Small bowel MRI in adult patients: not just Crohn’s disease—a tutorial

To provide an overview of less well-known small bowel and mesenteric diseases found at small bowel magnetic resonance (MR) enterography/enteroclysis and to review the imaging findings. MR enterography and enteroclysis are important techniques for evaluation of small bowel diseases. In most centres these techniques are primarily used in Crohn's disease, and most radiologists are familiar with these MRI findings. However, the knowledge of findings in other diseases is often sparse, including diseases that may cause similar clinical symptoms to those of Crohn's disease. We present a spectrum of less common and less well-known bowel and mesenteric diseases (e.g. internal hernia, intussusception, neuroendocrine tumour) from our small bowel MR database of over 2,000 cases. These diseases can be found in patients referred for bowel obstruction, abdominal pain or rectal blood loss. Further, in patients with (or suspected to have) Crohn's disease, some of these diseases (e.g. neuroendocrine tumour, familial Mediterranean fever) may mislead radiologists to erroneously diagnose active Crohn's disease. Radiologists should be familiar with diseases affecting the small bowel other than Crohn's disease, including diseases that may mimic Crohn's diseas

Instance reduction for one-class classification

Instance reduction techniques are data preprocessing methods originally developed to enhance the nearest neighbor rule for standard classification. They reduce the training data by selecting or generating representative examples of a given problem. These algorithms have been designed and widely analyzed in multi-class problems providing very competitive results. However, this issue was rarely addressed in the context of one-class classification. In this specific domain a reduction of the training set may not only decrease the classification time and classifier’s complexity, but also allows us to handle internal noisy data and simplify the data description boundary. We propose two methods for achieving this goal. The first one is a flexible framework that adjusts any instance reduction method to one-class scenario by introduction of meaningful artificial outliers. The second one is a novel modification of evolutionary instance reduction technique that is based on differential evolution and uses consistency measure for model evaluation in filter or wrapper modes. It is a powerful native one-class solution that does not require an access to counterexamples. Both of the proposed algorithms can be applied to any type of one-class classifier. On the basis of extensive computational experiments, we show that the proposed methods are highly efficient techniques to reduce the complexity and improve the classification performance in one-class scenarios

Highly efficient solid state catalysis by reconstructed (001) Ceria surface

Substrate engineering is a key factor in the synthesis of new complex materials. The substrate surface has to be conditioned in order to minimize the energy threshold for the formation of the desired phase or to enhance the catalytic activity of the substrate. The mechanism of the substrate activity, especially of technologically relevant oxide surfaces, is poorly understood. Here we design and synthesize several distinct and stable CeO(2) (001) surface reconstructions which are used to grow epitaxial films of the high-temperature superconductor YBa(2)Cu(3)O(7). The film grown on the substrate having the longest, fourfold period, reconstruction exhibits a twofold increase in performance over surfaces with shorter period reconstructions. This is explained by the crossover between the nucleation site dimensions and the period of the surface reconstruction. This result opens a new avenue for catalysis mediated solid state synthesis