Whether you're looking for adventure, work opportunities, new scenery or better weather conditions, California cities represent a special, sunny place in the popular imagination. But before packing your bags and heading to the Golden State, you'll want to know which is the most dangerous city in California.

The English language is challenging due to complicated grammar, inconsistent sentence structure and colloquial idioms that it doesn't share with related languages. However, English is a target language that sees significantly more resources and opportunities for immersion than many other languages.

You're driving out of a parking lot when you suddenly feel that you're having problems with the power steering. Learn about how to diagnose power steering problems in this article.

How does a hybrid car improve your gas mileage? And more importantly, does it pollute less just because it gets better gas mileage? Learn how hybrids work, plus get tips on how to drive a hybrid car for maximum efficiency.

[Culture] :
BTS member J-Hope completed his 18-month military service on Thursday. Upon being discharged earlier in the morning, J-Hope appeared at the gate of the 36th Infantry Division's Recruit Training Center in Wonju, Gangwon Province, and thanked the group's fandom, ARMY, saying he was able to complete his ...

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[Culture] :
Nobel Prize-winning author Han Kang hopes to write three more books over the next six years. Han made the remarks Thursday as she accepted the Pony Chung Innovation Prize at an award ceremony organized by the Pony Chung Foundation. The 53-year-old author, who turns 54 next month, said ages 50 to 60 are ...

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On hot summer days, is it best to turn off the AC when leaving home, turn it up or leave it as is? The answer may surprise you.

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You may have been awakened in the night by loud blaring noise and an alert text on your phone. Who sends these alerts, and why are you getting them?

s Virgo and Capricorn compatibility strong? Discover how these earth signs connect in love, friendship, and marriage with shared values, loyalty, and mutual support.

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Government and policy  British Geological Survey

Government and agencies  British Geological Survey

International geoscience  British Geological Survey

BGS scientist announced as Scottish Government policy fellow  British Geological Survey

BGS to help deliver International Centre of Excellence on Sustainable Resource Management  British Geological Survey

International geoscience research and development  British Geological Survey

Small-angle X-ray scattering (SAXS) is widely used to analyze the shape and size of nanoparticles in solution. A multitude of models, describing the SAXS intensity resulting from nanoparticles of various shapes, have been developed by the scientific community and are used for data analysis. Choosing the optimal model is a crucial step in data analysis, which can be difficult and time-consuming, especially for non-expert users. An algorithm is proposed, based on machine learning, representation learning and SAXS-specific preprocessing methods, which instantly selects the nanoparticle model best suited to describe SAXS data. The different algorithms compared are trained and evaluated on a simulated database. This database includes 75 000 scattering spectra from nine nanoparticle models, and realistically simulates two distinct device configurations. It will be made freely available to serve as a basis of comparison for future work. Deploying a universal solution for automatic nanoparticle model selection is a challenge made more difficult by the diversity of SAXS instruments and their flexible settings. The poor transferability of classification rules learned on one device configuration to another is highlighted. It is shown that training on several device configurations enables the algorithm to be generalized, without degrading performance compared with configuration-specific training. Finally, the classification algorithm is evaluated on a real data set obtained by performing SAXS experiments on nanoparticles for each of the instrumental configurations, which have been characterized by transmission electron microscopy. This data set, although very limited, allows estimation of the transferability of the classification rules learned on simulated data to real data.

Human tRNA (uracil-5-)-methyltransferase 2 homolog A (TRMT2A) is the dedicated enzyme for the methylation of uridine 54 in transfer RNA (tRNA). Human TRMT2A has also been described as a modifier of polyglutamine (polyQ)-derived neuronal toxicity. The corresponding human polyQ pathologies include Huntington's disease and constitute a family of devastating neuro­degenerative diseases. A polyQ tract in the corresponding disease-linked protein causes neuronal death and symptoms such as impaired motor function, as well as cognitive impairment. In polyQ disease models, silencing of TRMT2A reduced polyQ-associated cell death and polyQ protein aggregation, suggesting this protein as a valid drug target against this class of disorders. In this paper, the 1.6 Å resolution crystal structure of the RNA-recognition motif (RRM) from Drosophila melanogaster, which is a homolog of human TRMT2A, is described and analysed.

Plasmodium vivax is a major cause of malaria, which poses an increased health burden on approximately one third of the world's population due to climate change. Primaquine, the preferred treatment for P. vivax malaria, is contraindicated in individuals with glucose-6-phosphate dehydrogenase (G6PD) deficiency, a common genetic cause of hemolytic anemia, that affects ∼2.5% of the world's population and ∼8% of the population in areas of the world where P. vivax malaria is endemic. The Seattle Structural Genomics Center for Infectious Disease (SSGCID) conducted a structure–function analysis of P. vivax N-myristoyltransferase (PvNMT) as part of efforts to develop alternative malaria drugs. PvNMT catalyzes the attachment of myristate to the N-terminal glycine of many proteins, and this critical post-translational modification is required for the survival of P. vivax. The first step is the formation of a PvNMT–myristoyl–CoA binary complex that can bind to peptides. Understanding how inhibitors prevent protein binding will facilitate the development of PvNMT as a viable drug target. NMTs are secreted in all life stages of malarial parasites, making them attractive targets, unlike current antimalarials that are only effective during the plasmodial erythrocytic stages. The 2.3 Å resolution crystal structure of the ternary complex of PvNMT with myristoyl-CoA and a novel inhibitor is reported. One asymmetric unit contains two monomers. The structure reveals notable differences between the PvNMT and human enzymes and similarities to other plasmodial NMTs that can be exploited to develop new antimalarials.

Proliferating cell nuclear antigen (PCNA) plays a critical role in DNA replication by enhancing the activity of various proteins involved in replication. In this study, the crystal structure of ApePCNA1, one of three PCNAs from the thermophilic archaeon Aeropyrum pernix, was elucidated. ApePCNA1 was cloned and expressed in Escherichia coli and the protein was purified and crystallized. The resulting crystal structure determined at 2.00 Å resolution revealed that ApePCNA1 does not form a trimeric ring, unlike PCNAs from other domains of life. It has unique structural features, including a long interdomain-connecting loop and a PIP-box-like sequence at the N-terminus, indicating potential interactions with other proteins. These findings provide insights into the functional mechanisms of PCNAs in archaea and their evolutionary conservation across different domains of life. A modified medium and protocol were used to express recombinant protein containing the lac operon. The expression of the target protein increased and the total incubation time decreased when using this system compared with those of previous expression protocols.

New software capabilities in RMCProfile allow researchers to study the structure of materials by combining machine learning interatomic potentials and reverse Monte Carlo.

The symmetry groups of the weave patterns of the baskets, trays and mats of the Batak, an indigenous community in the Philippines, are discussed in this paper. The two-way twofold weaving technique is used by the Batak, and this study points to a total of 15 layer groups found in the Batak weaves out of the 80 layer groups known in crystallography.

RAPID (RAtio method Pattern InDexing) is an ImageJ macro script developed for the quick determination of sample orientation and indexing of calibrated and uncalibrated zone axis aligned electron diffraction patterns from materials with a cubic crystal structure. In addition to SAED and NBED patterns, the program is also capable of handling zone axis TEM Kikuchi patterns and FFTs derived from HR(S)TEM images. The software enables users to rapidly determine whether materials are cubic, pseudo-cubic, or non-cubic, and to distinguish between P, I, and F Bravais lattices. It can also provide lattice parameters for material verification and aid in determining the camera constant of the instrument, thus making the program a convenient tool for on-site crystallographic analysis in the TEM laboratory.

Structure refinement with reverse Monte Carlo (RMC) is a powerful tool for interpreting experimental diffraction data. To ensure that the under-constrained RMC algorithm yields reasonable results, the hybrid RMC approach applies interatomic potentials to obtain solutions that are both physically sensible and in agreement with experiment. To expand the range of materials that can be studied with hybrid RMC, we have implemented a new interatomic potential constraint in RMCProfile that grants flexibility to apply potentials supported by the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) molecular dynamics code. This includes machine learning interatomic potentials, which provide a pathway to applying hybrid RMC to materials without currently available interatomic potentials. To this end, we present a methodology to use RMC to train machine learning interatomic potentials for hybrid RMC applications.

This study introduces an alternative method to the Takagi–Taupin equations for investigating the dark-field X-ray microscopy (DFXM) of deformed crystals. In scenarios where dynamical diffraction cannot be disregarded, it is essential to assess the potential inaccuracies of data interpretation based on the kinematic diffraction theory. Unlike the Takagi–Taupin equations, this new method utilizes an exact dispersion relation, and a previously developed finite difference scheme with minor modifications is used for the numerical implementation. The numerical implementation has been validated by calculating the diffraction of a diamond crystal with three components, wherein dynamical diffraction is applicable to the first component and kinematic diffraction pertains to the remaining two. The numerical convergence is tested using diffraction intensities. In addition, the DFXM image of a diamond crystal containing a stacking fault is calculated using the new method and compared with the experimental result. The new method is also applied to calculate the DFXM image of a twisted diamond crystal, which clearly shows a result different from those obtained using the Takagi–Taupin equations.

Small-angle X-ray scattering (SAXS) is a widely used method for nanoparticle characterization. A common approach to analysing nanoparticles in solution by SAXS involves fitting the curve using a parametric model that relates real-space parameters, such as nanoparticle size and electron density, to intensity values in reciprocal space. Selecting the optimal model is a crucial step in terms of analysis quality and can be time-consuming and complex. Several studies have proposed effective methods, based on machine learning, to automate the model selection step. Deploying these methods in software intended for both researchers and industry raises several issues. The diversity of SAXS instrumentation requires assessment of the robustness of these methods on data from various machine configurations, involving significant variations in the q-space ranges and highly variable signal-to-noise ratios (SNR) from one data set to another. In the case of laboratory instrumentation, data acquisition can be time-consuming and there is no universal criterion for defining an optimal acquisition time. This paper presents an approach that revisits the nanoparticle model selection method proposed by Monge et al. [Acta Cryst. (2024), A80, 202–212], evaluating and enhancing its robustness on data from device configurations not seen during training, by expanding the data set used for training. The influence of SNR on predictor robustness is then assessed, improved, and used to propose a stopping criterion for optimizing the trade-off between exposure time and data quality.

Bone material contains a hierarchical network of micro- and nano-cavities and channels, known as the lacuna-canalicular network (LCN), that is thought to play an important role in mechanobiology and turnover. The LCN comprises micrometer-sized lacunae, voids that house osteocytes, and submicrometer-sized canaliculi that connect bone cells. Characterization of this network in three dimensions is crucial for many bone studies. To quantify X-ray Zernike phase-contrast nanotomography data, deep learning is used to isolate and assess porosity in artifact-laden tomographies of zebrafish bones. A technical solution is proposed to overcome the halo and shade-off domains in order to reliably obtain the distribution and morphology of the LCN in the tomographic data. Convolutional neural network (CNN) models are utilized with increasing numbers of images, repeatedly validated by `error loss' and `accuracy' metrics. U-Net and Sensor3D CNN models were trained on data obtained from two different synchrotron Zernike phase-contrast transmission X-ray microscopes, the ANATOMIX beamline at SOLEIL (Paris, France) and the P05 beamline at PETRA III (Hamburg, Germany). The Sensor3D CNN model with a smaller batch size of 32 and a training data size of 70 images showed the best performance (accuracy 0.983 and error loss 0.032). The analysis procedures, validated by comparison with human-identified ground-truth images, correctly identified the voids within the bone matrix. This proposed approach may have further application to classify structures in volumetric images that contain non-linear artifacts that degrade image quality and hinder feature identification.

During beam time at a research facility, alignment and optimization of instrumentation, such as spectrometers, is a time-intensive task and often needs to be performed multiple times throughout the operation of an experiment. Despite the motorization of individual components, automated alignment solutions are not always available. In this study, a novel approach that combines optimisers with neural network surrogate models to significantly reduce the alignment overhead for a mobile soft X-ray spectrometer is proposed. Neural networks were trained exclusively using simulated ray-tracing data, and the disparity between experiment and simulation was obtained through parameter optimization. Real-time validation of this process was performed using experimental data collected at the beamline. The results demonstrate the ability to reduce alignment time from one hour to approximately five minutes. This method can also be generalized beyond spectrometers, for example, towards the alignment of optical elements at beamlines, making it applicable to a broad spectrum of research facilities.

Transition-metal nitro­gen-doped carbons (TM-N-C) are emerging as a highly promising catalyst class for several important electrocatalytic processes, including the electrocatalytic CO2 reduction reaction (CO2RR). The unique local environment around the singly dispersed metal site in TM-N-C catalysts is likely to be responsible for their catalytic properties, which differ significantly from those of bulk or nanostructured catalysts. However, the identification of the actual working structure of the main active units in TM-N-C remains a challenging task due to the fluctional, dynamic nature of these catalysts, and scarcity of experimental techniques that could probe the structure of these materials under realistic working conditions. This issue is addressed in this work and the local atomistic and electronic structure of the metal site in a Co–N–C catalyst for CO2RR is investigated by employing time-resolved operando X-ray absorption spectroscopy (XAS) combined with advanced data analysis techniques. This multi-step approach, based on principal component analysis, spectral decomposition and supervised machine learning methods, allows the contributions of several co-existing species in the working Co–N–C catalysts to be decoupled, and their XAS spectra deciphered, paving the way for understanding the CO2RR mechanisms in the Co–N–C catalysts, and further optimization of this class of electrocatalytic systems.

The phase problem in the context of focusing synchrotron beams with X-ray lenses is addressed. The feasibility of retrieving the surface error of a lens system by using only the intensity of the propagated beam at several distances is demonstrated. A neural network, trained with a few thousand simulations using random errors, can predict accurately the lens error profile that accounts for all aberrations. It demonstrates the feasibility of routinely measuring the aberrations induced by an X-ray lens, or another optical system, using only a few intensity images.

MuscleX is an integrated, open-source computer software suite for data reduction of X-ray fiber diffraction patterns from striated muscle and other fibrous systems. It is written in Python and runs on Linux, Microsoft Windows or macOS. Most modules can be run either from a graphical user interface or in a `headless mode' from the command line, suitable for incorporation into beamline control systems. Here, we provide an overview of the general structure of the MuscleX software package and describe the specific features of the individual modules as well as examples of applications.

The structure of cis- or trans-bridged [GeF5]− anionic chains have been investigated [Mallouk et al. (1984). Inorg. Chem. 23, 3160–3166] showing the first crystal structures of μ-F-bridged penta­fluoro­germanates. Herein, we report the second crystal structure of trans-penta­fluoro­germanate anions present in the crystal structure of sodium trans-penta­fluoro­germanate(IV) bis­(hy­dro­gen fluoride), Na[GeF5]·2HF. The crystal structure [ortho­rhom­bic Pca21, a = 12.3786 (3), b = 7.2189 (2), c = 11.4969 (3) Å and Z = 8] is built up from infinite chains of trans-linked [GeF6]2− octa­hedra, extending along the b axis and spanning a network of penta­gonal bipyramidal distorted Na-centred polyhedra. These [NaF7] polyhedra are linked in a trans-edge fashion via hy­dro­gen fluoride mol­ecules, in analogy to already known sodium hy­dro­gen fluorides and potassium hy­dro­gen fluorides.

The reaction of titanium(IV) chloride with sodium hexa­fluoro­iso­pro­pox­ide, carried out in hexa­fluoro­iso­propanol, produces titanium(IV) hexa­fluoro­iso­pro­pox­ide, which is a liquid at room temperature. Recrystallization from coordinating solvents, such as aceto­nitrile or tetra­hydro­furan, results in the formation of bis-solvate com­plexes. These com­pounds are of inter­est as possible Ziegler–Natta polymerization catalysts. The aceto­nitrile com­plex had been structurally characterized previously and adopts a distorted octahedral structure in which the nitrile ligands adopt a cis configuration, with nitro­gen lone pairs coordinated to the metal. The low-melting tetra­hydro­furan com­plex has not provided crystals suitable for single-crystal X-ray analysis. However, the structure of chlorido­tris­(hexa­fluoro­isopropoxido-κO)bis­(tetra­hydro­furan-κO)titanium(IV), [Ti(C3HF6O)3Cl(C4H8O)2], has been obtained and adopts a distorted octa­hedral coordination geometry, with a facial arrangement of the alkoxide ligands and adjacent tetra­hydro­furan ligands, coordinated by way of metal–oxygen polar coordinate inter­actions.

Mevalonate kinase is central to the isoprenoid biosynthesis pathway. Here, high-resolution X-ray crystal structures of two mevalonate kinases are presented: a eukaryotic protein from Ramazzottius varieornatus and an archaeal protein from Methanococcoides burtonii. Both enzymes possess the highly conserved motifs of the GHMP enzyme superfamily, with notable differences between the two enzymes in the N-terminal part of the structures. Biochemical characterization of the two enzymes revealed major differences in their sensitivity to geranyl pyrophosphate and farnesyl pyrophosphate, and in their thermal stabilities. This work adds to the understanding of the structural basis of enzyme inhibition and thermostability in mevalonate kinases.

When solving a structure of a protein from single-wavelength anomalous diffraction X-ray data, the initial phases obtained by phasing from an anomalously scattering substructure usually need to be improved by an iterated electron-density modification. In this manuscript, the use of convolutional neural networks (CNNs) for segmentation of the initial experimental phasing electron-density maps is proposed. The results reported demonstrate that a CNN with U-net architecture, trained on several thousands of electron-density maps generated mainly using X-ray data from the Protein Data Bank in a supervised learning, can improve current density-modification methods.

During the automatic processing of crystallographic diffraction experiments, beamstop shadows are often unaccounted for or only partially masked. As a result of this, outlier reflection intensities are integrated, which is a known issue. Traditional statistical diagnostics have only limited effectiveness in identifying these outliers, here termed Not-Excluded-unMasked-Outliers (NEMOs). The diagnostic tool AUSPEX allows visual inspection of NEMOs, where they form a typical pattern: clusters at the low-resolution end of the AUSPEX plots of intensities or amplitudes versus resolution. To automate NEMO detection, a new algorithm was developed by combining data statistics with a density-based clustering method. This approach demonstrates a promising performance in detecting NEMOs in merged data sets without disrupting existing data-reduction pipelines. Re-refinement results indicate that excluding the identified NEMOs can effectively enhance the quality of subsequent structure-determination steps. This method offers a prospective automated means to assess the efficacy of a beamstop mask, as well as highlighting the potential of modern pattern-recognition techniques for automating outlier exclusion during data processing, facilitating future adaptation to evolving experimental strategies.

A group of three deep-learning tools, referred to collectively as CHiMP (Crystal Hits in My Plate), were created for analysis of micrographs of protein crystallization experiments at the Diamond Light Source (DLS) synchrotron, UK. The first tool, a classification network, assigns images into categories relating to experimental outcomes. The other two tools are networks that perform both object detection and instance segmentation, resulting in masks of individual crystals in the first case and masks of crystallization droplets in addition to crystals in the second case, allowing the positions and sizes of these entities to be recorded. The creation of these tools used transfer learning, where weights from a pre-trained deep-learning network were used as a starting point and repurposed by further training on a relatively small set of data. Two of the tools are now integrated at the VMXi macromolecular crystallography beamline at DLS, where they have the potential to absolve the need for any user input, both for monitoring crystallization experiments and for triggering in situ data collections. The third is being integrated into the XChem fragment-based drug-discovery screening platform, also at DLS, to allow the automatic targeting of acoustic compound dispensing into crystallization droplets.

Speckle-tracking X-ray imaging is an attractive candidate for dynamic X-ray imaging owing to its flexible setup and simultaneous yields of phase, transmission and scattering images. However, traditional speckle-tracking imaging methods suffer from phase distortion at locations with abrupt changes in density, which is always the case for real samples, limiting the applications of the speckle-tracking X-ray imaging method. In this paper, we report a deep-learning based method which can achieve dynamic X-ray speckle-tracking imaging with high-accuracy phase retrieval. The calibration results of a phantom show that the profile of the retrieved phase is highly consistent with the theoretical one. Experiments of polyurethane foaming demonstrated that the proposed method revealed the evolution of the complicated microstructure of the bubbles accurately. The proposed method is a promising solution for dynamic X-ray imaging with high-accuracy phase retrieval, and has extensive applications in metrology and quantitative analysis of dynamics in material science, physics, chemistry and biomedicine.

This paper uses deep learning to present a proof-of-concept for data-driven chemistry in single-molecule magnets (SMMs). Previous discussions within SMM research have proposed links between molecular structures (crystal structures) and single-molecule magnetic properties; however, these have only interpreted the results. Therefore, this study introduces a data-driven approach to predict the properties of SMM structures using deep learning. The deep-learning model learns the structural features of the SMM molecules by extracting the single-molecule magnetic properties from the 3D coordinates presented in this paper. The model accurately determined whether a molecule was a single-molecule magnet, with an accuracy rate of approximately 70% in predicting the SMM properties. The deep-learning model found SMMs from 20 000 metal complexes extracted from the Cambridge Structural Database. Using deep-learning models for predicting SMM properties and guiding the design of novel molecules is promising.

The discovery of lytic polysaccharide monooxygenases (LPMOs), a family of copper-dependent enzymes that play a major role in polysaccharide degradation, has revealed the importance of oxidoreductases in the biological utilization of biomass. In fungi, a range of redox proteins have been implicated as working in harness with LPMOs to bring about polysaccharide oxidation. In bacteria, less is known about the interplay between redox proteins and LPMOs, or how the interaction between the two contributes to polysaccharide degradation. We therefore set out to characterize two previously unstudied proteins from the shipworm symbiont Teredinibacter turnerae that were initially identified by the presence of carbohydrate binding domains appended to uncharacterized domains with probable redox functions. Here, X-ray crystal structures of several domains from these proteins are presented together with initial efforts to characterize their functions. The analysis suggests that the target proteins are unlikely to function as LPMO electron donors, raising new questions as to the potential redox functions that these large extracellular multi-haem-containing c-type cytochromes may perform in these bacteria.

Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase identification on simulated XRD patterns achieves 84.2 and 51.5% accuracies, respectively. In experimental settings, CPICANN achieves an 80% identification accuracy, surpassing JADE software (61%). Integration of CPICANN into XRD refinement software will significantly advance the cutting-edge technology in XRD materials characterization.

Mineral identification and quantification are key to the understanding and, hence, the capacity to predict material properties. The method of choice for mineral quantification is powder X-ray diffraction (XRD), generally using a Rietveld refinement approach. However, a successful Rietveld refinement requires preliminary identification of the phases that make up the sample. This is generally carried out manually, and this task becomes extremely long or virtually impossible in the case of very large datasets such as those from synchrotron X-ray diffraction computed tomography. To circumvent this issue, this article proposes a novel neural network (NN) method for automating phase identification and quantification. An XRD pattern calculation code was used to generate large datasets of synthetic data that are used to train the NN. This approach offers significant advantages, including the ability to construct databases with a substantial number of XRD patterns and the introduction of extensive variability into these patterns. To enhance the performance of the NN, a specifically designed loss function for proportion inference was employed during the training process, offering improved efficiency and stability compared with traditional functions. The NN, trained exclusively with synthetic data, proved its ability to identify and quantify mineral phases on synthetic and real XRD patterns. Trained NN errors were equal to 0.5% for phase quantification on the synthetic test set, and 6% on the experimental data, in a system containing four phases of contrasting crystal structures (calcite, gibbsite, dolomite and hematite). The proposed method is freely available on GitHub and allows for major advances since it can be applied to any dataset, regardless of the mineral phases present.

The accuracy of the information in the Protein Data Bank (PDB) is of great importance for the myriad downstream applications that make use of protein structural information. Despite best efforts, the occasional introduction of errors is inevitable, especially where the experimental data are of limited resolution. A novel protein structure validation approach based on spotting inconsistencies between the residue contacts and distances observed in a structural model and those computationally predicted by methods such as AlphaFold2 has previously been established. It is particularly well suited to the detection of register errors. Importantly, this new approach is orthogonal to traditional methods based on stereochemistry or map–model agreement, and is resolution independent. Here, thousands of likely register errors are identified by scanning 3–5 Å resolution structures in the PDB. Unlike most methods, the application of this approach yields suggested corrections to the register of affected regions, which it is shown, even by limited implementation, lead to improved refinement statistics in the vast majority of cases. A few limitations and confounding factors such as fold-switching proteins are characterized, but this approach is expected to have broad application in spotting potential issues in current accessions and, through its implementation and distribution in CCP4, helping to ensure the accuracy of future depositions.

The title compound, [Ni(NCS)2(C6H7N)2]n, was prepared by the reaction of Ni(NCS)2 with 4-methyl­pyridine in water. Its asymmetric unit consists of two crystallographically independent NiII cations, of which one is located on a twofold rotational axis whereas the second occupies a center of inversion, two independent thio­cyanate anions and two independent 4-methyl­pyridine co­ligands in general positions. Each NiII cation is octa­hedrally coordinated by two 4-methyl­pyridine coligands as well as two N- and two S-bonded thio­cyanate anions. One of the cations shows an all-trans, the other a cis–cis–trans configuration. The metal centers are linked by pairs of μ-1,3-bridging thio­cyanate anions into [101] chains. X-ray powder diffraction shows that a pure crystalline phase has been obtained and thermogravimetry coupled to differential thermoanalysis reveals that the title compound loses half of the 4-methyl­pyridine coligands and transforms into Ni(NCS)2(C6H7N). Nearly pure samples of this compound can be obtained by thermal annealing and a Rietveld refinement demonstrated that it is isotypic to its recently reported Cd analog [Neumann et al., (2020). CrystEngComm. 22, 184–194] In its crystal structure, the metal cations are linked by one μ-1,3(N,S)- and one μ-1,3,3(N,S,S)-bridging thio­cyanate anion into single chains that condense via the μ-1,3,3(N,S,S)-bridging anionic ligands into double chains.

The cyclic peptide cyclo(Val-Leu-Leu-d-Phe-Pro)2 (peptide 1) was specifically designed for structural chemistry investigations, drawing inspiration from Gramicidin S (GS). Previous studies have shown that Pro residues within 1 adopt a down-puckering conformation of the pyrrolidine ring. By incorporating fluoride-Pro with 4-trans/cis-isomers into 1, an up-puckering conformation was successfully induced. In the current investigation, introducing hy­droxy­prolines with 4-trans/cis-isomer configurations (tHyp/cHyp) into 1 gave cyclo(Val-Leu-Leu-d-Phe-tHyp)2 methanol disolvate monohydrate, C62H94N10O12·2CH4O·H2O (4), and cyclo(Val-Leu-Leu-d-Phe-cHyp)2 monohydrate, C62H94N10O12·H2O (5), respectively. However, the puckering of 4 and 5 remained in the down conformation, regardless of the geometric position of the hydroxyl group. Although the backbone structure of 4 with trans-substitution was asymmetric, the asymmetric backbone of 5 with cis-substitution was unexpected. It is speculated that the anti­cipated influence of stress from the geometric positioning, which was expected to affect the puckering, may have been mitigated by inter­actions between the hydroxyl groups of hy­droxy­proline, the solvent mol­ecules, and peptides.

The International Tables Symmetry Database (https://symmdb.iucr.org/), which is part of International Tables for Crystallography, is a collection of individual databases of crystallographic space-group and point-group information with associated programs. The programs let the user access and in some cases interactively visualize the data, and some also allow new data to be calculated `on the fly'. Together these databases and programs expand upon and complement the symmetry information provided in International Tables for Crystallography Volume A, Space-Group Symmetry, and Volume A1, Symmetry Relations between Space Groups. The Symmetry Database allows users to learn about and explore the space and point groups, and facilitates the study of group–subgroup relations between space groups, with applications in determining crystal-structure relationships, in studying phase transitions and in domain-structure analysis. The use of the International Tables Symmetry Database in all these areas is demonstrated using several examples.