Modeling and simulation analysis of propyl-thiouracil (PTU), an anti-thyroid drug on thyroid peroxidase (TPO), thyroid stimulating hormone receptor (TSHR), and sodium iodide (NIS) symporter based on systems biology approach Abstract The aim of metabolic modeling was to understand the cause effect interaction and reliance linked with the complex interactions of biological networks and molecular systems. Drugs that therapeutically modulate the biological processes of disease are often developed with limited knowledge of the underlying complexity of their specific targets. The robustness for systemic modulating behavior of thyroid hormone secretion during the course of different time unit simulation is explained in this study. In this work, a computational model has been developed which mimics the in vivo simulation. The model was constructed with the help of cell designer 4.1 used for analyzing the effect of perturbed amount of drugs at 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 amounts as a unit, targeting Thyroid Peroxidase (TPO), Thyroid Stimulating Hormone Receptor (TSHR), and Sodium Iodide Symporter (NIS). The rate kinetic equations were defined with each reaction to simulate the molecular species dynamic behavior. The modulating behavior of thyroid hormone secretion was analyzed by the process of activation and inactivation states of TPO, TSHR, and NIS at various amounts of drugs.Obtained results explain suitably the entire observable fact of the drug effects and are capable to proceed in response to the perturbations of the natural cell. TSHR was found as the most potent molecular therapeutic target in this study. Human body shows differential behaviors with homeostasis perturbation for over and under secretion of hormones (Chu et al. 2009; Diamanti-Kandarakis et al. 2009 ). This characterizes often the main hurdle to find out a competent pharmacological treatment against autoimmune thyroid disorder, which results in immune system attacks by body's own thyroid gland. Autoimmune thyroid disorder is an organspecific target (Swain et al. 2005) . Genetic, environmental exposures, and various endogenous factors are responsible for initiating the autoimmune thyroid disorder (Davies et al. 2012; Brent 2010; Weetman 2003) . It is reported that thyroid hormones (THs), triiodothyronine (T 3 ) and thyroxin (T 4 ), show the strong association between hyperthyroidism and high energy expenditure (EE) in human (Klieverik et al. 2009 ). The hypophysiotropic thyrotropin-releasing hormone (TRH), as a metabolic sensor, is regulated at multiple levels to control thyroid function and metabolism (Eduardo 2010; Hollenberg 2008; Wilding 2002) . The structure of T 3 , T 4 , and PTU is shown in Fig. 1 .. Thyroid-stimulating hormone (TSH) and receptor TSHR (Farid and Szkudlinski 2004) are main proteins in the regulation of thyroid function. Human TSHR (hTSHR) is measured to be the foremost autoantigen in autoimmune hyperthyroidism in case of Graves's disease (Vlase et al. 1998) . Inhibitors that stop the actions of TSHR-stimulating immunoglobulins could be useful for the study and treatment of Graves' disease for which limited therapy exists at present (Szkudlinski et al. 2002) . It was found that small molecule inhibitors may serve as a lead compound with therapeutic potential that target TSHR in patients suffering from hyperthyroidism (Neumann et al. 2009 (Neumann et al. , 2010 Chen et al. 2009 ). Such chemical species could be able to modulate spontaneous fluctuations between active and inactive states.. TPO is an important enzyme for thyroid hormone biosynthesis and major source for an autoantigen in thyroid disorder (Ruf and Carayon 2006; McLachlan and Rapoport 2007) . It is usually connected to Hashimoto's thyroiditis (Chardès et al. 2002) . The presence of high levels of antibodies to TPO is useful in diagnosis of the disease (McLachlan and Rapoport 2000; Chardès et al. 2002) . TPO cooperates in iodination of tyrosyl residues in thyroglobulin and other proteins (Ruf and Carayon 2006; Kessler et al. 2008) . TPO is excited by TSH and inhibited by propylthiouracil (PTU) and methimazole (MMI) (Roy and Mugesh 2008) .. NIS is a transmembrane glycoprotein that participates in the regulation Na ? /I -gradient and transports two sodium ions (Na ? ) for each iodide ion (I -) into the cell. NIS mediated uptake of iodide ion into follicular cells of the thyroid gland is initial stage for thyroid hormone biosynthesis (Dohán et al. 2003; Riesco-Eizaguirre and Santisteban 2006) . Goitre, enlargement of the thyroid, is due to lack of iodide transport inside follicular cells. Mutations in the NIS DNA lead to hypothyroidism and thyroid dyshormonogenesis (Ohye and Sugawara 2010) . It was established that perchlorate, pertechnetate and thiocyanate, all anion competitively inhibits the iodide uptake (RiescoEizaguirre and Santisteban 2006) .. A model regarding host and pathogen interaction has been developed for simulation analysis to predict the effect of drugs which are resistant to pathogen (Autiero et al. 2009 ). The integrated pathways help to minimize the effects of external perturbation to whole systems due to its robust properties (Lehar et al. 2008) . Every functional species in the pathways is considered as node and linked with other important nodes at defined rate kinetics. Human immune system is very robust to any pathogen attack till certain extent, but sometimes body's own cells do not differentiate it's own and foreign cells that lead to autoimmune disorder and weaken the robustness of defense system Kim et al. 2009 ).. The aim of metabolic modelling of thyroid disorder is to come out with a hypothesis based on computational simulation analysis of human disease physiology pathways at various time extents (Butcher et al. 2004; Gupta et al. 2012) . The integrated pathways are useful to understand the evolution of disorder, off target and mode of action of drugs as well as in development of new therapeutic drugs (Kell 2006; Singh et al. 2013) . It is significant to identify the key properties and parameters which are used to predict the model. Compatibility of amount and type of data used in the model is obligatory to meet the model parameters (Ideker et al. 2001) . It is very well established that the active molecules of a living system such as DNA, RNAs, enzymes, receptor, hormones, metabolites, ions, etc., setup is essential for an integrated network to modulate the behavior of metabolic pathways (Autiero et al. 2009 ). Several In silico comprehensive maps (Oda et al. 2005; Kaizu et al. 2010; Caron et al. 2010; Oda and Kitano 2006; Wu et al. 2010; Calzone et al. 2008 ) have been developed for disease physiology, dynamic analysis, control mechanism, regulatory element detection for regulation/deregulation, and identification of biomarker for therapy of particular diseases.. Thus, literature supports that three targets (TPO, TSHR, NIS) are quite significant for study due to their medical importance. The molecular interactions and dynamic behavior concerned with the immune system have not been covered so far. In this paper, an effort has been made to describe the integrated pathways of autoimmune thyroid disorder targeting TSHR, TPO, and NIS and effect of drug PTU to analyse the dynamics behavior of functional elements. Modeling of thyroid hormone synthesis pathway can help to incorporate experimental information into a coherent representation and to investigate, test, validate, or misrepresent hypotheses about the underlying biological mechanisms (Klipp and Liebermeister 2006) . Analyzing the noise effect of experimental data is important for interaction and regulation mechanism of various species involved in simulation effect as well as various network patterns are beneficial for knowledge discovery purposes (Xiang et al. 2012; Bortolussi and Policriti 2012) . TSHR was found as the most important therapeutic target as compared to TPO and NIS due to its occurrence on cell surface. Any biochemically active molecules do not easily cross the cell membrane; hence, targeting TSHR could be more encouraging than other targets.. The integrated immune system network is intricate, composed of myriad inter and intra contacts that evade absolute perceptive. It provides prolific platform for the extrication of these intertwined vagueness with assistance insight gained from synergistic effect of experimental studies, hypothesis, theory, and computational modeling. This approach can be useful for experimental design, interpretation, and comparative study of normal and diseased pathway. Noticeably, formulating, designing, modeling, and simulating an experiment from in silico point of view is easier than wet lab. By extending the idea derived here, it is obvious that concurrent multiple drugs' effect with perturbed amount could be useful to observe the robustness of the designed model. A number of undiscovered effects of drugs with their perturbed amount to its corresponding targets can be predicted. Nevertheless, it seems to be essential to examine more conditions that imitate realistic stress situation in an attempt to find out which method need to be developed to cope with their usual environment. In terms of biomedical significance, it is assumed that the availability of a constructed metabolic model can further speed up the research of non-communicable and communicable diseases. Drug discovery processes depend on decision making and such type of model can be useful in potential drug target identification, validation, and interpretation of result at cellular and system level.

O O B O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Efficient Broadcasting Using Network Coding and Directional Antennas in MANETs Abstract-In this paper, we consider the issue of efficient broadcasting in mobile ad hoc networks (MANETs) using network coding and directional antennas. Network coding-based broadcasting focuses on reducing the number of transmissions each forwarding node performs in the multiple source/multiple message broadcast application, where each forwarding node combines some of the received messages for transmission. With the help of network coding, the total number of transmissions can be reduced compared to broadcasting using the same forwarding nodes without coding. We exploit the usage of directional antennas to network coding-based broadcasting to further reduce energy consumption. A node equipped with directional antennas can divide the omnidirectional transmission range into several sectors and turn some of them on for transmission. In the proposed scheme using a directional antenna, forwarding nodes selected locally only need to transmit broadcast messages, original or coded, to restricted sectors. We also study two extensions. The first extension applies network coding to both dynamic and static forwarding node selection approaches. In the second extension, we design two approaches for the single source/single message issue in the network coding-based broadcast application. Performance analysis via simulations on the proposed algorithms using a custom simulator and ns2 is presented. B ROADCASTING is the most frequently used operation in mobile ad hoc networks (MANETs) for the dissemination of data and control messages in many applications. Usually, a network backbone is constructed for efficient broadcasting to avoid the broadcast storm problem caused by simple blind flooding, where only selected nodes that form the virtual backbone, called forwarding nodes, forward data to the entire network.. In MANETs, the forwarding node set for broadcasting is usually selected in a localized manner, where each node determines its own status of forwarding or nonforwarding based on local information [31] , or the status of a node is designated by its neighbors [16] . A smaller-sized forwarding node set is considered to be more efficient due to the reduced number of transmissions in the network, which helps to alleviate interference and also conserve energy. The connected dominating set (CDS), as a virtual backbone, has been widely studied [21] , where each node is either a forwarding node or a neighbor to a forwarding node in the set. The set is also connected. Finding a minimum CDS is NP-complete.. Li et al. [13] exploited network coding in the broadcast application. They applied coding methods to deterministic forwarding node selection approaches to gain a reduction in the number of transmissions, focusing on reducing the number of transmissions each forwarding node performs.. Network coding [12] is defined as allowing intermediate nodes to process the incoming information flows. When a forwarding node, chosen by a certain approach, needs to forward several messages to all of its neighbors, while some neighbors already have some of the messages, this node can combine some of the messages to reduce the number of forwardings, and each neighbor can still get every message via decoding.. For instance, node c gets two messages from nodes a and b, respectively. In order to let a and b have each other's message, c needs to forward both the messages as a traditional forwarding node. With network coding, c only needs to forward one coded message containing both original messages through the XOR operation. Nodes a and b can decode the message with the help of their own messages through the XOR operation. Note that the network coding works only when there are multiple messages broadcast at the same time in the network.. Yang et al. [33] focused on reducing the total number of forwarding directions/sectors of forwarding nodes. Using directional antennas, the omnidirectional transmission range of each node can be divided into several sectors and the transmission can be performed only in selected sectors. Therefore, by reducing the total number of transmission sectors of the forwarding nodes in the network, the interference can be alleviated, as well as the energy consumption.. In this paper, we try to combine the advantages of both network coding and directional antennas to achieve efficiency in broadcasting. We analyze the performance of these two methods and design an algorithm-Efficient Broadcast using Network Coding and Directional Antennas (EBCD), where each node decides its forwarding status using only local information and limited piggybacked broadcast state information. Our proposed design does not simply mix the two existing methods. We take advantage of the effects of interaction in both methods in an effort to achieve even better performance. Additionally, we modify the existing directional antenna method to a dynamic mode. As shown in Fig. 1a , there are four messages, A; B; C, and D, generated from nodes a; b; c, and d, respectively. We assume that node e is selected for forwarding using a forwarding node selection method. Therefore, e needs to forward all four messages, costing four transmissions totally. In network coding-based broadcasts based on two-hop neighborhood information, e can construct a neighbor reception table, as in Fig. 1b , to record the broadcast state information of the received messages. For instance, when a sends out message A, both e and b receive it. Therefore, b is a "covered" node of message A and there is a "1" in the grid at line b, column A. Based on the table, e then codes these four messages into two combined messages to forward, P 1 ð¼ A È CÞ and P 2 ð¼ B È DÞ (È is the XOR operation), using some network coding algorithms. Obviously, every other node can decode these two combined messages together with the messages it already has in order to gain all four of the original messages. For instance, node b has message A; B, and C. When b receives P 2 , it can use P 2 È B to extract message D. a can use P 1 È A and P 2 È B to obtain C and D.. With the help of directional antennas, the omnidirectional transmission range of e can be divided into K sectors (K is 4 in this example), as the dashed lines show in Fig. 1a . Then, e can restrict the transmissions of the two combined messages in only some of the sectors, as shown in table (Fig. 1c) . For instance, P 1 only needs to be transmitted in sectors I; II, and IV . If we let the consumption of the transmission of one message in one sector be the unit energy consumption, traditional broadcasting, where e transmits all four messages omnidirectionally, costs 16. Broadcasting with network coding costs 8. The broadcast with network coding and directional antennas costs 6. Other than the forwarding nodes, the source nodes can also restrict the transmissions to selected sectors to further reduce the total energy consumption, as long as the message can reach a forwarding node.. Although the forwarding node/edge selection and the further network coding procedures are independent, we show that different underlying forwarding node selection approaches significantly affect the efficiency of network coding. In EBCD, we design the dynamic version of the underlying forwarding node/edge selection approach. We then use a static version without piggybacked information for it to analyze the performance and trade-offs. We find out that the energy conservation of the dynamic and static versions is comparable, although the dynamic version is slightly better. However, since the static version has less overhead, it is more practical. Also, the network coding-based broadcast approach [13] works only when there are multiple sources with multiple messages in the network. We propose two approaches as another extension to EBCD to deal with the single source with single message application: the pipelinebased (PB) approach and spread-out (SO) approach. We also discuss the detailed implementation techniques in the proposed EBCD algorithm, such as the timing issue and the neighborhood information discovery issue.. The contributions of this paper can be summarized as follows:. 1. We present the advantages of the combination of the network coding and directional antenna approaches for efficient broadcasting and develop the EBCD algorithm. 2. We extend the EBCD algorithm to a static forwarding node/edge selection version to study the performance variation. 3. We propose two approaches for the application of single source with a single broadcast message. 4. We discuss some implementation techniques in EBCD, including local information collection, timer setting, and mobility handling. 5. We conduct performance analyses through simulations on the proposed algorithms in terms of energy consumption and delivery ratio. The remainder of the paper is organized as follows: Section 2 introduces some related works and preliminaries in the field. Section 3 presents the proposed efficient broadcast using the Network Coding and Directional Antennas approach (EBCD). Section 4 presents two extensions to the proposed (EBCD) approach. One is the static version of EBCD and the other is to solve the single message broadcast issue. Section 5 presents some implementation details of the proposed algorithms. A performance study through simulation is conducted in Section 6. The paper concludes in Section 7. Network coding has been exploited for efficient broadcasting to reduce the number of transmissions in the multiple source broadcast application. We combine the network coding-based broadcast approach with broadcasting using directional antennas for a more efficient broadcast strategy, and develop efficient broadcasting using network coding and directional antenna algorithm (EBCD). We extend existing broadcasting using the directional antenna approach to a dynamic mode. Although the coding-based approach is independent of the underlying forwarding node selection procedure, we show that different forwarding node selections affect the overall performance significantly. We also discuss the single source/ single message application and design two approaches for it. Performance analysis is conducted through simulations. The proposed EBCD approach has better performance than traditional CDS-based broadcasting and the existing network coding-based broadcasting in terms of energy consumption. Also, the static version of EBCD has comparative performance in energy conservation with smaller overhead. In a dynamic environment with signal interference and node mobility, EBCD has better performance against collision, but worse performance when it comes to movement. In the future, we will improve the robustness of mobility for the proposed approaches. . For more information on this or any other computing topic, please visit our Digital Library at www.computer.org/publications/dlib.

O B O B I O B I O O O O O O O O O O O B O O O O O O O B I O B I O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B O O O O O O O O O O O O B I O O O B O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O B O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O B I O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O O O O O O O O O O O B O B I O O O O O O O O O B O B I O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Non-linear correlation of content and metadata information extracted from biomedical article datasets Biomedical literature databases constitute valuable repositories of up to date scientific knowledge. The development of efficient machine learning methods in order to facilitate the organization of these databases and the extraction of novel biomedical knowledge is becoming increasingly important. Several of these methods require the representation of the documents as vectors of variables forming large multivariate datasets. Since the amount of information contained in different datasets is voluminous, an open issue is to combine information gained from various sources to a concise new dataset, which will efficiently represent the corpus of documents. This paper investigates the use of the multivariate statistical approach, called Non-Linear Canonical Correlation Analysis (NLCCA), for exploiting the correlation among the variables of different document representations and describing the documents with only one new dataset. Experiments with document datasets represented by text words, Medical Subject Headings (MeSH) and Gene Ontology (GO) terms showed the effectiveness of NLCCA. Biomedical literature is stored in large-scale organized repositories which tend to exhibit increasing demands in terms of space and access speeds since current scientific biological knowledge is emerging and evolving. The manual management of this information and the extraction of useful scientific knowledge is a tedious and laborious work that involves the identification of pertinent literature and data from a large number of articles [1] . For example, the PubMed database contains over 14 million bibliographic citations and abstract articles from more than 4800 journals [2] . In this context, clustering and classification methods are becoming increasingly necessary for efficiently organizing, exploring and retrieving the information of biomedical literature.. There are several research efforts in the machine learning field focusing on the clustering or classification of biomedical documents from PubMed and on the extraction of useful biological knowledge [7] [8] [9] [10] [11] [12] . Some of the proposed methods are maximum-entropy [8] , linear discriminant analysis [9] , Naïve Bayes [10] , support vector machines [11] , group-average agglomerative clustering [12] , etc.. The main characteristic of the methods used so far is the numeric representation of biomedical documents. By numeric representation we refer to having each document represented as a vector of numeric variables with each variable corresponding to a (informative/important) word in the document. Similar representations have appeared using vectors with variables (metadata) which correspond to specific terms from a structured controlled vocabulary usually created manually for concisely describing specific information and knowledge [3] [4] [5] .. Two popular metadata approaches are the Medical Subject Headings (MeSH) [5] and the Gene Ontology (GO) [3, 4] which are used for information retrieval from biomedical document databases:. • MeSH is mainly used by the PubMed database for semantically relating documents in order to enhance the search and improve the accuracy of the query system of PubMed 1 [6] . The MeSH terms are arranged both alphabetically and hierarchically. The hierarchy has 11 levels. The upper levels of the hierarchical structure contain the most general MeSH terms, for example ''Diseases'' appear in level one, whereas more specific terms, like ''Mitochondrial myopathies'' are found in lower levels. Each PubMed document usually has 10-12 MeSH terms, manually assigned to it by experts that read the full-length document [5] .. • GO is mainly used for describing gene products (RNA or proteins) in a species independent manner, but it has also been used for describing the information contained in biomedical documents. The GO terms are structured as nodes in a Direct Acyclic Graph (DAG). They are divided into three subgroups (sub-ontologies): biological processes, cellular components and molecular functions [3, 4] . The GO is not part of the PubMed system, but it has been used in several research work for describing biomedical documents (e.g. [1, [7] [8] [9] [10] [11] [12] ).. It should be noted that some terms are found in both MeSH and GO, for example ''DNA Methylation'' or ''Autophagy'' appear in both MeSH and GO terms listings.. A common characteristic of most of the research efforts is that the data representation of the documents is based on the Vector Space Model (VSM) [13] [14] [15] which transforms a document into a vector of numeric variables. The variables may represent words of the documents [8, 9] , MeSH terms [10] or GO terms [8, 9, 11] . Each of the aforementioned representations describes differently the scientific information contained in the documents. For example, the MeSH terms can be more general than the GO terms about the molecular concepts explained in the document, since GO terms are specifically built for describing molecular concepts. This fact raises a question of how the different representations are correlated and whether they can be combined in a smaller and more concise dataset.. Usually the number of variables is quite large and thus various efforts [9, 10, 12] deal with the dimensionality reduction of the dataset describing the documents. Certain statistical methods, like Singular Value Decomposition [12] or Principal Component Analysis [9] , exploit the correlation structure of the dataset in order to produce new variables, fewer and uncorrelated [12] . This is advantageous for other subsequent procedures, like certain classification methods, which assume uncorrelated variables. Nevertheless, the obvious fact is that the words of a document's text, the MeSH or the GO terms are semantically related. For example, the possibility of finding in the same document the words ''Methylation'' and ''Cancer'' is higher, than finding ''Methylation'' and ''Chemimechanical coupling'' together.. Since data reduction is important in order to reveal patterns, trends or groupings in biomedical documents, in [9] the authors have considered a classification (discriminant analysis) method which produces new and fewer variables, able to graphically represent the documents in a lowdimensional Euclidean space. This method models the relationships between words in the text and corresponding GO terms of biomedical articles. The results showed that the reduced number of new variables can efficiently describe the documents and compete with other classification methods, such as Support Vector Machines that use all the original variables.. The motivation of the present work is to consider all of the above document representations and propose a method which would combine them, by exploiting the information they contain. The methodology under consideration is the Non-Linear Canonical Correlation Analysis (NLCCA) [16, 17] which can combine information from more than two document representations by exploiting their correlation structure. The method's general principle is the optimal projection of the documents on a low-dimensional Euclidean space by computing a set of scores from the original variables in such a way that the new scores can adequately explain the variance of the original data, i.e. document words, MeSH and GO terms. It must be noted that NLCCA is not restricted to linear relationships and is especially useful for analyzing all kinds of data, numerical and categorical [22] . In general, the contribution of NLCCA to the text mining problems addressed so far by earlier efforts can be considered to be the abridgement of information from two or more datasets describing the same documents.. The rest of the paper is organized as follows: Section 2 describes the document representations used for the experimentation. Section 3 outlines NLCCA and various measures for evaluating the method. Section 4 presents the results of the experiments. Section 5 provides practical conclusions regarding the applicability of NLCCA and discusses future work. The results from our experiments indicate that the new variables of the NLCCA model provide useful information about the data and help us clearly to distinguish the documents based on the GO term they describe. NLCCA can also significantly reduce the number of variables contained in the three datasets, for example in our experiments the 1642 keywords and the 50 MeSH terms are reduced to only 50 new variables. Furthermore, NLCCA facilitates the visualization in two dimensions of our multidimensional datasets in order to discover patterns and specific trends in graphical plots of the data. Another interesting conclusion indicated by the multiple fit values of NLCCA is that the keywords do not describe the documents as well as the MeSH or GO terms.. It should be noted that the NLCCA can be used as the first phase of a clustering or classification procedure such as SVM, naïve Bayes, discriminant analysis, etc [15] . The new variables can be further utilized in building classification models in order to categorize data. The interesting point here is that the new variables are uncorrelated and since many classification or clustering algorithms assume that the variables of the datasets are uncorrelated we can clearly see the advantage. Note also that in the case of keywords we have original variables highly correlated to another one, since each word depends on other words of the same text. Therefore, NLCCA can remove this correlation which might be a problem for building a model.. The fact that NLCAA can combine information from more than two different datasets makes it a very good Table 13 The classifications results using the NLCCA information (correlation of keywords-MeSH and of keywords-MeSH-GO) for selecting keywords for training the models The last two columns have the classification results when all the keywords are used.. method for biology, where there is usually the need to combine information from different data, like for example microarrays, biomedical articles, sequence and structure data. For example, NLCCA could be used in whole genome analysis to describe genes with one dataset. It could utilize information from the text, the MeSH and GO terms, the phylogenetic profiles, etc. of the genes. The new dataset could then be used into a clustering method in order to extract useful biological knowledge, such as pathways, interactions, etc. Another application of NLCCA could be in the field of health care and medical informatics. An important aspect in the ''post genomic'' era is the correlation between the genotype and the phenotype [28] . NLCCA could be used for combining the clinical data of the patients with their genotype in order to group them into different categories and discover specific trends and patterns for a disease.. As part of our future work we would like to apply NLCCA to describe genes and proteins using information from different sources, like text, expression profiles, etc. in order to discover novel biological knowledge. Also, we would like to apply NLCCA in the field of text clustering and more specifically in the field of biomedical article categorization. We would like to introduce a new methodology that could combine different sources of information in order to improve the efficiency and the usefulness of existing clustering methods, like K-means clustering.

O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B I O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Real-time implementation of morphological filters with polygonal structuring elements Abstract In mathematical morphology, circular structuring elements (SE) are used whenever one needs angular isotropy. The circles-difficult to implement efficientlyare often approximated by convex, symmetric polygons that decompose under the Minkowski addition to 1-D inclined segments. In this paper, we show how to perform this decomposition efficiently, in stream with almost optimal latency to compute gray-scale erosion and dilation by flat regular polygons. We further increase its performance by introducing a spatial parallelism while maintaining sequential access to data. We implement these principles in a dedicated hardware block. Several of these blocks can be concatenated to efficiently compute sequential filters, or granulometries in one scan. With a configurable image size and programmable SE size, this architecture is usable in high-end, real-time industrial applications. We show on an example that it conforms to real-time requirements of the 100Hz 1080p FullHD TV standard, even for serial morphological filters using large hexagons or octagons. Since its first introduction in the late 1960s, mathematical morphology has become in the field of image processing a useful tool for analysis of the shape or the form of spatial structures [17, 23, 24] . Over time it has found its application as a widely used image processing technique with a large applicability [9, 18] .. Thanks to the recent technological development of sensors, the resolution of images increased to tens of megapixels. Certain morphological operations, e.g., top-hat transformation, ultimate openings, granulometry, alternating sequential filters (ASF) [25] , etc., on such large images require a large structuring element (SE), since its size should be proportional to the size of the image and its contents.. Existing hardware implementations either support rectangles using SE decomposition (fast computation, but angular anisotropic), or arbitrarily-shaped SEs at the cost of significant performance decrease. Our work supports polygonal SEs at the performance rate of the rectangular SEs.. The paper is organized as follows: Sect. 2 makes a short survey of the existing morphological algorithms and architectures. Section 3 outlines the basic aspects of morphological dilation and erosion, and show how to decompose the polygons into a set of lines. Section 4 describes the algorithm and its use to decompose polygons while preserving the sequential access to data, minimal memory consumption and latency. Section 5 gives the functional implementation of the algorithm. The principal result, a parallel version using two levels of parallelism (temporal and spatial), is presented in Sect. 6 . Finally, Sect. 7 presents the experimental results obtained on an FPGA. It is widely known that the processing data in stream allows to reduce latency, memory consumption and increases the system throughput. Until recently, computing morphological dilations or erosions in stream was only possible for small, limited neighborhoods [5, 6, 10] , or large rectangles [2] . Dilations by large polygons were computed iteratively, using homothecy. This required an external memory for intermediate data, limited the flexibility, and drastically increased system latency.. This paper opens the possibility of stream execution to morphological dilation with large polygons. Although the decomposition of polygons into the Minkowski addition of inclined lines has been known for years [1] , we bring several suggestions that-combined together-allow the execution in stream.. We show how to implement dilation by inclined linear segments with sequential access to input and output data. We show how to handle border effects and recall (since this is less known) that it requires large padding. Furthermore, we show how to partition an image to introduce efficient spatial parallelism while maintaining sequential access to data at all levels. This avoids increasing the system clock by dividing a fast data stream into several slower streams to process at a slower rate. We show how to efficiently handle the border effects on the partition. The proposed polygon decomposition uses sequential access to both input and output data. This allows for temporal parallelism, wherein concatenations like . . .ded. . . operators run simultaneously on the time-delayed data.We attain a very low (nearly optimal) latency, which has beneficial impacts on memory consumption. No external memory is used even for large SEs and large images.All these aspects together allow for a considerable data throughput for sequential morphological filters. We have implemented a programmable IP block, usable in industrial systems running under heavy timing constraints satisfying up to the 100Hz 1080p FullHD TV requirements.

O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A framework for a distributed, hybrid, multiple-ontology clinical-guideline library, and automated guideline-support tools Clinical guidelines are a major tool in improving the quality of medical care. However, most guidelines are in free text, not in a formal, executable format, and are not easily accessible to clinicians at the point of care. We introduce a Web-based, modular, distributed architecture, the Digital Electronic Guideline Library (DeGeL), which facilitates gradual conversion of clinical guidelines from text to a formal representation in chosen target guideline ontology. The architecture supports guideline classification, semantic markup, context-sensitive search, browsing, run-time application, and retrospective quality assessment. The DeGeL hybrid meta-ontology includes elements common to all guideline ontologies, such as semantic classification and domain knowledge; it also includes four content-representation formats: free text, semi-structured text, semi-formal representation, and a formal representation. These formats support increasingly sophisticated computational tasks. The DeGeL tools for support of guideline-based care operate, at some level, on all guideline ontologies. We have demonstrated the feasibility of the architecture and the tools for several guideline ontologies, including Asbru and GEM. Hybrid representations of clinical guidelines, as described in the current paper, include any combination of free-text, semi-structured text, semi-formal representation, and machine-comprehensible formats in a chosen target guideline ontology. They cater for the different capabilities of expert physicians, who need have only limited knowledge of the semantics of the chosen target ontology, and knowledge engineers, who are expected to have full semantic and syntactic knowledge of the chosen ontology, but do not necessarily have deep knowledge of the guidelineÕs semantics and goals. By incrementally converting free-text guidelines into semistructured, semi-formal, and then formal specifications, we are gradually enhancing the sophistication of the automated services that the guidelineÕs representation can support: from full-text search, through context-sensitive search and visualization (sensitive to specific knowledge roles of the target ontology), to fully automated application and quality assessment.. At the same time, the semi-structured view provides an independent value: Search precision has been shown to be significantly improved by marking-up the text of medical documents [31] , while displaying documents along a predefined meaningful ontology is highly preferred by users [32] .. Furthermore, the tools we are developing for runtime application and quality assessment can exploit the intermediate representation levels. Indeed, only a semi-structured or semi-formal representation is useful when no electronic patient record is available, and the attending physician or quality-assessment nurse is acting as the mediator to the patient record. Thus, our hybrid representation leads to a graceful degradation in the level of service experienced by the user, even when neither the guideline nor the medical record is fully machine comprehensible.. Although the advantages are considerable, one obvious limitation inherent in the hybrid-ontology model is the need to provide ontology-specific tools (both for editing and for runtime application) for the semiformal and formal representations. The reason is that, unlike the meta-ontology we suggest, whose elements are common to all guideline ontologies, the procedural semantics of each ontology differ considerably. Proposals to unify additional aspects of guideline ontologies (e.g., a virtual view of the medical record, a common objectives representation, and a generalized hierarchical plan representation) might eventually reduce the magnitude of this problem or completely solve it.. The effort invested in creating a consensus of experts before formally representing a guideline has been previously documented by researchers who had used an early version of the GLIF ontology, GLIF-2, for representation of complex clinical guidelines [29] . The research team encoded, using an extended version of GLIF-2, the secondary prevention portion of the National Cholesterol Education Program (NCEP) guideline for management of hypercholesterolemia [33] . The editing and execution framework was the PartnerÕs Computerized Algorithm Processor and Editor (PCAPE) system [34] . Although the expressivity of the representation framework was not an obstacle, the developers invested the bulk of their effort in first creating a consensus regarding the guidelineÕs semantics, and then translating it into an executable format. (It is interesting to note, though, that the major obstacle was in effectively integrating the system into the clinical workflow. The authorsÕ opinion was that without more sophisticated methods for such integration, including outpatient order entry, the benefits of complex guideline systems over simple rule-based reminders will be small.). Our future plans include modifying the current architecture so as to enable multiple (local) DeGeL sites, increasing the flexibility (e.g., easier access, improved security, and knowledge segmentation) of the DeGeL framework. A relative difficulty might arise when we implement our vision of multiple DeGeL sites. The task of indexing and searching through all local guideline libraries will become considerably more complex, and necessitate a new, distributed version of the Vaidurya search engine.. An interesting issue regarding the best authorization model for controlling the creation and editing of medical knowledge has arisen due to the distributed nature of the DeGeL library. The DeGeL permission model is based on a virtual, distributed medical-specialty authoring group notion, and on the different functionalities implied by the hybrid-representation model. The model is inspired by the legal and practical considerations involved in editing medical knowledge by multiple experts.. We are currently in the process of development of a new version of the DeGeLock permission and authorization module, to improve its flexibility and performance, by focusing on conceptual entities (e.g., ontologies, axes, and guideline-documents), and actions that are performed on every entity (e.g., update, delete, and view), neither of which necessarily correspond to any particular DeGeL module. We expect the extensions we are planning, namely focusing on entities and actions rather than on specific computational modules, to significantly enhance the DeGeLock moduleÕs flexibility and support the needs of a global knowledge-editing community.. We also intend to open the DeGeL architecture to facilitate the addition of new modules, by exposing a standard application interface to DeGeLÕs server-side components, for example to support runtime application modules for guideline languages other than Asbru. Furthermore, we are designing and implementing new client-server based modules (e.g., desktop applications), such as Uruz-3, a graphical markup editor for hybrid ontologies, whose interface we consider to be more intuitive than that of the current Web-based Uruz module. In general, we intend to convert modules that require highly sophisticated graphical interfaces into desktop applications, while maintaining the distributed nature of the architecture through a link to the DeGeL server.. The experience of using the Vaidurya context-sensitive search and retrieval engine has demonstrated that clinical users encounter several difficulties when facing its complex interface. Thus, we are currently adding to Vaidurya customizable and template-based interfaces [26] . These search templates represent typical information needs of various user types, and enable customization by the user.. Our vision for the future is a global network of hybrid digital guideline libraries, with a hierarchical (group-based) community of medical experts and knowledge engineers maintaining the knowledge base, using tools of the Uruz type and standardized vocabularies that are not site specific.. We also envision a set of tools for searching, retrieving, browsing, applying at runtime, and assessing retrospectively the quality of application, of guidelines in the library.. Ontology-independent tools, such as Vaidurya and VisiGuide, will be complemented by ontology-specific tools, such as Spock and QualiGuide. GEM ontology. Drs. Mary Goldstein, Susana Martins, Lawrence Basso, Herbert Kaizer, Aneel Advani, and Eitan Lunenfeld, were extremely helpful in assessing the various DeGeL tools.. Appendix A. DeGeLÕs knowledge-base guideline repository structure A simplified view of the main entities and relations of the guideline repository displayed in the unified modeling language (UML) notation. As explained previously, there are three types of ontologies: (1) the SourceOntology class is related to the SourceGuideline class through the documented-with association; (2) the HybridMetaOntology class consists of knowledge roles that are independent of any particular guideline-specification ontology, and is related to the HybridGuideline class through the documented-with association; and (3) the Specification Ontology class is related to the HybridGuideline class through the specified-with association. All the ontology classes inherit common attributes, such Fig. 10 . A simplified view of the main entities and relations of the guideline repository displayed in the unified modeling language (UML) notation. Fig. 11 . Part of a free-text guideline for screening, treating and monitoring patients with hypertension. This guideline is uploaded into the DeGeL library and stored as a source guideline. Fig. 12 . A semi-structured representation, showing a hybrid-Asbru abort condition marked by the domain expert. The text within this XML element was marked by the expert using the Uruz tool, from a free-text source. Fig. 13 . A semi-formal representation in the Hybrid-Asbru ontology. The Asbru abort condition marked-up by the medical expert and shown in Fig. 12 was semi-formalized (by the medical expert, possibly in collaboration with a knowledge engineer) using a graphical, Asbru-specific editing tool, which is a part of Uruz. The output of the graphical editing tool is an XML file that adheres to an XML schema that describes the Asbru semi-formal representation level. Part of that XML file (namely, only the abort condition knowledge role) is shown here. The XML file shown above specifies the temporal expression inherent in the semi-structured abort condition as an AND/OR tree. Note that: (1) Components of the AND/OR tree, such as ''systolic blood pressure >140'' are still in free text, although their logical role is now explicit; (2) Terms within the expressions, such as ''systolic blood pressure'' are specified using standardized vocabularies, in this case, LOINC 8512-6. The use of standardized terms supports future grounding to the terms of any site-specific patient database (3) Several of the terms used in the expressions, such as ''creatinine side effect,'' are in fact abstract terms defined in the Idan temporal-abstraction knowledge base (TAKB) and indexed by the context of the hypertension (HTN) domain.. as name and creation date, from the abstract base type Ontology. In addition, both HybridGuideline and SourceGuideline classes are related to the ClassificationAxis class through the classified-by association. Finally, the HybridGuideline class is related to additional HybridGuideline classes, through the association decomposed-into, thus fulfilling the requirement of most modern guideline-specification ontologies for enabling the creation of an explicit or Implicit hierarchy of guidelines and subguidelines Fig. 10 .

O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O


The MIXMAX random number generator In this note, we give a practical solution to the problem of determining the maximal period of matrix generators of pseudo-random numbers which are based on an integer-valued unimodular matrix of size NxN known as MIXMAX and arithmetic defined on a Galois field GF[p] with large prime modulus p. The existing theory of Galois finite fields is adapted to the present case, and necessary and sufficient condition to attain the maximum period is formulated. Three efficient algorithms are presented. First, allowing to compute the multiplication by the MIXMAX matrix with O(N) operations. Second, to recursively compute the characteristic polynomial with O(N 2 ) operations, and third, to apply skips of large number of steps S to the sequence in O(N 2 log(S)) operations. It is demonstrated that the dynamical properties of this generator dramatically improve with the size of the matrix N, as compared to the classes of generators based on sparse matrices and/or sparse characteristic polynomials. Finally, we present the implementation details of the generator and the results of rigorous statistical testing. In [5] it was proposed that k-mixing systems of Kolmogorov [1, 2, 3] may serve as a suitable random number generator. The particular system chosen was the one realizing linear automorphisms of the unit hypercube in R N :. A ij u j (t) mod 1 (1) where u ∈ [0, 1). For the purposes of generating pseudo-random numbers with this method, one chooses the initial vector u(0), called the "seed", with at least one non-zero component.. The entries of the matrix are integers: A ij ∈ Z and subject to the following two conditions [1, 2, 3, 5] on the defining matrix A :. • the eigenvalues λ k of A must not lie on the unit circle, |λ k | = 1 for all k = 1...N .. The first condition assures that the map defines a volume preserving automorphism. When the automorphism is viewed as defining a dynamical system with discrete time t ∈ Z, then the second condition assures that nearby trajectories diverge exponentially. There exists an everywhere dense, but discrete set of periodic trajectories all of which are unstable, and whose number as a function of the period τ asymptotically goes like e hτ /τ , where h is the Kolmogorov entropy of the system [1, 4] :. log |λ k |. The auto-correlation decay time τ 0 is related to the entropy as τ 0 ≤ 1/h. In this way, a connection is made between the chaotic dynamics of the system, its set of periodic trajectories and the entropy. In particular, the auto-correlation time is directly related to the empirical notion of randomness of the sequence. The second condition above states that none of the eigenvalues should lie precisely on the unit circle, and moreover the formula for the entropy demonstrates that it is desirable that most of the eigenvalues of the matrix should lie as far as possible away from it.. A particular matrix chosen in [6] was defined for all N ≥ 3: . The MIXMAX matrix contains integer numbers and is defined recursively, since the matrix of size N + 1 contains in it the matrix for size N . The only variable entry in the matrix is A 32 = 3 + s where s is some small "magic" integer, in many cases s = −1 or s = 0. For those N for which one or more eigenvalues lie on the unit circle for some s, one can choose another s. The eigenvalues of the MIXMAX matrix are widely dispersed for all N , see Figure 1 . Thus, the spectrum of this system is multi-scale, with trajectories exhibiting exponential instabilities on all time-scales [5] .. Empirical evidence suggests that the largest eigenvalue appears to grow at least linearly with N , but we have been unable to obtain a strict bound on it. However, Kolmogorov's entropy can be more conveniently calculated using the small eigenvalues as follows. Since the product of all of the eigenvalues is equal to the determinant,. the entropy can be calculated equally well using the eigenvalues which are less than one by absolute value:. e. the entropy is equal to the area under the upper branch of the curve in Fig. 1 and also is equal to the area under the lower branch of the curve, taking into account that the vertical axis is already set to the logarithmic scale. None of the eigenvalues of the Matrix A is smaller by absolute value than 1/4, regardless of N, and a large number of them tend to cluster just above 1/4. Therefore the Kolmogorov entropy can be strictly bound from above by bounding the area under the lower branch of the curve as follows: [13] all lie close to the unit circle. The eigenvalues closest to the circle have |λ| ≈ 1.0085, the farthest |λ| ≈ 1.043. In order to overcome the strong correlations exhibited by RCARRY as a result of this property, Lüscher proposed to skip a predefined number of steps in the sequence [14] .. We have also obtained an heuristic formula for the entropy which is more precise. The actual number of eigenvalues lesser than one by absolute value is asymptotically 2/3 N , and logarithm of their value is observed to lie close to a parabola:. Adding these up, we get an approximate asymptotic formula, which also satisfies the strict bound above:. This estimate appears to be reasonable, i.e. for N = 256 the actual value is h 164.4 versus our estimate of h 157.7. Figure 2 demonstrates graphically that some of the other popular generators do not satisfy the second requirement for randomness. In the case of RCARRY [13] which is a slight modification of a Fibonacci-like recurrence modulo 2 24 , this point has been made before by Lüscher [14] , and its failure was related to the weak mixing properties of its underlying matrix. Unfortunately, the Mersenne Twister (MT) [15] has not been studied from this point of view. Our preliminary investigation indicates that the real eigenvalues of its characteristic polynomial are distributed very close to the unit circle, with the largest being less than |λ max | < 1.0019, see Fig. 3 . In this sense, the underlying dynamical system of the Mersenne Twister has one order of magnitude less entropy than RCARRY. This is related to the singular flaw acknowledged by the authors of the Mersenne Twister, which is that MT has a very long recovery time when it is seeded by a vector containing mostly zeros: the output is observed to be non-random even after outputting a million values. In this instance, the divergence of some trajectory is observed away from the origin. In fact, from the dynamical system point of view this is not merely a manifestation of an unlucky initialization, since any two nearby trajectories of the MT diverge very slowly and this is ultimately what causes the failure of MT in the statistical tests. The total entropy of the MT system is approximately h ≈ 4.8. On the basis of the known behavior of RCARRY and our investigation of the MIXMAX, it appears that an entropy of at least h 50 is required for a generator to have a sufficiently random trajectory. In light of this, it is perhaps not surprising that the Mersenne Twister fails many tests in its pure form, and still fails some tests even when some additional tempering of the output is applied.. One can even conjecture that the sequence produced by the Mersenne Twister would equally benefit from the method of skipping proposed by Lüscher. However, the required amount of skipping would likely be prohibitively expensive. Moreover, it is clear that the recurrences based on primitive trinomials of even higher order such as those found in [17] would exhibit even longer correlations.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O