A discrete event simulation model to evaluate the use of community services in the treatment of patients with Parkinson's disease in the United Kingdom.

BACKGROUND: The number of people affected by Parkinson's disease (PD) is increasing in the United Kingdom driven by population ageing. The treatment of the disease is complex, resource intensive and currently there is no known cure to PD. The National Health Service (NHS), the public organisation delivering healthcare in the UK, is under financial pressures. There is a need to find innovative ways to improve the operational and financial performance of treating PD patients. The use of community services is a new and promising way of providing treatment and care to PD patients at reduced cost than hospital care. The aim of this study is to evaluate the potential operational and financial benefits, which could be achieved through increased integration of community services in the delivery of treatment and care to PD patients in the UK without compromising care quality. METHODS: A Discrete Event Simulation model was developed to represent the PD care structure including patients' pathways, treatment modes, and the mix of resources required to treat PD patients. The model was parametrised with data from a large NHS Trust in the UK and validated using information from the same trust. Four possible scenarios involving increased use of community services were simulated on the model. RESULTS: Shifting more patients with PD from hospital treatment to community services will reduce the number of visits of PD patients to hospitals by about 25% and the number of PD doctors and nurses required to treat these patients by around 32%. Hospital based treatment costs overall should decrease by 26% leading to overall savings of 10% in the total cost of treating PD patients. CONCLUSIONS: The simulation model was useful in predicting the effects of increased use of community services on the performance of PD care delivery. Treatment policies need to reflect upon and formalise the use of community services and integrate these better in PD care. The advantages of community services need to be effectively shared with PD patients and carers to help inform management choices and care plans

Local object patterns for representation and classification of colon tissue images

Cataloged from PDF version of article.This paper presents a new approach for the effective representation and classification of images of histopathological colon tissues stained with hematoxylin and eosin. In this approach, we propose to decompose a tissue image into its histological components and introduce a set of new texture descriptors, which we call local object patterns, on these components to model their composition within a tissue. We define these descriptors using the idea of local binary patterns, which quantify a pixel by constructing a binary string based on relative intensities of its neighbors. However, as opposed to pixel-level local binary patterns, we define our local object pattern descriptors at the component level to quantify a component. To this end, we specify neighborhoods with different locality ranges and encode spatial arrangements of the components within the specified local neighborhoods by generating strings. We then extract our texture descriptors from these strings to characterize histological components and construct the bag-of-words representation of an image from the characterized components. Working on microscopic images of colon tissues, our experiments reveal that the use of these component-level texture descriptors results in higher classification accuracies than the previous textural approaches. © 2013 IEEE

Graph run-length matrices for histopathological image segmentation

Cataloged from PDF version of article.The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. In this paper, we introduce an effective and robust algorithm for the segmentation of histopathological tissue images. This algorithm incorporates the background knowledge of the tissue organization into segmentation. For this purpose, it quantifies spatial relations of cytological tissue components by constructing a graph and uses this graph to define new texture features for image segmentation. This new texture definition makes use of the idea of gray-level run-length matrices. However, it considers the runs of cytological components on a graph to form a matrix, instead of considering the runs of pixel intensities. Working with colon tissue images, our experiments demonstrate that the texture features extracted from "graph run-length matrices" lead to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with four other segmentation algorithms, the results show that the proposed algorithm is more effective in histopathological image segmentatio

Efficient successor retrieval operations for aggregate query processing on clustered road networks

Cataloged from PDF version of article.Get-Successors (GS) which retrieves all successors of a junction is a kernel operation used to facilitate aggregate computations in road network queries. Efficient implementation of the GS operation is crucial since the disk access cost of this operation constitutes a considerable portion of the total query processing cost. Firstly, we propose a new successor retrieval operation Get-Unevaluated-Successors (GUS), which retrieves only the unevaluated successors of a given junction. The GUS operation is an efficient implementation of the GS operation, where the candidate successors to be retrieved are pruned according to the properties and state of the algorithm. Secondly, we propose a hypergraph-based model for clustering successively retrieved junctions by the GUS operations to the same pages. The proposed model utilizes query logs to correctly capture the disk access cost of GUS operations. The proposed GUS operation and associated clustering model are evaluated for two different instances of GUS operations which typically arise in Dijkstra's single source shortest path algorithm and incremental network expansion framework. Our simulation results show that the proposed successor retrieval operation together with the proposed clustering hypergraph model is quite effective in reducing the number of disk accesses in query processing. (C) 2010 Published by Elsevier Inc

Mathematical modeling of the malignancy of cancer using graph evolution

Cataloged from PDF version of article.We report a novel computational method based on graph evolution process to model the malignancy of brain cancer called glioma. In this work, we analyze the phases that a graph passes through during its evolution and demonstrate strong relation between the malignancy of cancer and the phase of its graph. From the photomicrographs of tissues, which are diagnosed as normal, low-grade cancerous and high-grade cancerous, we construct cell-graphs based on the locations of cells; we probabilistically generate an edge between every pair of cells depending on the Euclidean distance between them. For a cell-graph, we extract connectivity information including the properties of its connected components in order to analyze the phase of the cell-graph. Working with brain tissue samples surgically removed from 12 patients, we demonstrate that cell-graphs generated for different tissue types evolve differently and that they exhibit different phase properties, which distinguish a tissue type from another. (C) 2007 Elsevier Inc. All rights reserved

A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images

Cataloged from PDF version of article.Computer-based imaging systems are becoming important tools for quantitative assessment of peripheral blood and bone marrow samples to help experts diagnose blood disorders such as acute leukemia. These systems generally initiate a segmentation stage where white blood cells are separated from the background and other nonsalient objects. As the success of such imaging systems mainly depends on the accuracy of this stage, studies attach great importance for developing accurate segmentation algorithms. Although previous studies give promising results for segmentation of sparsely distributed normal white blood cells, only a few of them focus on segmenting touching and overlapping cell clusters, which is usually the case when leukemic cells are present. In this article, we present a new algorithm for segmentation of both normal and leukemic cells in peripheral blood and bone marrow images. In this algorithm, we propose to model color and shape characteristics of white blood cells by defining two transformations and introduce an efficient use of these transformations in a marker-controlled watershed algorithm. Particularly, these domain specific characteristics are used to identify markers and define the marking function of the watershed algorithm as well as to eliminate false white blood cells in a postprocessing step. Working on 650 white blood cells in peripheral blood and bone marrow images, our experiments reveal that the proposed algorithm improves the segmentation performance compared with its counterparts, leading to high accuracies for both sparsely distributed normal white blood cells and dense leukemic cell clusters. (C) 2014 International Society for Advancement of Cytometr

Relaxation of the Dynamical Gluino Phase and Unambiguous Electric Dipole Moments

We propose a new axionic solution of the strong CP problem with a Peccei-Quinn mechanism using the gluino rather than quarks. The spontaneous breaking of this new global U(1) at 10^{11} GeV also generates the supersymmetry breaking scale of 1 TeV (solving the so-called \mu problem at the same time) and results in the MSSM (Minimal Supersymmetric Standard Model) with R parity conservation. In this framework, electric dipole moments become calculable without ambiguity.Comment: Typos corrected and a footnote added, 10 p