820 research outputs found
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
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