null,"abstract":"Diffraction intensities from a crystallographic experiment include contributions from the entire unit cell of the crystal: the macromolecule, the solvent around it and eventually other compounds. These contributions cannot typically be well described by an atomic model alone, i.e. using point scatterers. Indeed, entities such as disordered (bulk) solvent, semi-ordered solvent (e.g. lipid belts in membrane proteins, ligands, ion channels) and disordered polymer loops require other types of modeling than a collection of individual atoms. This results in the model structure factors containing multiple contributions. Most macromolecular applications assume two-component structure factors: one component arising from the atomic model and the second one describing the bulk solvent. A more accurate and detailed modeling of the disordered regions of the crystal will naturally require more than two components in the structure factors, which presents algorithmic and computational challenges. Here an efficient solution of this problem is proposed. All algorithms described in this work have been implemented in the computational crystallography toolbox (CCTBX) and are also available within Phenix software. These algorithms are rather general and do not use any assumptions about molecule type or size nor about those of its components.","PeriodicalId":106,"journal":{"name":"Acta Crystallographica Section A: Foundations and Advances","volume":"79 Pt 4","pages":"345-352"},"PeriodicalIF":1.9,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10317137/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9749927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning for classifying narrow-beam electron diffraction data. 窄束电子衍射数据分类的机器学习。

IF 1.8 4区材料科学

Acta Crystallographica Section A: Foundations and Advances Pub Date : 2023-07-01 DOI: 10.1107/S2053273323004680

Senik Matinyan, Burak Demir, Pavel Filipcik, Jan Pieter Abrahams, Eric van Genderen

{"title":"Machine learning for classifying narrow-beam electron diffraction data.","authors":"Senik Matinyan, Burak Demir, Pavel Filipcik, Jan Pieter Abrahams, Eric van Genderen","doi":"10.1107/S2053273323004680","DOIUrl":"https://doi.org/10.1107/S2053273323004680","url":null,"abstract":"As an alternative approach to X-ray crystallography and single-particle cryo-electron microscopy, single-molecule electron diffraction has a better signal-to-noise ratio and the potential to increase the resolution of protein models. This technology requires collection of numerous diffraction patterns, which can lead to congestion of data collection pipelines. However, only a minority of the diffraction data are useful for structure determination because the chances of hitting a protein of interest with a narrow electron beam may be small. This necessitates novel concepts for quick and accurate data selection. For this purpose, a set of machine learning algorithms for diffraction data classification has been implemented and tested. The proposed pre-processing and analysis workflow efficiently distinguished between amorphous ice and carbon support, providing proof of the principle of machine learning based identification of positions of interest. While limited in its current context, this approach exploits inherent characteristics of narrow electron beam diffraction patterns and can be extended for protein data classification and feature extraction.","PeriodicalId":106,"journal":{"name":"Acta Crystallographica Section A: Foundations and Advances","volume":"79 Pt 4","pages":"360-368"},"PeriodicalIF":1.8,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10317134/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10126915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A note on the wedge reversion antisymmetry operation and 51 types of physical quantities in arbitrary dimensions. 关于楔形反转反对称运算和任意维51种物理量的注解。

IF 1.8 4区材料科学

Acta Crystallographica Section A: Foundations and Advances Pub Date : 2023-07-01 DOI: 10.1107/S2053273323003303

Piotr Fabrykiewicz

引用次数: 0

On automatic determination of quasicrystal orientations by indexing of detected reflections. 利用探测到的反射标度自动确定准晶体取向。

IF 1.8 4区材料科学

Acta Crystallographica Section A: Foundations and Advances Pub Date : 2023-07-01 DOI: 10.1107/S205327332300373X

Adam Morawiec

引用次数: 0

Crystal search - feasibility study of a real-time deep learning process for crystallization well images. 晶体搜索-一个实时深度学习过程的可行性研究结晶井图像。

IF 1.8 4区材料科学

Acta Crystallographica Section A: Foundations and Advances Pub Date : 2023-07-01 DOI: 10.1107/S2053273323001948

Yvonne Thielmann, Thorsten Luft, Norbert Zint, Juergen Koepke

{"title":"Crystal search - feasibility study of a real-time deep learning process for crystallization well images.","authors":"Yvonne Thielmann, Thorsten Luft, Norbert Zint, Juergen Koepke","doi":"10.1107/S2053273323001948","DOIUrl":"https://doi.org/10.1107/S2053273323001948","url":null,"abstract":"To avoid the time-consuming and often monotonous task of manual inspection of crystallization plates, a Python-based program to automatically detect crystals in crystallization wells employing deep learning techniques was developed. The program uses manually scored crystallization trials deposited in a database of an in-house crystallization robot as a training set. Since the success rate of such a system is able to catch up with manual inspection by trained persons, it will become an important tool for crystallographers working on biological samples. Four network architectures were compared and the SqueezeNet architecture performed best. In detecting crystals AlexNet accomplished a better result, but with a lower threshold the mean value for crystal detection was improved for SqueezeNet. Two assumptions were made about the imaging rate. With these two extremes it was found that an image processing rate of at least two times, but up to 58 times in the worst case, would be needed to reach the maximum imaging rate according to the deep learning network architecture employed for real-time classification. To avoid high workloads for the control computer of the CrystalMation system, the computing is distributed over several workstations, participating voluntarily, by the grid programming system from the Berkeley Open Infrastructure for Network Computing (BOINC). The outcome of the program is redistributed into the database as automatic real-time scores (ARTscore). These are immediately visible as colored frames around each crystallization well image of the inspection program. In addition, regions of droplets with the highest scoring probability found by the system are also available as images.","PeriodicalId":106,"journal":{"name":"Acta Crystallographica Section A: Foundations and Advances","volume":"79 Pt 4","pages":"331-338"},"PeriodicalIF":1.8,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10317135/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9750632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the combinatorics of crystal structures. II. Number of Wyckoff sequences of a given subdivision complexity. 论晶体结构的组合学。2给定细分复杂度的Wyckoff序列的个数。

IF 1.8 4区材料科学

Acta Crystallographica Section A: Foundations and Advances Pub Date : 2023-05-01 DOI: 10.1107/S2053273323002437

Wolfgang Hornfeck, Kamil Červený

{"title":"On the combinatorics of crystal structures. II. Number of Wyckoff sequences of a given subdivision complexity.","authors":"Wolfgang Hornfeck, Kamil Červený","doi":"10.1107/S2053273323002437","DOIUrl":"https://doi.org/10.1107/S2053273323002437","url":null,"abstract":"Wyckoff sequences are a way of encoding combinatorial information about crystal structures of a given symmetry. In particular, they offer an easy access to the calculation of a crystal structure's combinatorial, coordinational and configurational complexity, taking into account the individual multiplicities (combinatorial degrees of freedom) and arities (coordinational degrees of freedom) associated with each Wyckoff position. However, distinct Wyckoff sequences can yield the same total numbers of combinatorial and coordinational degrees of freedom. In this case, they share the same value for their Shannon entropy based subdivision complexity. The enumeration of Wyckoff sequences with this property is a combinatorial problem solved in this work, first in the general case of fixed subdivision complexity but non-specified Wyckoff sequence length, and second for the restricted case of Wyckoff sequences of both fixed subdivision complexity and fixed Wyckoff sequence length. The combinatorial results are accompanied by calculations of the combinatorial, coordinational, configurational and subdivision complexities, performed on Wyckoff sequences representing actual crystal structures.","PeriodicalId":106,"journal":{"name":"Acta Crystallographica Section A: Foundations and Advances","volume":"79 Pt 3","pages":"280-294"},"PeriodicalIF":1.8,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178003/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9451260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0