Solving surface structures from normal incidence X-ray standing wave data

A program is provided to determine structural parameters of atoms in or adsorbed on surfaces by refinement of atomistic models towards experimentally determined data generated by the normal incidence X-ray standing wave (NIXSW) technique. The method employs a combination of Differential Evolution Genetic Algorithms and Steepest Descent Line Minimisations to provide a fast, reliable and user friendly tool for experimentalists to interpret complex multidimensional NIXSW data sets

The ABCs of ATVs: Factors implicated in child deaths and injuries involving all terrain vehicles on New Zealand farms

The agricultural sector features prominently in the rates of ATV injuries and fatalities amongst children in New Zealand. This research project assesses the nature and scope of ATV accidents to children on New Zealand farms and provides recommendations that attempt to meet the needs of all relevant stakeholders. In particular, we believe that the most effective means of reducing the rates of ATV injuries and fatalities amongst children involves a strategy which recognises the unique circumstances which give rise to practical impediments to safer farm workplace practices. We identified three distinct groups of children in the literature, each facing a different major risk category. Very young children were most at risk as passengers. As age increased the highest risks applied to bystanders, while older children and teenagers were more likely to be injured as drivers. The high risks to younger children as passengers and bystanders were indicative of underlying problems associated with childcare options – or, more particularly, the lack of childcare options. Accidents involving older children were associated more closely with practices around child supervision and involved aspects of farming culture, rather than practical barriers to safer practices

Structure of adsorbed organometallic rhodium: model single atom catalysts

We have determined the structure of a complex rhodium carbonyl chloride [Rh(CO)(2)Cl] molecule adsorbed on the TiO2 (110) surface by the normal incidence x-ray standing wave technique. The data show that the technique is applicable to reducible oxide systems and that the dominant adsorbed species is undissociated with Rh binding atop bridging oxygen and to the Cl found close to the fivefold coordinated Ti ions in the surface. A minority geminal dicarboryl species, where Rh-Cl bond scission has occurred, is found bridging the bridging oxygen ions forming a high-symmetry site

Fast global interactive volume segmentation with regional supervoxel descriptors

In this paper we propose a novel approach towards fast multi-class volume segmentation that exploits supervoxels in order to reduce complexity, time and memory requirements. Current methods for biomedical image segmentation typically require either complex mathematical models with slow convergence, or expensive-to-calculate image features, which makes them non-feasible for large volumes with many objects (tens to hundreds) of different classes, as is typical in modern medical and biological datasets. Recently, graphical models such as Markov Random Fields (MRF) or Conditional Random Fields (CRF) are having a huge impact in different computer vision areas (e.g. image parsing, object detection, object recognition) as they provide global regularization for multiclass problems over an energy minimization framework. These models have yet to find impact in biomedical imaging due to complexities in training and slow inference in 3D images due to the very large number of voxels. Here, we define an interactive segmentation approach over a supervoxel space by first defining novel, robust and fast regional descriptors for supervoxels. Then, a hierarchical segmentation approach is adopted by training Contextual Extremely Random Forests in a user-defined label hierarchy where the classification output of the previous layer is used as additional features to train a new classifier to refine more detailed label information. This hierarchical model yields final class likelihoods for supervoxels which are finally refined by a MRF model for 3D segmentation. Results demonstrate the effectiveness on a challenging cryo-soft X-ray tomography dataset by segmenting cell areas with only a few user scribbles as the input for our algorithm. Further results demonstrate the effectiveness of our method to fully extract different organelles from the cell volume with another few seconds of user interaction. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

SMURFS: superpixels from multi-scale refinement of super-regions

Recent applications in computer vision have come to rely on superpixel segmentation as a pre-processing step for higher level vision tasks, such as object recognition, scene labelling or image segmentation. Here, we present a new algorithm, Superpixels from MUlti-scale ReFinement of Super-regions (SMURFS), which not only obtains state-of-the-art superpixels, but can also be applied hierarchically to form what we call n-th order super-regions. In essence, starting from a uniformly distributed set of super-regions, the algorithm iteratively alternates graph-based split and merge optimization schemes which yield superpixels that better represent the image. The split step is performed over the pixel grid to separate large super-regions into different smaller superpixels. The merging process, conversely, is performed over the superpixel graph to create 2nd-order super-regions (super-segments). Iterative refinement over two scale of regions allows the algorithm to achieve better over-segmentation results than current state-of-the-art methods, as experimental results show on the public Berkeley Segmentation Dataset (BSD500)

A comparison of clustering models for inference of T cell receptor antigen specificity

The vast potential sequence diversity of TCRs and their ligands has presented an historic barrier to computational prediction of TCR epitope specificity, a holy grail of quantitative immunology. One common approach is to cluster sequences together, on the assumption that similar receptors bind similar epitopes. Here, we provide an independent evaluation of widely used clustering algorithms for TCR specificity inference, observing some variability in predictive performance between models, and marked differences in scalability. Despite these differences, we find that different algorithms produce clusters with high degrees of similarity for receptors recognising the same epitope. Our analysis highlights an unmet need for improvement of complex models over a simple Hamming distance comparator, and strengthens the case for use of clustering models in TCR specificity inference