1,354 research outputs found
Clinical and preclinical pharmacokinetic and dynamic studies with cisplatin, topotecan and docetaxel
Stabilization of highly polar BiFeO-like structure: a new interface design route for enhanced ferroelectricity in artificial perovskite superlattices
In ABO3 perovskites, oxygen octahedron rotations are common structural
distortions that can promote large ferroelectricity in BiFeO3 with an R3c
structure [1], but suppress ferroelectricity in CaTiO3 with a Pbnm symmetry
[2]. For many CaTiO3-like perovskites, the BiFeO3 structure is a metastable
phase. Here, we report the stabilization of the highly-polar BiFeO3-like phase
of CaTiO3 in a BaTiO3/CaTiO3 superlattice grown on a SrTiO3 substrate. The
stabilization is realized by a reconstruction of oxygen octahedron rotations at
the interface from the pattern of nonpolar bulk CaTiO3 to a different pattern
that is characteristic of a BiFeO3 phase. The reconstruction is interpreted
through a combination of amplitude-contrast sub 0.1nm high-resolution
transmission electron microscopy and first-principles theories of the
structure, energetics, and polarization of the superlattice and its
constituents. We further predict a number of new artificial ferroelectric
materials demonstrating that nonpolar perovskites can be turned into
ferroelectrics via this interface mechanism. Therefore, a large number of
perovskites with the CaTiO3 structure type, which include many magnetic
representatives, are now good candidates as novel highly-polar multiferroic
materials [3].Comment: Phys. Rev. X, in productio
Comparison of ethanol plasma-protein precipitation with plasma ultrafiltration and trichloroacetic acid protein precipitation for the measurement of unbound platinum concentrations
Analysing the Surface Morphology of Colorectal Polyps:Differential Geometry and Pit Pattern Prediction
We present an initial study analysing the surface morphology of colorectal polyps from optical projection tomography. The differential geometry of polyp surfaces, seg-mented using a level sets method, is explored in terms of local, multi-scale shape index and curvedness descriptors. A surface region of interest can be represented using his-tograms of these descriptors. An experiment is described investigating the ability to predict pit pattern categories from these histograms using support vector machines.
An automated pattern recognition system for classifying indirect immunofluorescence images for HEp-2 cells and specimens
AbstractImmunofluorescence antinuclear antibody tests are important for diagnosis and management of autoimmune conditions; a key step that would benefit from reliable automation is the recognition of subcellular patterns suggestive of different diseases. We present a system to recognize such patterns, at cellular and specimen levels, in images of HEp-2 cells. Ensembles of SVMs were trained to classify cells into six classes based on sparse encoding of texture features with cell pyramids, capturing spatial, multi-scale structure. A similar approach was used to classify specimens into seven classes. Software implementations were submitted to an international contest hosted by ICPR 2014 (Performance Evaluation of Indirect Immunofluorescence Image Analysis Systems). Mean class accuracies obtained on heldout test data sets were 87.1% and 88.5% for cell and specimen classification respectively. These were the highest achieved in the competition, suggesting that our methods are state-of-the-art. We provide detailed descriptions and extensive experiments with various features and encoding methods
Cost, range anxiety and future electricity supply:A review of how today's technology trends may influence the future uptake of BEVs
Local structure prediction for gland segmentation
We present a method to segment individual glands from colon histopathology images. Segmentation based on sliding window classification does not usually make explicit use of information about the spatial configurations of class labels. To improve on this we propose to segment glands using a structure learning approach in which the local label configurations (structures) are considered when training a support vector machine classifier. The proposed method not only distinguishes foreground from background, it also distinguishes between different local structures in pixel labelling, e.g. locations between adjacent glands and locations far from glands. It directly predicts these label configurations at test time. Experiments demonstrate that it produces better segmentations than when the local label structure is not used to train the classifier
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