In the post-genome era, high throughput gene expression profiling has been successfully used to develop genomic biomarker panels (GBP) that can be integrated into clinical decision making. The development of GBPs in the context of personalized medicine is a scientifically challenging and resource-intense process. It needs to be accomplished in a systematic phased approach to address biological variation related to a clinical phenotype  and minimize technical variation (noise). Here we present the methodological aspects of GBP development based on the experience of the Cardiac Allograft Rejection Gene Expression Observation (CARGO) study, a study that lead to the development of a molecular classifier for rejection screening in heart transplant patients.  The completion of the Human Genome Project has revolutionized the field of biomedical sciences by providing an answer to the question of the genetic sequence of the human species . Based oWhole genome GEP technology has been successfully implemented to develop genomic classifiers to detect/predict outcomes in clinical medicine e.g. breast cancer ,13, cardSystems biology is a comprehensive quantitative analysis utilized to understand in which way all the components of a biological unit interact functionally over time. Systems approaches have long been taken, particularly in immunology, physiology, development, and neurobiology. However, technology development during the 1980s permitted the concepts generated by many years of reductionist inquiry to be analyzed in the context of the entire system. Automated DNA sequencers enabled the sequencing of genomes and the definition of polymorphisms among individuals; microarray analysis permitted global transcriptional profiling , but cannot be monocausally explained by the properties of the individual components on a lower system level, . Downward causation are effects of properties on higher levels of the system, e.g. rejection of the heart or MOD on the properties on a lower system level, . Dynamic “feedback loop” behavior is the probabilistic effect of iterative interactions of systems components, e.g. a set of gene transcripts, on the properties of other systems components, e.g. MOD. In essence, the systems biological approach postulates that quantifiable differential expression patterns of individual genes, proteins, and modules on the molecular and cellular level are simultaneously related, in a mathematically describable probabilistic way, to the clinical entity of interest on the highest systems level, the phenotype level . Within the concept of multiscaling of different hierarchical levels, the relationship between properties on the lower system level and emergent properties on the higher system level are key to understanding the phenotype within the concept of systems biology.Systems biological approaches include level distinction , component interaction, emergent properties, downward causation, and dynamic “feedback loop” behavior. et al. published a re-analysis of seven publically available microarray studies which all had the goal of cancer outcome prediction [The major challenge in clinical research and genomic biomarker panel development is to control for circumstances unrelated to the relationship between transcriptome and phenome level . Standarediction ,43. The ediction . The newediction ,45.2): (1) Clinical phenotype consensus definition; (2) Establishment of study logistics; (3) Candidate gene discovery using a combination of genome-wide and knowledge-base approaches; (4) Differential gene list validation using PCR assays; (5) Molecular classifier algorithm development using rigorous statistical methods; and (6) External classifier validation in an independent patient population. Based on a clinically important phenotype of interest, GEP based biomarker panel development is conducted in well defined phases  identification of patients in the early stages of the development of the phenotype of interest, 2) differential diagnosis of the phenotype of interest from related phenotypes, 3) identification of patients at high risk for future development of a phenotype of interest, or 4) assessment of response to a specific therapy.Diagnostic test development requires a clinical standard against which to measure the new test’s performance. The existence of imperfect clinical standards creates a formidable challenge. For the correlation of clinical results with gene expression patterns it is crucial that the clinical phenotype is clearly defined. In the CARGO study the clinical standard was a biopsy-based histopathological assessment . Biopsy Dichotomous phenotypes, typically with extreme ends of a clinical spectrum, with fully developed signs/symptoms on one end and a contrasting picture with no clinical signs/symptoms at the other end, are more likely to be associated with differential gene expression signatures and therefore are more appropriate choices for a proof-of-principle study. Although for this proof-of principle rationale a design based on a dichotomous phenotype is preferable, the choice of a continuous phenotype spectrum may allow for a more practical outcome validation approach. In the phenotype of MOD where continuous scoring systems are used to grade the degree of organ dysfunction e.g. Sequential Organ Failure Assessment (SOFA) score ,53, extrClinical disease phenotypes have historically been described as static entities by the presence or absence of characteristic clinical signs/symptoms and molecular markers of a disease process. While in fact the state of health and disease in human body is a dynamic phenomenon, static phenotype choices are relatively less challenging to assess the associated gene expression patterns. Characterizing the dynamics of a disease process in an organism is intrinsically more difficult than quantifications rooted in static features. One needs to assess and monitor the changes in a biological system over time using time series analysis of GEP. After the consensus definition of the – dichotomous or continuous – phenotype of interest, the establishment of multicenter study logistics is important especially in critical care medicine. A multicenter study design is recommended for genomic studies, it increases sample size but also variabilities. For example, in the CARGO-study, eight US-centers were involved. 3). Pilot or feasibility studies can be helpful in the early identification of problems in the collection, handling and processing of specimens before a larger study is undertaken. These pilot studies may also help in determining the new processes and personnel training needs required before the implementation of a new protocol. For example, the CARGO-study team paid clear attention that the clinical trial protocol addressed microarray-related aspects of sampling, sample processing and storage. The SOP was distributed to the participating centers and it required agreement upon by everyone involved, including clinicians and bioinformaticians. It specified in detail how biopsies should be taken and how blood should be drawn, including the tube type, storage temperature and storage duration. Only centers which agreed on these specifications were allowed to participate in the study. This process must take place well before the first sample is taken, and also include that each center is informed on the technologies that are applied to their samples [In general, sample processing and array analysis should be organized by consensus standardized operating procedures (SOP). Any SOP should be tested for validity in pilot studies , the distance from the collection point to the processing lab and to the storage facility (if this is a different location), stabilizing or preservation techniques for maintaining/preservation of macromolecules required for the specific analyses, and specimen labeling and tracking strategies. Recently the whole blood approach was suggested which reduces the handling steps involved in sample processing but leads to contamination with Globin-mRNA from the RBCs which decreases transcript detection sensitivity and increases signal variation. Methods have been reported to reduce this contamination up to 70%  and geneFor microarray experiments samples need to be processed at a core microarray facility, which follows a specific SOP for each sample. Processing of all samples in batches, in a single microarray laboratory, ideally by one technician, limits the technical noise induced by handling of the samples by several individuals or by using different workstations in different laboratories. On the first level of clinical biomarker panel development one would like to avoid too much unwanted systematic technical noise. After establishing the multicenter infrastructure with motivated centers and established logistics for the sample processing the actual experimental phase of gene discovery is critical.whole-genome wide approach provides more comprehensive scanning to identify significant and novel genes which were not described before in the clinical context of interest. Using different bioinformatics tools e.g. significance analysis of microarray (SAM), GeneSpringGX, and Partek, differentially expressed genes are identified in relationship to the concurrent phenotype of interest.A variety of strategic decisions in the gene discovery process need to be made. The The knowledge-base approach, focusing on known genes, may ignore new biology which might be apparent in a non-hypothesis-driven approach. Although the knowledge-based approach leads to the identification of genes that can distinguish the different phenotypes of interest, whole genome arrays may yield additional, different, or better gene candidates.Both approaches can be combined with a leukocyte microarray and a knowledge-base approach or literature review as adopted in previous studies ,14. The 4). Through these effects on the transcriptome, these genetic variations can affect the results of not only gene discovery but also their validation and later clinical application of GBP. With the lack of current better understanding of the relationship between genetic variations and the transcriptome, it is difficult to address this challenge.The presence of various genetic variations have effect on transcriptome . The proIt has to be noted that in the GBP strategy proposed here, the emphasis is on assembly of expression profiling based candidate genes while purposefully neglecting the additional variation associated with various genetic variations on the genome level as well as the modulatory effect on the proteome level.Since the array-based non-supervised approach to discover differentially expressed genes is limited by sensitivity and variability, a second molecular biological method such as RT-PCR with higher sensitivity and lower variability needs to be applied to internally validate the discovery phase gene list. For realtime-PCR validation studies, genes which do not discriminate between absence and presence of the phenotype of interest are being considered as normalization genes. Additional assays should be included as controls to detect genomic DNA contamination by the difference between a transcribed and non-transcribed region of a gene e.g. the Gus-B gene and a spiked-in control template to determine if the PCR reaction was successful, e.g. the Arabidopsis gene . The differences observed between the microarray and PCR methods in significance of specific genes may be due to 1) lower sensitivity of microarrays leading to the elimination of genes which show discrimination in PCR; 2) enhanced reproducibility of RT-PCR allowing measurement of small differences in gene expression , likely Recently digital mRNA quantification technology  has been suggested to be a potential tool which does not require the enzymatic reactions as compared to real time PCR. This exciting new technology which currently is not using a whole transcriptome approach may be utilized for the validation step during classifier development instead of the RT-PCR approach after the initial gene discovery by array technology . The next-generation sequencing (NGS), with direct sequencing and quantification of DNA and RNA, is opening new avenues for discovery in the post-genome era . The tecThe goal for the development of a diagnostic classifier is the systematic reduction of the internally validated differentially expressed gene list into a minimum number algorithm which then is subjected to external validation/training in Phase 6. One must differentiate between the identification of discriminatory genes and the development of a robust classifier. For example in the CARGO-study, several cytotoxic T-cell genes were not selected for the classifier by the automated and unbiased statistical method of linear discriminant analysis, while PDCD1, a known marker of T cell activation, whose expression is correlated with the other T-cell genes was chosen in the final model. The fact that some of the “well-known” markers of this pathway were not selected by the method points to the strong relevance of PDCD1 as the optimal representative of this pathway and suggests that these other genes were either not incrementally informative or lacked sufficient reproducibility to be included in the model. training and validation sets. The significantly differentially expressed genes identified in the training set and are tested in a validation set to estimate the degree of misclassification. Alternatively, the leave-one-out cross validation approach can be used which involves using a single observation from the original sample as the validation data, and the remaining observations as the training data. This is repeated such that each observation in the sample is used once as the validation data. To test the performance of the selected classifier genes, collected patient samples are typically randomly split into separate The classifier score development poses unique mathematical modeling challenges. Conceptually, correlation strategies  and mutunot evidence that a genomic result is false since the biological literature is not complete in this regard. In fact, genomic technologies enable researchers to discover new genes or pathways associated with disease that may not be expected from the existing literature. If only “expected” genes are accepted in genomics studies, new discovery will be stunted [An important challenge is in understanding how genomic results correlate to known or “expected” biological pathways for the disease state. Biological plausibility is a valid criterion to increase one’s confidence in a genomic result, but the converse—the lack of such information—is  stunted . After constructing the diagnostic test score with specific range using rigorous mathematical modeling (e.g. 0-40 for CARGO classifier) Receiver Operator Characteristic (ROC) curve analysis is performed to define the best possible cutoff on this range with desired sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV) in relationship to the phenotype of interest using Area Under the Curve (AUC) as a criteria to estimate how well the classifier performs.The external clinical validation of the constructed diagnostic classifier in an independent patient sample is critically important in genomic test development. After the initial training set of samples during the test development, it is mandatory to validate these results in an independent patient sample for the generalizability of the classifier performance. For example, in the CARGO-study, an independent cohort of CARGO patients was selected to validate the effectiveness of the LDA classifier defined in the Diagnostic Development phase using a prospective and blinded study protocol. The primary objective of the validation study was to test the prespecified hypothesis that the diagnostic score distinguishes absence of rejection, defined as ISHLT grade 0 from moderate/severe biopsy-proven acute rejection, defined as ISHLT grade ≥3A, both grades determined from local and centralized cardio-pathological examination. This was assessed using a two-tailed Student’s t-test for comparing score distributions for rejection and quiescent samples. Secondary and exploratory objectives included documentation of diagnostic performance across thresholds and description of correlations to clinical variables.In order to establish the test reproducibility and to demonstrate acceptable precision of the Test results further studies need to be designed to study the effect of different variables associated with the specimen handling process which may cause variation in test results e.g. operator-to-operator variation, run-to-run variation, lot-to-lot variation of reagents, plate-to-plate variation, and section-to-section variation of the plates used to run the test.While in the first step after GBP development, test performance should be based on cross-sectional phenotype identification by the GEP-test , in a seIn the last step, the added information derived from intra-individual temporal/longitudinal profiling increase the values of rule out, detection or prediction goals  and alloThe scientific advancements in the post human genome project era have revolutionized the field of biomedical sciences. Using the available GEP technology has been successfully used to develop biomarker panels for different phenotypes in clinical medicine. The process of GBP development is associated with various conceptual, clinical, methodological, and computational challenges. These challenges in the GBP development can be best addressed by adopting the described novel systematic phased approach.


This paper proposes an efficient mixture model for establishing robust point correspondences between two sets of points under multi-layer motion. Our algorithm starts by creating a set of putative correspondences which can contain a number of false correspondences, or outliers, in addition to the true correspondences (inliers). Next we solve for correspondence by interpolating a set of spatial transformations on the putative correspondence set based on a mixture model, which involves estimating a consensus of inlier points whose matching follows a non-parametric geometrical constraint. We formulate this as a maximum a posteriori (MAP) estimation of a Bayesian model with hidden/latent variables indicating whether matches in the putative set are outliers or inliers. We impose non-parametric geometrical constraints on the correspondence, as a prior distribution, in a reproducing kernel Hilbert space (RKHS). MAP estimation is performed by the EM algorithm which by also estimating the variance of the prior model  is able to obtain good estimates very quickly . We further provide a fast implementation based on sparse approximation which can achieve a significant speed-up without much performance degradation. We illustrate the proposed method on 2D and 3D real images for sparse feature correspondence, as well as a public available dataset for shape matching. The quantitative results demonstrate that our method is robust to non-rigid deformation and multi-layer/large discontinuous motion. Even after regularization there remain an exponential number of possible matches between the two sets and efficient algorithms are required to obtain the best solution by removing the false matches. The difficulty of the matching problem is typically made harder by the presence of unmatched points in the two images (due to occlusion or failures of the feature detectors).Establishing reliable correspondence between two images is a fundamental problem in computer vision and it is a critical prerequisite in a wide range of applications including structure-from-motion, camera self-calibration, tracking, image retrieval, and object recognition putative correspondences are computed by using a similarity constraint to reduce the set of possible matches. This putative correspondence set typically includes most of the true matches, the inliers, but also a large number of false matches, or outliers, due to ambiguities in the similarity constraints (particularly if the images contain repetitive patterns). The second stage is designed to remove the outliers and estimate the inliers and the geometric parameters A popular strategy for solving the matching problem is to use a two stage process. In the first stage, a set of Recently, some new non-parametric model-based methods have also been developed to deal with the non-rigidity, such as identifying point correspondences by correspondence function (ICF) In this paper, we generalize the former non-parametric model-based methods to deal with large discontinuities. Rather than these methods which interpolates a global transformation, our approach uses a mixture model and fits the correspondences with a set of transformations. This enables us to capture multi-layer motion, and hence robust to large discontinuous motion. To illustrate the main idea of our approach, we show a simple example in This section briefly reviews the background material that our work is based on. This includes methods for establishing a set of putative correspondences and methods like RANSAC which use robust criteria for performing correspondence assuming parametric geometric constraints. Next we discuss approaches for solving matching problems which solve for a correspondence matrix between point sets.There has been considerable study of robust estimation in the statistics literature The RANSAC algorithm matches two point sets by first computing a putative set and then using robust methods to impose parametric geometric constraints In the recent past, some new non-parametric model-based methods have also been developed, such as ICF Another strategy for point correspondences is to formulate this problem in terms of a correspondence matrix between points (in the two datasets) together with a parametric, or non-parametric, geometric constraint Point correspondence has also been formulated as a graph matching problem, such as the dual decomposition (DD) Suppose now we are given a set of putative image point correspondences We make the assumption that, for the inliers, the noise is Gaussian on each component with zero mean and uniform standard deviation We want to recover the transformations This is equivalent to seeking the minimal energyThe set of transformations There are several ways to estimate the parameters of the mixture model, such as EM algorithm, gradient descent, and variational inference. The EM algorithm E-step: We use the current parameter values M-step: We determine the revised parameter estimate Next we consider the terms of We model The optimal solution of the regularized risk functional (12) is given bywith the coefficient setdetermined by a linear systemwhereis the kernel matrix with the is a diagonal matrix, andProof. The kernel For further details about the reproducing property we refer the readers to Let Form the reproducing property, i.e. Thus Therefore, the optimal solution of the regularized risk functional (12) comes from the space Once the EM algorithm converges, we then obtain the set of transformations RPM-MM.We summarize our method in Solving the transformation Rather than searching for the optimal solution in In contrast to the optimal solution given by the representer theorem, which is a linear combination of the basis functions For the RPM-MM algorithm, the corresponding kernel matrix For the fast implementation, the corresponding kernel matrix is of size Point set registration aims to align two point sets Typically, for a pure point matching problem, the appearance information is not available. In general, if the two point sets have similar shapes, the corresponding points have similar neighborhood structures which could be incorporated into a feature descriptor. Thus finding correspondences between two point sets is equivalent to finding for each point in one point set  the point in the other point set  that has the most similar feature descriptor. Fortunately, the initial correspondences need not be very accurate, since our method is robust to noise and outliers. Inspired by these facts, we use shape context After we get the rough correspondences between two point sets based on their shape features, we fit a set of transformations and use them to warp the model points. To this end, we need to determine the attribution of each model point . First, according to the matching results based on the rough correspondences, we can determine the attributions of the inliers; for the rest of the model points, we attribute them to their nearest neighbors which already have attributions. The two steps of estimating correspondences and transformations are iterated to obtain a reliable result. In this paper, we use a fixed number of iterations, typically The performance of point matching algorithms typically depends on the coordinate system in which points are expressed; here we use data normalization to control for this. More specifically, we perform a linear re-scaling of the correspondences so that the points in the two sets both have zero mean and unit variance. Furthermore, we define the transformation The EM algorithm is well known to converge to a local maximum. To initialize the EM iteration, we first use the K-means algorithm to cluster the correspondences into There are mainly three parameters in our algorithm: In order to evaluate the performance of our algorithm, we conducted two types of experiments: i) sparse image feature correspondence on 2D images and 3D surfaces; ii) non-rigid shape matching on synthetical 2D datasets.Tree and Church) are wide baseline images which are public available Books) contains some shared content which is taken by ourselves. It often occurs in image or object retrieval. The 3D surface pairs  are objects with different amounts of non-rigid deformation, which come from a surface correspondence benchmark We present a few representative matching results on both 2D image pairs and 3D surface pairs, as shown in The match correctness is determined as follows. For the 2D images, a method combining subjectivity and objectivity is considered. We first fit the epipolar geometry  by RANSAC and use it to determine the match correctness. We further confirm them artificially. Although the judgment of correct match and mismatch seems arbitrary, we make the benchmark before performing experiments to ensure objectivity. For the 3D surfaces, the ground truth correspondences are supplied by the dataset.The experimental results are evaluated by precision and recall, where the precision is defined as the ratio of the preserved inlier number and the preserved correspondence number, and the recall is defined as the ratio of the preserved inlier number and the inlier number contained in the putative correspondences. We compare our RPM-MM algorithm with other four methods which remove outliers from given putative point correspondences, such as RANSAC Tree or Person which involves a small amount of rotation, viewpoint change, or non-rigid deformation, the mixture model in our algorithm then degenerates and contains only one component. In this case Our RPM-MM algorithm is equal to a normal non-parametric model-based method, e.g., VFC Church, Books, Centaur and Dog. Note that in the image pair of Church, our RPM-MM preserves the correspondences on the sky; this is very useful since removing inliers outstanding in depth tend to make the recovery of epipolar geometry unstable and ill conditioned Books shows our method's capability in image retrieval.The results of our RPM-MM are presented in Books. The graph matching based method GS generally can obtain better performance than ICF and VFC in case of large discontinuity, e.g., Books. But its recalls are still relatively low compared to our RPM-MM. In We further quantitatively compare our RPM-MM to four state-of-the-art point matching algorithms: RANSAC, ICF, GS and VFC. We next evaluate our RPM-MM on the non-rigid point set matching problem, more specifically, shape matching. For the dataset, we choose the same synthesized data as in To provide a quantitative comparison, we report the registration results of the five algorithms on all the 100 samples. We compute the recall as the metric used in Within this paper, we have proposed and studied a new robust point matching algorithm based on a mixture model (RPM-MM). It interpolates a set of transformations to fit different layers of the motion correspondence by an iterative EM algorithm, and hence establish reliable correspondence between two images. Quantitative comparisons on both sparse feature correspondence and shape matching demonstrate that our algorithm outperforms state-of-the-art point matching methods, especially when the motion of the scene contains large discontinuities.


Power grids deal with the business of generation, transmission, and distribution of electric power. Recently, interest in power quality in electrical distribution systems has increased rapidly. In Korea, the communication network to deliver voltage, current, and temperature measurements gathered from pole transformers to remote monitoring centers employs cellular mobile technology. Due to high cost of the cellular mobile technology, power quality monitoring measurements are limited and data gathering intervals are large. This causes difficulties in providing the power quality monitoring service. To alleviate the problems, in this paper we present a communication infrastructure to provide low cost, reliable data delivery. The communication infrastructure consists of wired connections between substations and monitoring centers, and wireless connections between pole transformers and substations. For the wireless connection, we employ a wireless sensor network and design its corresponding data forwarding protocol to improve the quality of data delivery. For the design, we adopt a tree-based data forwarding protocol in order to customize the distribution pattern of the power quality information. We verify the performance of the proposed data forwarding protocol quantitatively using the NS-2 network simulator. Korea Electric Power Corporation (KEPCO)  in KoreaVarious issues such as measurements, controls, databases, and communications must be considered for expanding the power quality monitoring system. We focus on designing an efficient communication network in the electrical distribution system (EDS). Wireless multi-hop communication paradigm is often employed to construct EDS to reduce the deployment and management costs. While wireless sensor networks (WSNs) have been widely studied in the abstract –9, to ouA WSN is a system composed of numerous computing and sensing devices distributed within an environment to be monitored. In the past decades, WSNs have been applied to autonomous use of computing, sensing, and wireless communication devices for both scientific and commercial purposes ,11. For Our goal was to design a reliable WSN for a power quality monitoring system to provide high data delivery quality with the least cost of communication system installation and maintenance. EDS delivers power quality information generated by pole transformers to remote monitoring centers in residential divisions. Usually, the power quality information is periodically measured, gathered, and transmitted to the monitoring centers. Once power quality measured at a pole transformer is out of a normal range, an alarm message with detailed contents is promptly sent in the event based manner. To improve the performance in terms of the data delivery rate and the distribution delay of the power quality data, we customize our communication protocol for the distribution pattern of the power quality information. We also examine the performance tradeoffs between the data delivery rate and the distribution delay of the power quality data.The remainder of this paper is structured as follows. Section 2 describes our system architecture and design goals. Section 3 explains our communication infrastructure and discusses the proposed tree-based data forwarding protocol. Following this, we verify the designed system by NS-2 simulations in Section 4. Finally, Section 5 summarizes our results, discusses our future plans, and offers conclusions.2.2.1.The EDS in Seoul, Korea  has tensThe distribution network for the EDS consists of three subsystems as shown in 2.2.Despite the fact that power quality monitoring in EDS is becoming more and more important, the performance of existing systems is not sufficient to provide expanded power quality monitoring services. The system has a quite high communication cost of installation and maintenance. In the systems, a major pole transformer periodically gathers data sensed from pole transformers within its coverage using single-hop or multi-hop communication. The major pole transformer sends the gathered data towards the monitoring center through the cellular communication network every 30 minutes. The limitations of the monitoring system are as follows: first, since the data is only transmitted every 30 minutes due to the high communication cost, the power quality data cannot be delivered in a timely fashion to the remote monitoring centers. Second, it is difficult to monitor power quality using only the basic electric quantities such as voltage, current, and temperature of pole transformers. Finally, data collection is severely limited for a specific small area.♦ The power quality data is measured every one second.♦ The measured data is transmitted to the monitoring center every transmission interval.♦ The system is operated over a medium or large-size city. We plan to deploy our system at the area which is covered by a residential division.♦ For the usual data, a high data delivery ratio is important within the acceptable transmission delay.♦ For the alarm data, the distribution delay of the power quality data is important as well as the data delivery ratio.♦ The use of the current off-the-shelf radio interfaces is recommended.To overcome the limitations, we have designed a communications system to provide data delivery service in a timely and reliable manner. The design goals of our system are as follows:3.3.1.In this subsection, we design a communication infrastructure for EDS power quality delivery as seen in The collection subsystem can be implemented by using the IEEE 802.15.4  or IEEE 3.2.3.3.Power quality is any power problem manifested in voltage, current, or frequency deviations, that results in failure or malfunctioning of the customer equipment. Power quality information includes harmonics, voltage sags, voltage swells, unbalance of network voltage, electric switching device load effects, and so on. Our power quality system monitors the first three information items. 3.4.Since not every transformer is placed within the communication range of the monitoring center, data cannot be sent directly. Instead, it has to hop its way towards the monitoring center using other transformers as intermediate hops. In EDS, all pole transformers can be data sources, while the monitoring center alone is a data sink. In addition, the network topology in EDS is stationary. We design the data forwarding protocol for the collection subsystem. Since multi-hop and many-to-one nature of data flows, we come up with a new tree-based data forwarding protocol for delivery of the power quality information from each pole transformer  to a monitoring center.One of cost factors in constructing the least cost path from a node to the substation is the number of hops between the node and the substation. At the network initialization stage, the substation floods a PROBE message over the entire network so that each node in the network can infer the minimum number of hops from the substation to itself. Thereafter, the substation floods a PROBE message periodically so that nodes can update their hop distance from the substation. The path cost is used in constructing the tree branch between the substation and one node.The substation starts the tree construction procedure by sending a PROBE message to its neighboring nodes via one-hop flooding. In the PROBE message, the path cost from the substation to the node having sent the message is recorded. When a node receives a PROBE message, it compares the path cost stored in the message with its path cost. If the path cost maintained locally at the node is less than that in the PROBE message, the node ignores the message. Otherwise, the node updates its path cost and configures the node having sent the PROBE message as its parent node. Once the node newly determines its parent node, it sends a PROBE message with updated path cost to its neighboring nodes via the one-hop flooding. This process continues until the node which has no child node receives a PROBE message.Each node on the tree maintains the soft states on its parent node and child nodes in its forwarding table. A node periodically sends a HELLO message including its parent node ID to its neighboring node as its heartbeat. When a node receives a HELLO message, it checks whether the sender of the message is one of the parent node and child nodes. If it is, the node having received the message updates the soft state on the node having sent. Otherwise, if the node having received the HELLO message is the parent node of the node having sent, it adds the node having sent to a list of its child nodes.If a node or the wireless link to the node fails, its parent node and child nodes do not receive any HELLO messages from the node for a given amount of time. Thus, the parent node and child nodes of the failed node release the soft state on the node. The node detecting the failure of its parent node tries to repair the broken tree branch by sending a REPAIR message to its neighboring nodes via the one-hop flooding. Once a neighboring node receives the REPAIR message, it responds with a REPAIR_ACK message having its path cost. Then, the node having sent the REPAIR message receives the REPAIR_ACK message(s) and it selects the node having the least path cost as its parent node. The detailed procedure of the tree construction is depicted in Once the data forwarding tree is constructed, the power quality data is delivered to the monitoring center through the tree. Whenever a node has data to send, periodically or in the event-based manner, the node transmits the data to its parent node. The parent node checks whether the sender is one of its child nodes. If it is, the parent node forwards the data to its upstream node towards the monitoring center. This forwarding process continues until the monitoring center receives the power quality information.4.In this section, we will describe our simulation environment for our protocol evaluation. To evaluate the performance of the proposed data forwarding protocol quantitatively, we use NS-2 network simulator . The colThe collection subsystem is made up of one substation and six feeders. Each feeder collects the power quality data from 300 pole transformers placed in a random pattern. In other words, a total of 1,800 nodes are deployed in the network. The distance between two neighboring pole transformers is set to around 200–300 meters. The transmission radius of a node (a communicating device on the pole transformer) is 500 m. We use the shadowing model in ,22. As tThe traffic pattern generated by each node is the constant bit rate (CBR). The size of a CBR data packet is set to 1,500 bytes and the total simulation time to 3,600 seconds. Since the RTS/CTS/ACK exchange adds significant overhead in the IEEE 802.11 based network, the data throughput with 1,500-byte data packet is higher than that with 64-byte or 500-byte packet . Thus weSince the packet drop probability in a wireless multi-hop communication environment increases with the number of hops , in the In general, the collision domain size is a major consideration to determine the effective throughput. However, since pole transformers stand in a row in our system, the power quality data is forwarded in a peer-to-peer manner except at branch nodes with more than one child node. The collision domain size is three. As shown in the figure, the effective throughput is acceptable for providing the desired power quality monitoring service.ad hoc network increases as the network diameter increases [Only the cases of the substation successfully receiving data are counted in. It is well known that the transmission delay in a wireless ncreases . BesidesAs we can see in 5.Our communication infrastructure reduces the cost of network installation and maintenance.Our wireless sensor network delivers rich quantities of data for providing the power quality monitoring service.Our data forwarding protocol provides a data delivery service in a timely and reliable manner.Our experiment shows the feasibility of the designed system by evaluating the performance of our data forwarding protocol using a NS-2 network simulator.We have designed a wireless sensor network for a power quality monitoring system. The contributions of this paper are stated as follows:In our work, we did not consider the case that a node is lost or the network is congested in the in-line and multi-hop architecture. It is an important matter to improve the system reliability. As a further work, we will solve the problem using an artificial intelligence technique such as neural network and Bayesian network.


Pharmacological research is hampered by scattered data which have to be retrieved by varying methods and in different data formats. This heterogeneity increases research costs and limits throughput. Over the last two years, the Open PHACTS Discovery Platform  has beenFollowing an application-oriented approach, the Open PHACTS project started with the definition of potential use cases in the form of prioritized research questions , most ofHere, we present the ChemBioNavigator (CBN) , a web aIn this contribution we roughly outline the OpenPHACTS/CBN technology and present a number of high-priority research questions, highlight the advantages of exploiting the integrated data through the CBN's smart and intuitive interface.


To avoid misinterpretation and mismanagement clinicians should be aware of the interference of new direct oral anticoagulants (DOA) on coagulation assays. A variety of oral anticoagulants targeting specific coagulation factors has already entered the market, and new indications for DOA will be released each year over the next few years. Due to their heterogeneous mode of action and different pharmacokinetic profile each DOA will vary in its effects on coagulations assays, and it is of current importance to recognize these variable effects.In this summary the main considerable factors for correct laboratory test interpretation under DOA treatment are described. In multiple clinical situations, coagulation parameters as the prothrombin time (PT) are checked for safe and effective treatment with vitamin K anticoagulants. Global Therefore, the main considerable factors for correct laboratory test interpretation under DOA treatment have to be known.Usually, the time between the addition of a coagulation activating reagents and the formation of the fibrin clot is measured. Increasing viscosity in the plasma sample allows the detection of the end point. Inhibition of Factor Xa results in inhibiting the formation of thrombin. Thrombin itself is needed to format the fibrin clot, which is detected in a coagulation clotting assay. Because rivaroxaban and apixaban are selective direct inhibitors of Factor Xa and dabiagtran a direct thrombin inhibitor, coagulation clotting assays are influenced by these drugs Figure .The in vitro and ex-vivo influence of rivaroxaban and dabigatran on global and specific coagulation assays are well described-13, sincThe results of the ex-vivo and in-vitro effects of these new drugs on the coagulation parameters as PT or aPTT can be used to find out the main considerable factors for correct interpretation of laboratory test results in patients under DOA treatment.To avoid erroneous interpretation it is important to be aware of the four main factors that have to be considered at the same time when coagulation parameters are measured in plasma with DOA Figure .1. Dependence on the drug.Different mode of action – different effects on coagulation assaysRivaroxaban and apixaban as selective direct inhibitors of factor Xa, inhibit prothrombinase-bound factor Xa. Since the PT assay is sensitive to the prothrombinase complex, the PT is influenced by direct factor Xa inhibitors of factor Xa, whereas dabigatran in low doses has little effect on PT Figure.As screening assay the aPTT detects abnormalities in kininogen, prekallikrein, XII, XI, IX, VIII, V, and factor Xa and thrombin, both targets of rivaroxaban and dabigatran. A prolongation of aPTT is observed in the presence of increasing doses of dabigatran and –to a much less extend – with rivaroxaban or apixaban,9,14,15 The thrombin time (TT) is used to measure fibrin polymerisation and is performed by adding a low concentration of thrombin to plasma. Since the TT assay is a direct measurement of thrombin activity, and dabigatran is a direct thrombin inhibitor, the TT assay is highly sensitive to dabigatran TableClinicians should find out which drug was administrated to the patient before routine coagulation parameters are checked and interpreted.2. Dependence on the drug pharmacokinetics and drug concentration.More drug concentration in plasma-more effect on coagulation assaysDabigatran, rivaroxaban and apixaban have predictable pharmacokinetic profiles with plasma concentrations reaching a peak within 2-4 hours after intakeand terminal half-lives of 7-17 hours. Corresponding to the half-lives, a decline of plasma concentration is observed over time; and consequently the effects of DOA on coagulation parameters also decrease over time. Since there is this direct correlation between drug concentration and its effect on clotting assays, it is of prime importance to know the time of drug intake in relation to blood sampling as can be seen for example in the values of PT and aPTT in Figures The prolongations of PT and aPTT are short lived. Two to three hours after DOA administration, a significant prolongation expressed in seconds of PT or aPTT will be observed. The minimum effect of DOA is observed directly before drug intake.Therefore it is recommended, that laboratory monitoring of coagulation factors, if needed, should be performed directly before drug administration to keep the interaction of drugs low.3. Dependence on the test method used in the laboratory.Different test methods – different test resultsThe knowledge of the test method used for determination of the coagulation parameter is important for correct interpretation of results. Two examples:a. Antithrombin determination can be performed using test systems, in which the plasma sample is incubated either with an excess of thrombin, or with an excess of factor Xa. Antithrombin levels based on addition of factor Xa are measured falsely high when rivaroxaban plasma samples are investigated. No influence is observed by rivaroxaban, when determination of antithrombin levels is based on thrombin.b. The frequently used method for the determination of fibrinogen as one of the routine parameters in coagulation testing is the method according to Clauss. This method is based on the addition of an excess of thrombin to plasma. Rivaroxaban, does not influence fibrinogen assays based on the Clauss method because it does not inhibit thrombin activity. AnotherTherefore, clinicians should to be aware of various effects of DOA depending on the test method. Dabigatran as direct thrombin inhibitor, influences fibrinogen assays based on Clauss method at increasing drug concentrations and test reagents- and to much less extend PT derived fibrinogen assays.4. Dependence on the test reagent used in the laboratory.Different test reagents – different influence of DOASeveral ex-vivo and in-vitro studies have shown that the prolongation of PT and aPTT depends on the different sensitivities of the thromboplastin reagents and APTT reagents used for the assay-12. For Therefore, clinicians should also be aware of the different sensitivities of DOA on various test reagents within one test parameter.In emergency situations like acute bleeding the clinicians could be misled by the results of coagulation tests, which are affected depending on various factors as the pharmacokinetics of the drug after intake or the type of test assay used.For correct interpretation of coagulation assays during DOA treatment, clinicians need to know about the temporal effects of the target specific drugs on coagulation tests and the pitfalls in the laboratory.The results of coagulation assays may differ from laboratory to laboratory depending on the test reagent and test method used in the laboratory, making comparisons between the laboratories difficult.In future, practical approach in the laboratory is necessary and a comprehensive system of optimal patient care by exact interpretation of coagulation results under DOA treatment has to be developed worldwide in future.Prof Lindhoff-Last received honoraria for lecturing and consulting fees within the last 10 years from Bayer HealthCare, Boehringer Ingelheim, Pfizer-BMS, Roche Diagnostics, Siemens Diagnostics, Instrumentation Laboratory.Dr. Mani declares any conflict of interest.HM drafted the manuscript. ELL revised the manuscript. Both authors read and approved the final manuscript.


This paper illustrates the use of a combined neural network model based on Stacked Generalization method for classification of electrocardiogram (ECG) beats. In conventional Stacked Generalization method, the combiner learns to map the base classifiers' outputs to the target data. We claim adding the input pattern to the base classifiers' outputs helps the combiner to obtain knowledge about the input space and as the result, performs better on the same task. Experimental results support our claim that the additional knowledge according to the input space, improves the performance of the proposed method which is called Modified Stacked Generalization. In particular, for classification of 14966 ECG beats that were not previously seen during training phase, the Modified Stacked Generalization method reduced the error rate for 12.41% in comparison with the best of ten popular classifier fusion methods including Max, Min, Average, Product, Majority Voting, Borda Count, Decision Templates, Weighted Averaging based on Particle Swarm Optimization and Stacked Generalization. Accurate and computationally efficient means of classifying electrocardiography (ECG) arrhythmias has been the subject of considerable research effort in recent years. Electrocardiography deals with the electrical activity of the heart Several algorithms have been developed for the detection and classification of the ECG signals As for classifiers, Artificial Neural Networks have been used in a great number of medical diagnostic decision support system functions obtained after dilatation and translation of an analyzing wavelet In this paper, we propose a new combination method for classifying normal heartbeats, Premature Ventricular Contraction (PVC) and other abnormalities. In the preprocessing module, an Undecimated Wavelet Transform is used to provide an informative representation that is both robust to noise and tuned to the morphological characteristics of the waveform features. For feature extraction, we have used a suitable set of features that consists of both morphological and temporal features. This way we can keep both spatial and temporal information of signals. For classification we have used a number of diverse MLPs neural networks as the base classifiers that are trained by Back Propagation algorithm. Then we employed and compared different combination methods. In our proposed method, unlike the Stacked Generalization, the second level classifier (combiner) receives the input pattern directly adding on the base classifiers outputs. In fact, in the learning phase, the combiner learns the expertise areas for each base classifier. In the test phase, based on spatial position of the input data, and by considering the expertise areas of all base classifiers, the combiner specifies the weights for optimal combination of the decisions from the base classifiers.Therefore, we expect that the Modified Stacked Generalization method to be able to use both fusion and selection mechanisms for various test samples, proportional to the position of the sample in the problem space. We used 10 different combination methods: Max, Min, Average, Product, Majority Voting, Borda Count, Decision Templates, Weighted Averaging based on Particle Swarm Optimization, Stacked Generalization and Modified Stacked Generalization. Experimental results indicate that our proposed combining method performs better than other combining methods.An ECG consists of three basic waves: the P, QRS, and T. These waves correspond to the far field induced by specific electrical phenomena on the cardiac surface, namely, a trial depolarization (P wave), ventricular depolarization (QRS complex), and ventricular repolarization (T wave). One of the most important ECG components is the QRS complex The MIT–BIH arrhythmia database There are over 109,000 labeled ventricular beats from 15 different heartbeat types. There is a large difference in the number of examples in each heart beat type. The largest class is “Normal beat” with about 75,000 examples and the smallest class is “Supra ventricular premature beat” with just two examples. The database is indexed both in timing information and beat classification. We used a total of seven records marked as: 100, 101, 102, 104, 105, 106, and 107 in the database. We extracted a total of 15,566 beats: 8390 normal beats, 627 abnormal PVC arrhythmia beats, and 6549 other arrhythmia beats. We used the database index files from database to locate beats in ECG signals. Of all these 15566 beats, we used 450 beats for training, 150 beats for validation and the other 14966 for testing our networks. This way we assigned equal number of samples to each class in our training and validation phases .The objectives of preprocessing stage are the omission of high-frequency noise and the enhancement of signal quality to obtain appropriate features. ECG signal is measured on static conditions since various types of noise including muscle artifacts and electrode moving artifacts are coupled in dynamic environment. To remove such noises an advanced signal processing method, such as discrete wavelet transform denoising technique Suppose the signal This figure shows a decomposition of three levels: the blocks of A MLPs is a supervised, fully-connected feedforward artificial neural network which learns a mapping between a set of input samples and their corresponding target classes. The MLPs is in fact an extension of the Perceptron neural network which was originally proposed by Rosenblatt in 1957 A MLPs is consisted of one input layer, one or more hidden layers and one output layer. For the Combining is an approach to improve the performance in classification particularly for difficult problems such as those involving a considerable amount of noise, limited number of patterns, high dimensional feature sets, and highly overlapped classes. From a computational viewpoint, according to the principle of divide-and-conquer, a complex computational task is solved by dividing it into a number of computationally simple tasks and then combining the solutions of those tasks. In supervised learning, computational simplicity is achieved by distributing the learning task among a number of experts, which in turn divides the input space into a set of subspaces Static structures: In this class of combining methods of neural networks, the responses of several predictors  are combined by means of a mechanism which does not involve the input signal; hence the designation “static”.Dynamic structures: In the second class of combining methods, the input signal is directly involved in actuating the mechanism that integrates the outputs of the individual experts into an overall output; hence the designation “dynamic” The combination methodologies from the combiner viewpoint are divided into two categories: non-trainable and trainable. Simple algebraic combiners are, in general, non-trainable combiners of continuous outputs. In non-trainable classifiers, the total support for each class is obtained as a simple function of the supports received from individual classifiers. Following the same notation in Mean Rule (Averaging). The support for In either case, the ensemble decision is taken as the class Minimum/Maximum/Median Rule. As the names imply, these functions simply take the minimum, maximum or the median among the classifiers' individual outputs.Product Rule. In product rule, supports provided by the classifiers are multiplied. This rule is very sensitive to the most pessimistic classifiers: a low support (close to 0) for a class from any of the classifiers can totally remove the chance of that class to be selected. However, if individual posterior probabilities are estimated correctly at the classifier outputs, then this rule provides the best estimate of the overall posterior probability of the class selected by the ensemble.Majority Voting. Majority voting follows a simple rule: it will vote for the class which is chosen by maximum number of individual classifiers. Let us define the decision of the Borda Count. The Borda count is originally a voting method in which each classifier gives a complete ranking of all possible alternatives. This method, introduced in 1770 by Jean-Charles de Borda, is used if and when the classifiers can rank-order the classes. This can be easily done if the classifiers provide continuous outputs, as the classes can then be rank-ordered with respect to the support they receive from the classifier. However, Borda count does not need the values of these continuous outputs, but just the rankings, hence it qualifies as a combination rule that applies to labels. In standard Borda count, each voter (classifier) rank-orders the candidates (classes). If there are Unlike non-trainable combiners, in trainable combiners, a learning process makes the combiner learn to map the base classifiers' outputs to the target space.Decision Template Method. Decision templates, DTs, were proposed by Kuncheva in Weighted Averaging Based on Particle Swarm Optimization. In combining classifiers, since the base classifiers are diverse from each other, it seems that a weighted combination of their outputs yields better results in comparison with simple averaging method (explained in section 3.2.1).n-dimensional space of the real numbers. In Particle Swarm Optimization, each particle in the swarm represents a point in the solution space. The position of a particle is influenced by the best position visited by itself and the position of the best particle in its neighborhood. When the neighborhood of a particle is the entire swarm, the best position in the neighborhood is referred as the global best particle, and the resulting algorithm is referred to as a In Eq. (18), the inertia weight Stacked Generalization Method. Stacked generalization is a technique proposed by Wolpert In this section, we introduce our proposed method, the Modified Stacked Generalization, and after justifying its use, we will demonstrate how this method improves the performance of a classification problem.optimal weights for combining the outputs of the base classifiers regarding the input sample In combination methods discussed in sections 3.1 and 3.2, the outputs of the diverse base classifiers are combined together in different ways. In section 3.1 a fix rule combines the classifiers' results, independent from knowing how the problem space is broken. In section 3.2 we described the Stacked Generalization method in which, during the training phase, the combiner learns proper weights for combining the outputs of the base classifiers for an input sample. These weights are then used in the test phase to optimally combine the outputs of the base classifiers for each test sample. In this method, as the weights for combining the outputs of the base classifiers are obtained during a learning process which endows the power of generalization to the model, we expect the model to have a better performance in comparison with the fix combination rules. However, since the learning process of the combiner is only depended on the outputs of the base classifiers, it does have no information about the relation between the input problem space and the votes of the base classifiers. As the result, the combiner just learns the mapping between the outputs of the base classifiers and the target classes of the input samples, rather than learning the mapping between the input samples and their corresponding target classes. In other words, the combiner never sees the distribution of the input problem space, and therefore it cannot assign the In the training phase, by looking at the input sample and the base classifiers' outputs, the combiner learns how to divide the problem space between the base classifiers. In the testing phase and for each input sample, the combiner uses the representation of the input sample and the previous knowledge about the expertise of the base classifiers to optimally determine the weights to aggregate the votes of base classifier(s).In this section, after a description of preprocessing module and feature extraction, as we are proposing a new combining strategy, we continue with important issues for choosing the base classifiers' parameters. We then show the improvement of the proposed method over some combining methods followed by error analysis using some evaluation metrics to properly assess the effectiveness of our proposed algorithm. We finally bring the comparison with other related methods in the literature.For denoising we have used the Daubechies wavelet functions (db1) with decomposition level of five. This selection is based on our extensive experiments among which one sample is shown in . In thisIn this study, we have used the Savitsky–Golay filtering method for smoothing of the ECG signals Thus, ten ECG morphological features are extracted, as well as one timing interval feature. To extract this feature we propose a two-steps method. The first step involves the cutting of the normal and PVC and other beats by making use of the annotation files which exist in MIT–BIH arrhythmia database. The second step involves identification of peaks and valleys in every normal or abnormal beat and obtaining their respective amplitudes and positions. In order to break to normal and abnormal beats, we process annotated file records from MIT–BIH database. For example to extract normal beats, the algorithm examines the annotation file which contains the sample number of the normal beat. Then it creates a matrix with rows equal to the number of normal beats. An R-wave detector is required to initialize our computer-aided ECG classification process. Next, the algorithm saves 40 samples surrounding the target normal beat from all the recorded samples. The sample beat itself is also saved in the final matrix. We extracted the abnormal beats in the same manner too. After classification of normal and abnormal beats, peaks and valleys are detected. For this purpose, we implemented the Al-Alaoui algorithm An important issue in combining classifiers is the diversity of the classifiers in learning the input data. When the base classifiers of a combining structure are diverse , they become specialized in specific areas of the input space, and consequently have fewer errors in those areas. Thus, combining the outputs of classifiers that are perfectly diverse, improves the overall performance. For diversifying the base classifiers, different training parameters and classifiers with different structures have used.In our combining methods we have used MLPs classifiers with Back Propagation training algorithm as our base classifiers. For each classifier, some parameters are pre-specified according to the characteristics of our classification task, e.g. the number of input nodes is equal to the number of extracted features of each input sample, while the number of output nodes is specified on the basis of the number of classes. Some parameters including the initial weights and the learning rate are specified by trial and errors. The number of neurons in the hidden layer of the MLPs is also determined via trial and error method. In this manner, as graphically depicted by For evaluation, we used the same diverse classifiers as the base classifiers and then their outputs are aggregated via different combining strategies. In all experiments, a number of base classifiers are first created. In test phase, for Max, Min, Average and Product methods, their outputs are combined with corresponding rules. In Stacked Generalization scheme, the outputs are used as inputs to second level classifier to learn the mapping between the base classifiers' outputs and the target of test sample. In MSG method, the outputs and a direct input pattern are used as inputs to a second level meta-classifier to learn the mapping between the base classifiers' outputs and the actual correct labels. The input of the combiner (second-level classifier) in conventional Stack Generalization has nine elements. In our proposed method, in addition to these nine inputs, the combiner also receives the original input pattern (which has eleven elements). Altogether, the input of the combiner would be a 20-element vector. In Here, two relatively important points can be derived from We analyzed the performance of our proposed method based on some evaluation metrics described in The test performance of the classifiers can be determined by the computation of the following four statistical parameters:Specificity: number of correctly classified normal beats over total number of normal beats.Sensitivity (PVC): number of correctly classified premature ventricular contraction beats over total number of premature ventricular contraction beats.Sensitivity (other): number of correctly classified other beats over total number of other beats.Overall classification accuracy: number of correctly classified beats over number of total beats.These parameters are computed as shown in The last part of this section is the comparison of the recognition rates for the proposed method with some popular classifiers in the literature see .In summary, this paper presented a new combining method for classification of the ECG beats based on Stacked Generalization. By aggregating the original input patterns to the outputs of the base classifiers, and as the result, by increasing the knowledge of the combiner, we helped it make a better decision according to the base classifiers' decisions. Experimental results and higher recognition rates of the proposed method support our claim that such additional knowledge lets the combiner to find a better solution.


Mathematical models have been used to study the dynamics of infectious disease outbreaks and predict the effectiveness of potential mass vaccination campaigns. However, models depend on simplifying assumptions to be tractable, and the consequences of making such assumptions need to be studied. Two assumptions usually incorporated by mathematical models of vector-borne disease transmission is homogeneous mixing among the hosts and vectors and homogeneous distribution of the vectors.We explored the effects of mosquito movement and distribution in an individual-based model of dengue transmission in which humans and mosquitoes are explicitly represented in a spatial environment. We found that the limited flight range of the vector in the model greatly reduced its ability to transmit dengue among humans. A model that does not assume a limited flight range could yield similar attack rates when transmissibility of dengue was reduced by 39%. A model in which mosquitoes are distributed uniformly across locations behaves similarly to one in which the number of mosquitoes per location is drawn from an exponential distribution with a slightly higher mean number of mosquitoes per location. When the models with different assumptions were calibrated to have similar human infection attack rates, mass vaccination had nearly identical effects.Small changes in assumptions in a mathematical model of dengue transmission can greatly change its behavior, but estimates of the effectiveness of mass dengue vaccination are robust to some simplifying assumptions typically made in mathematical models of vector-borne disease. Aedes aegypti. Most deterministic models include both the human hosts and the mosquito vectors, but the level of mechanistic detail can vary. One of the most common assumptions used in mathematical models of vector-borne disease transmission is that of homogeneous mixing of the vectors and their human hosts Mathematical models of dengue transmission can help elucidate the dynamics of infectious disease transmission and will likely play a role in planning for interventions such as mass vaccination Aedes aegypti, is a mosquito that flies short distances A. aegypti. There are also complex immune interactions among the circulating serotypes, which could cause temporal and spatial patterns of infection Models of vector-borne disease have been studied for nearly a century, starting with the malaria model of Ross Here, we use an individual-based model of dengue transmission to examine the effect of making different assumptions about mosquito movement and distribution. In this model, individual households are represented in space, and infectious mosquitoes can travel among the households, schools, and workplaces. The model also represents the four dengue serotypes, and infection by one serotype confers short-term protection against infection by the others. Using an individual-based model allows one to easily change assumptions about mosquito movement and spatial distribution. Because of recent progress towards producing a dengue vaccine https://github.com/tjhladish/dengue/ on March 15, 2013. In brief, a synthetic population of 207,591 individuals with demography based on that of rural Thailand was assigned to households according to census data We used an individual-based model to simulate the spread of dengue in a semi-rural region in Thailand over a single year. A full description of this model, including the synthetic population and parameter settings, is in All of these locations are associated with a mosquito population that can infect or be infected by humans. The model runs in one-day time steps, and the daily probability of contact between an individual human and a single mosquito is proportional to the amount of time they spend in the same location. The proportion of human–mosquito contact that occurs in the household is proportional to the amount of estimated mosquito biting activity before 9am and after 5pm, while the remaining transmission occurs in the locations where individuals are during the day, which may be work, school, or home  of moving to a randomly selected location regardless of distance (We studied the relationship between mosquito mobility and dengue transmission in the model. The distance  [23]. Wedistance . When thdistance . Howeverwell-mixed, mosquitoes moved to a random location each day regardless of flight distance (and from mosquito-to-person (In a version of the model in which the mosquito population was distance . In the distance . If mosqdistance . To reduIn the models described above, each location had the same number of resident mosquitoes. When the number of mosquitoes per location was drawn from an exponential distribution with the same mean number of mosquitoes per location , transmi4 = 24%. When the three models with different assumptions about mosquito movement and spatial distribution were calibrated to have the same attack rates in the absence of vaccination, mass vaccination had similar effects on human infection attack rates for all levels of coverage tested (leaky instead of all-or-none, so that vaccinees had a 70% reduction in susceptibility to infection per infectious mosquito bite. There was little difference in effectiveness of mass vaccination when the vaccine protection was leaky instead of all-or-none (indirect protection, defined as one minus the attack rate among unvaccinated individuals in a partially vaccinated population divided by the attack rate among a fully unvaccinated population, surpassed 70% once 60% of the population was vaccinated (We measured the effectiveness of mass vaccination of the human population in the models described above. The vaccine was 70% effective at preventing infection with any of the four dengue serotypes in the vaccinee. Therefore, 70% of vaccinees were completely protected against infection by a given dengue serotype. The probability of protection against a single serotype was independent of protection against the other three for a given vaccinated individual, so the probability that a vaccinated individual was protected against all four dengue serotypes was 70%e tested . In the -or-none . Overall-or-none . We founccinated .A. aegypti, to stay within a building and fly only short distances greatly affects its vectorial capacity. However, once a model is calibrated to obtain a realistic human infection attack rate, the population-wide effectiveness of mass dengue vaccination is robust to these assumptions. Although the model variants were calibrated to produce the same average infection attack rates, the spatial heterogeneity of infection risk could be different. When mosquito mobility is low, dengue risk would be elevated near dengue cases and lower elsewhere. In areas of high risk, a vaccine that confers “leaky” protection would be less effective in areas of high risk than one that confers “all-or-none” protection. However, the difference in the effectiveness of mass vaccination using the leaky or all-or-none assumption was minor.Using an individual-based model of dengue transmission, we explored how different assumptions about the movement and spatial distribution of the vector can affect dengue incidence in humans. We found that the tendency for the most important vector of dengue, A. aegypti are known to breed in small containers associated with households, which may lead to spatial clustering at the household level Households may be the primary venue for dengue infection in humans. A. aegypti. Perifocal spraying measures typically target a 100 meter radius around detected cases of dengue fever. Studies have found that the risk of dengue infection was significantly higher for children living within 100 meters of another infected child than in control clusters Dengue outbreaks often occur in small spatial clusters, probably because of the short flight range of A recent modeling study found that the fine-scale spatial distribution of mosquitoes could affect dengue transmission, since some houses had a superabundance of mosquitoes Human movement probably plays a role in the spatial spread of dengue. A recent study demonstrated that visiting households with dengue-infected individuals was associated with an increased risk of infection We found that the incidence of dengue in the model was highly sensitive to the parameters associated with transmissibility but less so to the size of the mosquito population, which is consistent with simple deterministic compartmental models of vector-borne pathogens Keeping the limitations of our mathematical model in mind, we can draw a few general conclusions. Although fine-grained spatial heterogeneity in the mosquito population likely has significant effects on dengue transmission within individual households, the ability of mosquitoes, and people, to move reduces the effects of these heterogeneities. Focal interventions, such as perifocal spraying, may require careful and fine-grained spatial modeling