Improved Orientation Sampling for Indexing Diffraction Patterns of Polycrystalline Materials

Orientation mapping is a widely used technique for revealing the microstructure of a polycrystalline sample. The crystalline orientation at each point in the sample is determined by analysis of the diffraction pattern, a process known as pattern indexing. A recent development in pattern indexing is the use of a brute-force approach, whereby diffraction patterns are simulated for a large number of crystalline orientations, and compared against the experimentally observed diffraction pattern in order to determine the most likely orientation. Whilst this method can robust identify orientations in the presence of noise, it has very high computational requirements. In this article, the computational burden is reduced by developing a method for nearly-optimal sampling of orientations. By using the quaternion representation of orientations, it is shown that the optimal sampling problem is equivalent to that of optimally distributing points on a four-dimensional sphere. In doing so, the number of orientation samples needed to achieve a indexing desired accuracy is significantly reduced. Orientation sets at a range of sizes are generated in this way for all Laue groups, and are made available online for easy use.Comment: 11 pages, 7 figure

The biochemical properties of manganese in plants

Manganese (Mn) is an essential micronutrient with many functional roles in plant metabolism. Manganese acts as an activator and co-factor of hundreds of metalloenzymes in plants. Because of its ability to readily change oxidation state in biological systems, Mn plays and important role in a broad range of enzyme-catalyzed reactions, including redox reactions, phosphorylation, decarboxylation, and hydrolysis. Manganese(II) is the prevalent oxidation state of Mn in plants and exhibits fast ligand exchange kinetics, which means that Mn can often be substituted by other metal ions, such as Mg(II), which has similar ion characteristics and requirements to the ligand environment of the metal binding sites. Knowledge of the molecular mechanisms catalyzed by Mn and regulation of Mn insertion into the active site of Mn-dependent enzymes, in the presence of other metals, is gradually evolving. This review presents an overview of the chemistry and biochemistry of Mn in plants, including an updated list of known Mn-dependent enzymes, together with enzymes where Mn has been shown to exchange with other metal ions. Furthermore, the current knowledge of the structure and functional role of the three most well characterized Mn-containing metalloenzymes in plants; the oxygen evolving complex of photosystem II, Mn superoxide dismutase, and oxalate oxidase is summarized

Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data

Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), and the results point towards the conclusion that FC exhibits dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a non-parametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted in Bayesian statistical modeling we use the predictive likelihood to investigate if the model can discriminate between a motor task and rest both within and across subjects. We further investigate what drives dynamic states using the model on the entire data collated across subjects and task/rest. We find that the number of states extracted are driven by subject variability and preprocessing differences while the individual states are almost purely defined by either task or rest. This questions how we in general interpret dynamic FC and points to the need for more research on what drives dynamic FC.Comment: 8 pages, 1 figure. Presented at the Machine Learning and Interpretation in Neuroimaging Workshop (MLINI-2015), 2015 (arXiv:1605.04435

Robust structural identification via polyhedral template matching

Successful scientific applications of large-scale molecular dynamics often rely on automated methods for identifying the local crystalline structure of condensed phases. Many existing methods for structural identification, such as Common Neighbour Analysis, rely on interatomic distances (or thresholds thereof) to classify atomic structure. As a consequence they are sensitive to strain and thermal displacements, and preprocessing such as quenching or temporal averaging of the atomic positions is necessary to provide reliable identifications. We propose a new method, Polyhedral Template Matching (PTM), which classifies structures according to the topology of the local atomic environment, without any ambiguity in the classification, and with greater reliability than e.g. Common Neighbour Analysis in the presence of thermal fluctuations. We demonstrate that the method can reliably be used to identify structures even in simulations near the melting point, and that it can identify the most common ordered alloy structures as well. In addition, the method makes it easy to identify the local lattice orientation in polycrystalline samples, and to calculate the local strain tensor. An implementation is made available under a Free and Open Source Software license.Comment: 20 pages, 14 figures. Revised version: algorithm improved slightl