Hasil untuk "Crystallography"

Sarah Knapp, Verena Weber, Maud Verheirstraeten et al.

Abstract Mono-ADP-ribosylation, a modification of both proteins and nucleic acids, is implicated in innate immunity. Intracellularly, this modification is catalyzed by PARP enzymes, some induced in response to interferons. Mono-ADP-ribosylation is reversed by hydrolases including proteins with macrodomains, which are conserved across all kingdoms of life. Macrodomains encoded by certain positive-sense single-stranded RNA viruses, such as Chikungunya virus and SARS-CoV-2, antagonize host MARylation to enhance viral replication and suppress the immune response. While macrodomain hydrolase activity is essential for CHIKV replication, in SARS-CoV-2 it predominantly contributes to immune evasion, underscoring viral macrodomains as potential antiviral drug targets. Efforts to develop macrodomain inhibitors include computational modeling, crystallography-based methods, and in vitro assays. However, tools to study macrodomain activity directly in cells remain rare. Here, we established a cell-based assay using PARP15 isoform 1, which we found forms nuclear foci dependent on its ADP-ribosyltransferase activity. Enzymatically active macrodomains dissolve these foci, enabling hydrolase activity monitoring in living cells. Using stable cell lines, this system allows the screening of macrodomain inhibitors while simultaneously addressing cell permeability, toxicity, and physiological relevance. Adaptable to various macrodomains, our platform offers a versatile tool to study macrodomain function in living cells, analyzing mutants, and advancing drug discovery efforts.

Ali Vosoughi, Ayoub Shahnazari, Yufeng Xi et al.

We introduce OPENXRD, a comprehensive benchmarking framework for evaluating large language models (LLMs) and multimodal LLMs (MLLMs) in crystallography question answering. The framework measures context assimilation, or how models use fixed, domain-specific supporting information during inference. The framework includes 217 expert-curated X-ray diffraction (XRD) questions covering fundamental to advanced crystallographic concepts, each evaluated under closed-book (without context) and open-book (with context) conditions, where the latter includes concise reference passages generated by GPT-4.5 and refined by crystallography experts. We benchmark 74 state-of-the-art LLMs and MLLMs, including GPT-4, GPT-5, O-series, LLaVA, LLaMA, QWEN, Mistral, and Gemini families, to quantify how different architectures and scales assimilate external knowledge. Results show that mid-sized models (7B--70B parameters) gain the most from contextual materials, while very large models often show saturation or interference and the largest relative gains appear in small and mid-sized models. Expert-reviewed materials provide significantly higher improvements than AI-generated ones even when token counts are matched, confirming that content quality, not quantity, drives performance. OPENXRD offers a reproducible diagnostic benchmark for assessing reasoning, knowledge integration, and guidance sensitivity in scientific domains, and provides a foundation for future multimodal and retrieval-augmented crystallography systems.

Bibhu P. Sahu, Mohsen T. Andani, Arkajit Ghosh et al.

The crystallography of the eutectic Al-Si microstructure in both unmodified and Sr (0.2 wt.%)-modified hypereutectic Al-20 wt.% Si alloys, processed via arc-melting and laser surface remelting, has been comprehensively characterized using transmission electron microscopy and electron diffraction. Although, under as-cast conditions, specific orientations between different planes of Al and Si, satisfying defined orientation relationships (ORs), have been investigated within the flake morphology, the rapid solidification induced by laser surface remelting results in a notable transformation from a flake morphology to nanocrystalline Si fibers dispersed in an Al matrix. Consequently, this transformation results in a mis-orientation of the interface between the eutectic Al and Si phases, preventing the formation of orientation relationships, thus promoting the formation of faceted interfaces exhibiting substantial lattice disregistry.

Silja Flenner, Adam Kubec, Christian David et al.

Nanotomography with hard X-rays is a widely used technique for high-resolution imaging, providing insights into the structure and composition of various materials. In recent years, tomographic approaches based on simultaneous illuminations of the same sample region from different angles by multiple beams have been developed at micrometre image resolution. Transferring these techniques to the nanoscale is challenging due to the loss in photon flux by focusing the X-ray beam. We present an approach for multi-beam nanotomography using a dual-beam Fresnel zone plate (dFZP) in a near-field holography setup. The dFZP generates two nano-focused beams that overlap in the sample plane, enabling the simultaneous acquisition of two projections from slightly different angles. This first proof-of-principle implementation of the dual-beam setup allows for the efficient removal of ring artifacts and noise using machine-learning approaches. The results open new possibilities for full-field multi-beam nanotomography and pave the way for future advancements in fast holotomography and artifact-reduction techniques.

R. Oishi-Tomiyasu

In ab-initio indexing, for a given diffraction/scattering pattern, the unit-cell parameters and the Miller indices assigned to reflections in the pattern are determined simultaneously. "Ab-initio" means a process performed without any good prior information on the crystal lattice. Newly developed ab-initio indexing software is frequently reported in crystallography. However, it is not widely recognized that use of a Bravais lattice determination method, which is tolerant to experimental errors, can simplify indexing algorithms and increase their success rates. One of the goals of this article is to collect information on the lattice-basis reduction theory and its applications. The main result is Bravais lattice determination algorithm for 2D lattices, along with a mathematical proof that it works even for parameters containing large observational errors. As in our error-stable algorithm for 3D lattices, it uses two lattice-basis reduction methods that seem to be optimal for different symmetries. In indexing, a method for error-stable unit-cell identification is also required to exclude duplicate solutions. We introduce several methods to measure the difference of unit cells known in crystallography and mathematics.

Chen Dun, Qiutai Pan, Shikai Jin et al.

Determining the structure of a protein has been a decades-long open question. A protein's three-dimensional structure often poses nontrivial computation costs, when classical simulation algorithms are utilized. Advances in the transformer neural network architecture -- such as AlphaFold2 -- achieve significant improvements for this problem, by learning from a large dataset of sequence information and corresponding protein structures. Yet, such methods only focus on sequence information; other available prior knowledge, such as protein crystallography and partial structure of amino acids, could be potentially utilized. To the best of our knowledge, we propose the first transformer-based model that directly utilizes protein crystallography and partial structure information to predict the electron density maps of proteins. Via two new datasets of peptide fragments (2-residue and 15-residue) , we demonstrate our method, dubbed \texttt{CrysFormer}, can achieve accurate predictions, based on a much smaller dataset size and with reduced computation costs.

Eunyoung Park, Jiho Lee, Jeong-Han Kim et al.

Abstract The crystal structures of the pyribencarb E and Z stereoisomers were determined using single-crystal X-ray crystallography. The isomers were confirmed a single data respectively by crystal analysis, LC-UVD mass spectrometry, and NMR spectroscopy. Pyribencarb E crystallizes in triclinic P − 1 and the Z isomer in monoclinic P21/c, with the crystal structures showing comparable packing motifs. Moreover, molecular docking was carried out with cytochrome bc 1, revealing binding energies in the ranges of − 24.9 to − 17.6 and − 21.6 to − 14.7 kcal/mol for the E and Z isomers, respectively. Through a combined experimental and theoretical approach, this study contributes to our understanding of pesticides. Graphical Abstract

Aleksandar Nikolov, Lili Dobreva, Svetla Danova et al.

Many antimicrobial coatings deliver a peak release of antimicrobial agent at an early age, after which they lost antimicrobial activity over time. In the present study a novel geopolymer paints with long term antimicrobial activity were developed based on natural zeolite modified with silver and copper ions. The obtained geopolymer paints were applied by brushing on concrete, ceramic, gypsum paperboard and steel. The coating was characterized by excellent adhesive strength and hiding properties. The long-term antimicrobial effect was evaluated by accelerated aging in carbonation chamber. Microstructural changes were analyzed by powder X-ray diffraction and Fourier transformed infrared spectroscopy. Cytotoxicity, antibacterial, antifungal and virucidal properties were investigated on raw and carbonated geopolymer paints. Geopolymer paints based on modified natural zeolite seems promising antimicrobial coating material that can be implemented in the global fight against the spread of diseases and pathogens.

Aleksei Panov, Dmitri Panov, Peter Panov

We discuss here the geometry of frieze patterns, and add a few words about Greek vases, molecular symmetry, and 2D crystallography. The work is written primarily for school students.

Tu Quynh Loan Ngo, Yu-Ren Wang, Dai-Lun Chiang

In the construction industry, non–destructive testing (NDT) methods are often used in the field to inspect the compressive strength of concrete. NDT methods do not cause damage to the existing structure and are relatively economical. Two popular NDT methods are the rebound hammer (RH) test and the ultrasonic pulse velocity (UPV) test. One major drawback of the RH test and UPV test is that the concrete compressive strength estimations are not very accurate when comparing them to the results obtained from the destructive tests. To improve concrete strength estimation, the researchers applied artificial intelligence prediction models to explore the relationships between the input values (results from the two NDT tests) and the output values (concrete strength). In-situ NDT data from a total of 98 samples were collected in collaboration with a material testing laboratory and the Professional Civil Engineer Association. In-situ NDT data were used to develop and validate the prediction models (both traditional statistical models and AI models). The analysis results showed that AI prediction models provide more accurate estimations when compared to statistical regression models. The research results show significant improvement when AI techniques (ANNs, SVM and ANFIS) are applied to estimate concrete compressive strength in RH and UPV tests.

Veronika Blank, Roman Skidanov, Leonid Doskolovich et al.

We propose a novel type of spectral diffractive lenses that operate in the ±1-st diffraction orders. Such spectral lenses generate a sharp image of the wavelengths of interest in the +1-st and –1-st diffraction orders. The spectral lenses are convenient to use for obtaining remotely sensed vegetation index images instead of full-fledged hyperspectral images. We discuss the design and fabrication of spectral diffractive lenses for measuring vegetation indices, which include a Modified Red Edge Simple Ratio Index and a Water Band Index. We report synthesizing diffractive lenses with a microrelief thickness of 4 µm using the direct laser writing in a photoresist. The use of the fabricated spectral lenses in a prototype scheme of an imaging sensor for index measurements is discussed. Distributions of the aforesaid spectral indices are obtained by the linear scanning of vegetation specimens. Using a linear scanning of vegetation samples, distributions of the above-said water band index were experimentally measured.

Cyril Cayron

The current theory of twin crystallography is based on the concept of invariant plane. The present paper extends the theory to encompass the cases of heterotwins in which the composition plane is only quasi-invariant.

Sergei V. Kalinin, Mark P. Oxley, Mani Valleti et al.

The advent of high-resolution electron and scanning probe microscopy imaging has opened the floodgates for acquiring atomically resolved images of bulk materials, 2D materials, and surfaces. This plethora of data contains an immense volume of information on materials structures, structural distortions, and physical functionalities. Harnessing this knowledge regarding local physical phenomena necessitates the development of the mathematical frameworks for extraction of relevant information. However, the analysis of atomically resolved images is often based on the adaptation of concepts from macroscopic physics, notably translational and point group symmetries and symmetry lowering phenomena. Here, we explore the bottom-up definition of structural units and symmetry in atomically resolved data using a Bayesian framework. We demonstrate the need for a Bayesian definition of symmetry using a simple toy model and demonstrate how this definition can be extended to the experimental data using deep learning networks in a Bayesian setting, namely rotationally invariant variational autoencoders.

Yun-Lian Shi, Tao Huang, Xu-Liang Nie et al.

[C15H6Cl6O4], monoclinic, Cc (no. 9), a = 12.0980(11) Å, b = 10.0628(9) Å, c = 15.1518(14) Å, β = 104.231(1)°, V = 1788.0(3) Å3, Z = 4, Rgt(F) = 0.0214, wRref(F2) = 0.0568, T = 296(2) K.

Albert Mingazov, Leonid Doskolovich, Dmitry Bykov et al.

The article deals with a problem of calculating two reflecting surfaces that form a given irradiance distribution with a flat wavefront, provided that a point source of light is used. A notion of a weak solution for the said problem is formulated and the equivalence of this problem and the Monge–Kantorovich mass transfer is proven.

Markus Lazar

Dislocations play a key role in the understanding of many phenomena in solid state physics, materials science, crystallography and engineering. Dislocations are line defects producing distortions and self-stresses in an otherwise perfect crystal lattice. In particular, dislocations are the primary carrier of crystal plasticity and in dislocation based fracture mechanics.

David Bramer, Guo-Wei Wei

Debye-Waller factor, a measure of X-ray attenuation, can be experimentally observed in protein X-ray crystallography. Previous theoretical models have made strong inroads in the analysis of B-factors by linearly fitting protein B-factors from experimental data. However, the blind prediction of B-factors for unknown proteins is an unsolved problem. This work integrates machine learning and advanced graph theory, namely, multiscale weighted colored graphs (MWCGs), to blindly predict B-factors of unknown proteins. MWCGs are local features that measure the intrinsic flexibility due to a protein structure. Global features that connect the B-factors of different proteins, e.g., the resolution of X-ray crystallography, are introduced to enable the cross-protein B-factor predictions. Several machine learning approaches, including ensemble methods and deep learning, are considered in the present work. The proposed method is validated with hundreds of thousands of experimental B-factors. Extensive numerical results indicate that the blind B-factor predictions obtained from the present method are more accurate than the least squares fittings using traditional methods.

Hong Yu, Ronghua Lu, Shensheng Han et al.

Knowledge gained through X-ray crystallography fostered structural determination of materials and greatly facilitated the development of modern science and technology in the past century. Atomic details of sample structures is achievable by X-ray crystallography, however, it is only applied to crystalline structures. Imaging techniques based on X-ray coherent diffraction or zone plates are capable of resolving the internal structure of non-crystalline materials at nanoscales, but it is still a challenge to achieve atomic resolution. Here we demonstrate a novel lensless Fourier-transform ghost imaging method with pseudo-thermal hard X-rays by measuring the second-order intensity correlation function of the light. We show that high resolution Fourier-transform diffraction pattern of a complex amplitude sample can be achieved at Fresnel region and the amplitude and phase distributions of a sample in spatial domain can be retrieved successfully. The method of lensless X-ray Fourier-transform ghost imaging extends X-ray crystallography to non-crystalline samples, and its spatial resolution is limited only by the wavelength of the X-ray, thus atomic resolution should be routinely obtainable. Since highly coherent X-ray source is not required, comparing to conventional X-ray coherent diffraction imaging, the method can be implemented with laboratory X-ray sources, and it also provides a potential solution for lensless diffraction imaging with fermions, such as neutron and electron where the intensive coherent source usually is not available.

Ciprian S. Borcea, Ileana Streinu

In mathematical crystallography and computational materials science, it is important to infer flexibility properties of framework materials from their geometric representation. We study combinatorial, geometric and kinematic properties for frameworks modeled on sodalite.

Veit Elser

Bit retrieval is the problem of reconstructing a binary sequence from its periodic autocorrelation, with applications in cryptography and x-ray crystallography. After defining the problem, with and without noise, we describe and compare various algorithms for solving it. A geometrical constraint satisfaction algorithm, relaxed-reflect-reflect, is currently the best algorithm for noisy bit retrieval.