Disseminated paracoccidioidomycosis diagnosis based on oral lesions

Directory of Open Access Journals (Sweden)

Liana Preto Webber

2014-01-01

Full Text Available Paracoccidioidomycosis (PCM is a deep mycosis with primary lung manifestations that may present cutaneous and oral lesions. Oral lesions mimic other infectious diseases or even squamous cell carcinoma, clinically and microscopically. Sometimes, the dentist is the first to detect the disease, because lung lesions are asymptomatic, or even misdiagnosed. An unusual case of PCM with 5 months of evolution presenting pulmonary, oral, and cutaneous lesions that was diagnosed by the dentist based on oral lesions is presented and discussed.

Voting-based consensus clustering for combining multiple clusterings of chemical structures

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Saeed Faisal

2012-12-01

Full Text Available Abstract Background Although many consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics, few consensus clustering methods have been applied for combining multiple clusterings of chemical structures. It is known that any individual clustering method will not always give the best results for all types of applications. So, in this paper, three voting and graph-based consensus clusterings were used for combining multiple clusterings of chemical structures to enhance the ability of separating biologically active molecules from inactive ones in each cluster. Results The cumulative voting-based aggregation algorithm (CVAA, cluster-based similarity partitioning algorithm (CSPA and hyper-graph partitioning algorithm (HGPA were examined. The F-measure and Quality Partition Index method (QPI were used to evaluate the clusterings and the results were compared to the Ward’s clustering method. The MDL Drug Data Report (MDDR dataset was used for experiments and was represented by two 2D fingerprints, ALOGP and ECFP_4. The performance of voting-based consensus clustering method outperformed the Ward’s method using F-measure and QPI method for both ALOGP and ECFP_4 fingerprints, while the graph-based consensus clustering methods outperformed the Ward’s method only for ALOGP using QPI. The Jaccard and Euclidean distance measures were the methods of choice to generate the ensembles, which give the highest values for both criteria. Conclusions The results of the experiments show that consensus clustering methods can improve the effectiveness of chemical structures clusterings. The cumulative voting-based aggregation algorithm (CVAA was the method of choice among consensus clustering methods.

Text Clustering Algorithm Based on Random Cluster Core

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Huang Long-Jun

2016-01-01

Full Text Available Nowadays clustering has become a popular text mining algorithm, but the huge data can put forward higher requirements for the accuracy and performance of text mining. In view of the performance bottleneck of traditional text clustering algorithm, this paper proposes a text clustering algorithm with random features. This is a kind of clustering algorithm based on text density, at the same time using the neighboring heuristic rules, the concept of random cluster is introduced, which effectively reduces the complexity of the distance calculation.

Normalization based K means Clustering Algorithm

OpenAIRE

Virmani, Deepali; Taneja, Shweta; Malhotra, Geetika

2015-01-01

K-means is an effective clustering technique used to separate similar data into groups based on initial centroids of clusters. In this paper, Normalization based K-means clustering algorithm(N-K means) is proposed. Proposed N-K means clustering algorithm applies normalization prior to clustering on the available data as well as the proposed approach calculates initial centroids based on weights. Experimental results prove the betterment of proposed N-K means clustering algorithm over existing...

Progressive Exponential Clustering-Based Steganography

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Li Yue

2010-01-01

Full Text Available Cluster indexing-based steganography is an important branch of data-hiding techniques. Such schemes normally achieve good balance between high embedding capacity and low embedding distortion. However, most cluster indexing-based steganographic schemes utilise less efficient clustering algorithms for embedding data, which causes redundancy and leaves room for increasing the embedding capacity further. In this paper, a new clustering algorithm, called progressive exponential clustering (PEC, is applied to increase the embedding capacity by avoiding redundancy. Meanwhile, a cluster expansion algorithm is also developed in order to further increase the capacity without sacrificing imperceptibility.

Scalable Density-Based Subspace Clustering

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

2011-01-01

For knowledge discovery in high dimensional databases, subspace clustering detects clusters in arbitrary subspace projections. Scalability is a crucial issue, as the number of possible projections is exponential in the number of dimensions. We propose a scalable density-based subspace clustering...... method that steers mining to few selected subspace clusters. Our novel steering technique reduces subspace processing by identifying and clustering promising subspaces and their combinations directly. Thereby, it narrows down the search space while maintaining accuracy. Thorough experiments on real...... and synthetic databases show that steering is efficient and scalable, with high quality results. For future work, our steering paradigm for density-based subspace clustering opens research potential for speeding up other subspace clustering approaches as well....

Segmentation of dermatoscopic images by frequency domain filtering and k-means clustering algorithms.

Science.gov (United States)

Rajab, Maher I

2011-11-01

Since the introduction of epiluminescence microscopy (ELM), image analysis tools have been extended to the field of dermatology, in an attempt to algorithmically reproduce clinical evaluation. Accurate image segmentation of skin lesions is one of the key steps for useful, early and non-invasive diagnosis of coetaneous melanomas. This paper proposes two image segmentation algorithms based on frequency domain processing and k-means clustering/fuzzy k-means clustering. The two methods are capable of segmenting and extracting the true border that reveals the global structure irregularity (indentations and protrusions), which may suggest excessive cell growth or regression of a melanoma. As a pre-processing step, Fourier low-pass filtering is applied to reduce the surrounding noise in a skin lesion image. A quantitative comparison of the techniques is enabled by the use of synthetic skin lesion images that model lesions covered with hair to which Gaussian noise is added. The proposed techniques are also compared with an established optimal-based thresholding skin-segmentation method. It is demonstrated that for lesions with a range of different border irregularity properties, the k-means clustering and fuzzy k-means clustering segmentation methods provide the best performance over a range of signal to noise ratios. The proposed segmentation techniques are also demonstrated to have similar performance when tested on real skin lesions representing high-resolution ELM images. This study suggests that the segmentation results obtained using a combination of low-pass frequency filtering and k-means or fuzzy k-means clustering are superior to the result that would be obtained by using k-means or fuzzy k-means clustering segmentation methods alone. © 2011 John Wiley & Sons A/S.

Fast semi-automated lesion demarcation in stroke

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Bianca de Haan

2015-01-01

Full Text Available Lesion–behaviour mapping analyses require the demarcation of the brain lesion on each (usually transverse slice of the individual stroke patient's brain image. To date, this is generally thought to be most precise when done manually, which is, however, both time-consuming and potentially observer-dependent. Fully automated lesion demarcation methods have been developed to address these issues, but these are often not practicable in acute stroke research where for each patient only a single image modality is available and the available image modality differs over patients. In the current study, we evaluated a semi-automated lesion demarcation approach, the so-called Clusterize algorithm, in acute stroke patients scanned in a range of common image modalities. Our results suggest that, compared to the standard of manual lesion demarcation, the semi-automated Clusterize algorithm is capable of significantly speeding up lesion demarcation in the most commonly used image modalities, without loss of either lesion demarcation precision or lesion demarcation reproducibility. For the three investigated acute datasets (CT, DWI, T2FLAIR, containing a total of 44 patient images obtained in a regular clinical setting at patient admission, the reduction in processing time was on average 17.8 min per patient and this advantage increased with increasing lesion volume (up to 60 min per patient for the largest lesion volumes in our datasets. Additionally, our results suggest that performance of the Clusterize algorithm in a chronic dataset with 11 T1 images was comparable to its performance in the acute datasets. We thus advocate the use of the Clusterize algorithm, integrated into a simple, freely available SPM toolbox, for the precise, reliable and fast preparation of imaging data for lesion–behaviour mapping analyses.

Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

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Wang, Huiya; Feng, Jun; Wang, Hongyu

2017-07-20

Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues. From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively. The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate.

Membership determination of open clusters based on a spectral clustering method

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Gao, Xin-Hua

2018-06-01

We present a spectral clustering (SC) method aimed at segregating reliable members of open clusters in multi-dimensional space. The SC method is a non-parametric clustering technique that performs cluster division using eigenvectors of the similarity matrix; no prior knowledge of the clusters is required. This method is more flexible in dealing with multi-dimensional data compared to other methods of membership determination. We use this method to segregate the cluster members of five open clusters (Hyades, Coma Ber, Pleiades, Praesepe, and NGC 188) in five-dimensional space; fairly clean cluster members are obtained. We find that the SC method can capture a small number of cluster members (weak signal) from a large number of field stars (heavy noise). Based on these cluster members, we compute the mean proper motions and distances for the Hyades, Coma Ber, Pleiades, and Praesepe clusters, and our results are in general quite consistent with the results derived by other authors. The test results indicate that the SC method is highly suitable for segregating cluster members of open clusters based on high-precision multi-dimensional astrometric data such as Gaia data.

Efficient clustering aggregation based on data fragments.

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Wu, Ou; Hu, Weiming; Maybank, Stephen J; Zhu, Mingliang; Li, Bing

2012-06-01

Clustering aggregation, known as clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a single better clustering. Existing clustering aggregation algorithms are applied directly to data points, in what is referred to as the point-based approach. The algorithms are inefficient if the number of data points is large. We define an efficient approach for clustering aggregation based on data fragments. In this fragment-based approach, a data fragment is any subset of the data that is not split by any of the clustering results. To establish the theoretical bases of the proposed approach, we prove that clustering aggregation can be performed directly on data fragments under two widely used goodness measures for clustering aggregation taken from the literature. Three new clustering aggregation algorithms are described. The experimental results obtained using several public data sets show that the new algorithms have lower computational complexity than three well-known existing point-based clustering aggregation algorithms (Agglomerative, Furthest, and LocalSearch); nevertheless, the new algorithms do not sacrifice the accuracy.

Early detection of breast cancer mass lesions by mammogram segmentation images based on texture features

International Nuclear Information System (INIS)

Mahmood, F.H.

2012-01-01

Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer.The calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years. This paper proposes a computer aided diagnostic system for the extraction of features like mass lesions in mammograms for early detection of breast cancer. The proposed technique is based on a four-step procedure: (a) the preprocessing of the image is done, (b) regions of interest (ROI) specification, (c) supervised segmentation method includes two to stages performed using the minimum distance (M D) criterion, and (d) feature extraction based on Gray level Co-occurrence matrices GLC M for the identification of mass lesions. The method suggested for the detection of mass lesions from mammogram image segmentation and analysis was tested over several images taken from A L-llwiya Hospital in Baghdad, Iraq.The proposed technique shows better results.

Robust MST-Based Clustering Algorithm.

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Liu, Qidong; Zhang, Ruisheng; Zhao, Zhili; Wang, Zhenghai; Jiao, Mengyao; Wang, Guangjing

2018-06-01

Minimax similarity stresses the connectedness of points via mediating elements rather than favoring high mutual similarity. The grouping principle yields superior clustering results when mining arbitrarily-shaped clusters in data. However, it is not robust against noises and outliers in the data. There are two main problems with the grouping principle: first, a single object that is far away from all other objects defines a separate cluster, and second, two connected clusters would be regarded as two parts of one cluster. In order to solve such problems, we propose robust minimum spanning tree (MST)-based clustering algorithm in this letter. First, we separate the connected objects by applying a density-based coarsening phase, resulting in a low-rank matrix in which the element denotes the supernode by combining a set of nodes. Then a greedy method is presented to partition those supernodes through working on the low-rank matrix. Instead of removing the longest edges from MST, our algorithm groups the data set based on the minimax similarity. Finally, the assignment of all data points can be achieved through their corresponding supernodes. Experimental results on many synthetic and real-world data sets show that our algorithm consistently outperforms compared clustering algorithms.

'Do not touch' lesions of the skull base

International Nuclear Information System (INIS)

Dobre, Mircea C.; Fischbein, Nancy

2014-01-01

Imaging of the skull base presents many challenges due to its anatomical complexity, numerous normal variants and lack of familiarity to many radiologists. As the skull base is a region which is not amenable to physical examination and as lesions of the skull base are generally difficult to biopsy and even more difficult to operate on, the radiologist plays a major role in directing patient management via accurate image interpretation. Knowledge of the skull base should not be limited to neuroradiologists and head and neck radiologists, however, as the central skull base is routinely included in the field of view when imaging the brain, cervical spine, or head and neck with computed tomography or magnetic resonance imaging, and hence, its nuances should be familiar to general radiologists as well. We herein review the imaging findings of a subcategory of lesions of the central skull base, the 'do not touch' lesions.

CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life

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Musaab Riyadh

2017-01-01

Full Text Available The rapid spreading of positioning devices leads to the generation of massive spatiotemporal trajectories data. In some scenarios, spatiotemporal data are received in stream manner. Clustering of stream data is beneficial for different applications such as traffic management and weather forecasting. In this article, an algorithm for Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life is proposed. The algorithm consists of two phases. There is the online phase where temporal micro clusters are used to store summarized spatiotemporal information for each group of similar segments. The clustering task in online phase is based on temporal micro cluster lifetime instead of time window technique which divides stream data into time bins and clusters each bin separately. For offline phase, a density based clustering approach is used to generate macro clusters depending on temporal micro clusters. The evaluation of the proposed algorithm on real data sets shows the efficiency and the effectiveness of the proposed algorithm and proved it is efficient alternative to time window technique.

BioCluster: Tool for Identification and Clustering of Enterobacteriaceae Based on Biochemical Data

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Ahmed Abdullah

2015-06-01

Full Text Available Presumptive identification of different Enterobacteriaceae species is routinely achieved based on biochemical properties. Traditional practice includes manual comparison of each biochemical property of the unknown sample with known reference samples and inference of its identity based on the maximum similarity pattern with the known samples. This process is labor-intensive, time-consuming, error-prone, and subjective. Therefore, automation of sorting and similarity in calculation would be advantageous. Here we present a MATLAB-based graphical user interface (GUI tool named BioCluster. This tool was designed for automated clustering and identification of Enterobacteriaceae based on biochemical test results. In this tool, we used two types of algorithms, i.e., traditional hierarchical clustering (HC and the Improved Hierarchical Clustering (IHC, a modified algorithm that was developed specifically for the clustering and identification of Enterobacteriaceae species. IHC takes into account the variability in result of 1–47 biochemical tests within this Enterobacteriaceae family. This tool also provides different options to optimize the clustering in a user-friendly way. Using computer-generated synthetic data and some real data, we have demonstrated that BioCluster has high accuracy in clustering and identifying enterobacterial species based on biochemical test data. This tool can be freely downloaded at http://microbialgen.du.ac.bd/biocluster/.

Projection-based curve clustering

International Nuclear Information System (INIS)

Auder, Benjamin; Fischer, Aurelie

2012-01-01

This paper focuses on unsupervised curve classification in the context of nuclear industry. At the Commissariat a l'Energie Atomique (CEA), Cadarache (France), the thermal-hydraulic computer code CATHARE is used to study the reliability of reactor vessels. The code inputs are physical parameters and the outputs are time evolution curves of a few other physical quantities. As the CATHARE code is quite complex and CPU time-consuming, it has to be approximated by a regression model. This regression process involves a clustering step. In the present paper, the CATHARE output curves are clustered using a k-means scheme, with a projection onto a lower dimensional space. We study the properties of the empirically optimal cluster centres found by the clustering method based on projections, compared with the 'true' ones. The choice of the projection basis is discussed, and an algorithm is implemented to select the best projection basis among a library of orthonormal bases. The approach is illustrated on a simulated example and then applied to the industrial problem. (authors)

Classical Music Clustering Based on Acoustic Features

OpenAIRE

Wang, Xindi; Haque, Syed Arefinul

2017-01-01

In this paper we cluster 330 classical music pieces collected from MusicNet database based on their musical note sequence. We use shingling and chord trajectory matrices to create signature for each music piece and performed spectral clustering to find the clusters. Based on different resolution, the output clusters distinctively indicate composition from different classical music era and different composing style of the musicians.

Cluster Based Text Classification Model

DEFF Research Database (Denmark)

Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock

2011-01-01

We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases...... the accuracy at the same time. The test example is classified using simpler and smaller model. The training examples in a particular cluster share the common vocabulary. At the time of clustering, we do not take into account the labels of the training examples. After the clusters have been created......, the classifier is trained on each cluster having reduced dimensionality and less number of examples. The experimental results show that the proposed model outperforms the existing classification models for the task of suspicious email detection and topic categorization on the Reuters-21578 and 20 Newsgroups...

Weighted voting-based consensus clustering for chemical structure databases

Science.gov (United States)

Saeed, Faisal; Ahmed, Ali; Shamsir, Mohd Shahir; Salim, Naomie

2014-06-01

The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF_4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures.

Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach

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Ayad Hendalianpour

2016-11-01

Full Text Available Clustering is absolutely useful information to explore data structures and has been employed in many places. It organizes a set of objects into similar groups called clusters, and the objects within one cluster are both highly similar and dissimilar with the objects in other clusters. The K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms are the most popular clustering algorithms for their easy implementation and fast work, but in some cases we cannot use these algorithms. Regarding this, in this paper, a hybrid model for customer clustering is presented that is applicable in five banks of Fars Province, Shiraz, Iran. In this way, the fuzzy relation among customers is defined by using their features described in linguistic and quantitative variables. As follows, the customers of banks are grouped according to K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms and the proposed Fuzzy Relation Clustering (FRC algorithm. The aim of this paper is to show how to choose the best clustering algorithms based on density-based clustering and present a new clustering algorithm for both crisp and fuzzy variables. Finally, we apply the proposed approach to five datasets of customer's segmentation in banks. The result of the FCR shows the accuracy and high performance of FRC compared other clustering methods.

Severity of post-stroke aphasia according to aphasia type and lesion location in Koreans.

Science.gov (United States)

Kang, Eun Kyoung; Sohn, Hae Min; Han, Moon-Ku; Kim, Won; Han, Tai Ryoon; Paik, Nam-Jong

2010-01-01

To determine the relations between post-stroke aphasia severity and aphasia type and lesion location, a retrospective review was undertaken using the medical records of 97 Korean patients, treated within 90 days of onset, for aphasia caused by unilateral left hemispheric stroke. Types of aphasia were classified according to the validated Korean version of the Western Aphasia Battery (K-WAB), and severities of aphasia were quantified using WAB Aphasia Quotients (AQ). Lesion locations were classified as cortical or subcortical, and were determined by magnetic resonance imaging. Two-step cluster analysis was performed using AQ values to classify aphasia severity by aphasia type and lesion location. Cluster analysis resulted in four severity clusters: 1) mild; anomic type, 2) moderate; Wernicke's, transcortical motor, transcortical sensory, conduction, and mixed transcortical types, 3) moderately severe; Broca's aphasia, and 4) severe; global aphasia, and also in three lesion location clusters: 1) mild; subcortical 2) moderate; cortical lesions involving Broca's and/or Wernicke's areas, and 3) severe; insular and cortical lesions not in Broca's or Wernicke's areas. These results revealed that within 3 months of stroke, global aphasia was the more severely affected type and cortical lesions were more likely to affect language function than subcortical lesions.

A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.

Science.gov (United States)

Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing

2017-01-01

An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.

Segmentation of Brain Lesions in MRI and CT Scan Images: A Hybrid Approach Using k-Means Clustering and Image Morphology

Science.gov (United States)

Agrawal, Ritu; Sharma, Manisha; Singh, Bikesh Kumar

2018-04-01

Manual segmentation and analysis of lesions in medical images is time consuming and subjected to human errors. Automated segmentation has thus gained significant attention in recent years. This article presents a hybrid approach for brain lesion segmentation in different imaging modalities by combining median filter, k means clustering, Sobel edge detection and morphological operations. Median filter is an essential pre-processing step and is used to remove impulsive noise from the acquired brain images followed by k-means segmentation, Sobel edge detection and morphological processing. The performance of proposed automated system is tested on standard datasets using performance measures such as segmentation accuracy and execution time. The proposed method achieves a high accuracy of 94% when compared with manual delineation performed by an expert radiologist. Furthermore, the statistical significance test between lesion segmented using automated approach and that by expert delineation using ANOVA and correlation coefficient achieved high significance values of 0.986 and 1 respectively. The experimental results obtained are discussed in lieu of some recently reported studies.

Structure based alignment and clustering of proteins (STRALCP)

Science.gov (United States)

Zemla, Adam T.; Zhou, Carol E.; Smith, Jason R.; Lam, Marisa W.

2013-06-18

Disclosed are computational methods of clustering a set of protein structures based on local and pair-wise global similarity values. Pair-wise local and global similarity values are generated based on pair-wise structural alignments for each protein in the set of protein structures. Initially, the protein structures are clustered based on pair-wise local similarity values. The protein structures are then clustered based on pair-wise global similarity values. For each given cluster both a representative structure and spans of conserved residues are identified. The representative protein structure is used to assign newly-solved protein structures to a group. The spans are used to characterize conservation and assign a "structural footprint" to the cluster.

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

Science.gov (United States)

Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

2018-02-01

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

An object-based approach for detecting small brain lesions: application to Virchow-Robin spaces.

Science.gov (United States)

Descombes, Xavier; Kruggel, Frithjof; Wollny, Gert; Gertz, Hermann Josef

2004-02-01

This paper is concerned with the detection of multiple small brain lesions from magnetic resonance imaging (MRI) data. A model based on the marked point process framework is designed to detect Virchow-Robin spaces (VRSs). These tubular shaped spaces are due to retraction of the brain parenchyma from its supplying arteries. VRS are described by simple geometrical objects that are introduced as small tubular structures. Their radiometric properties are embedded in a data term. A prior model includes interactions describing the clustering property of VRS. A Reversible Jump Markov Chain Monte Carlo algorithm (RJMCMC) optimizes the proposed model, obtained by multiplying the prior and the data model. Example results are shown on T1-weighted MRI datasets of elderly subjects.

Relevance of brain lesion location to cognition in relapsing multiple sclerosis.

Directory of Open Access Journals (Sweden)

Francesca Rossi

Full Text Available OBJECTIVE: To assess the relationship between cognition and brain white matter (WM lesion distribution and frequency in patients with relapsing-remitting multiple sclerosis (RR MS. METHODS: MRI-based T2 lesion probability map (LPM was used to assess the relevance of brain lesion location for cognitive impairment in a group of 142 consecutive patients with RRMS. Significance of voxelwise analyses was p<0.05, cluster-corrected for multiple comparisons. The Rao Brief Repeatable Battery was administered at the time of brain MRI to categorize the MS population into cognitively preserved (CP and cognitively impaired (CI. RESULTS: Out of 142 RRMS, 106 were classified as CP and 36 as CI. Although the CI group had greater WM lesion volume than the CP group (p = 0.001, T2 lesions tended to be less widespread across the WM. The peak of lesion frequency was almost twice higher in CI (61% in the forceps major than in CP patients (37% in the posterior corona radiata. The voxelwise analysis confirmed that lesion frequency was higher in CI than in CP patients with significant bilateral clusters in the forceps major and in the splenium of the corpus callosum (p<0.05, corrected. Low scores of the Symbol Digit Modalities Test correlated with higher lesion frequency in these WM regions. CONCLUSIONS: Overall these results suggest that in MS patients, areas relevant for cognition lie mostly in the commissural fiber tracts. This supports the notion of a functional (multiple disconnection between grey matter structures, secondary to damage located in specific WM areas, as one of the most important mechanisms leading to cognitive impairment in MS.

The yield, processing, and biological consequences of clustered DNA damage induced by ionizing radiation

International Nuclear Information System (INIS)

Shikazono, Naoya; Noguchi, Miho; Fujii, Kentaro; Urushibara, Ayumi; Yokoya, Akinari

2009-01-01

After living cells are exposed to ionizing radiation, a variety of chemical modifications of DNA are induced either directly by ionization of DNA or indirectly through interactions with water-derived radicals. The DNA lesions include single strand breaks (SSB), base lesions, sugar damage, and apurinic/apyrimidinic sites (AP sites). Clustered DNA damage, which is defined as two or more of such lesions within one to two helical turns of DNA induced by a single radiation track, is considered to be a unique feature of ionizing radiation. A double strand break (DSB) is a type of clustered DNA damage, in which single strand breaks are formed on opposite strands in close proximity. Formation and repair of DSBs have been studied in great detail over the years as they have been linked to important biological endpoints, such as cell death, loss of genetic material, chromosome aberration. Although non-DSB clustered DNA damage has received less attention, there is growing evidence of its biological significance. This review focuses on the current understanding of (1) the yield of non-DSB clustered damage induced by ionizing radiation (2) the processing, and (3) biological consequences of non-DSB clustered DNA damage. (author)

Cluster-based global firms' use of local capabilities

DEFF Research Database (Denmark)

Andersen, Poul Houman; Bøllingtoft, Anne

2011-01-01

Purpose – Despite growing interest in clusters role for the global competitiveness of firms, there has been little research into how globalization affects cluster-based firms’ (CBFs) use of local knowledge resources and the combination of local and global knowledge used. Using the cluster......’s knowledge base as a mediating variable, the purpose of this paper is to examine how globalization affected the studied firms’ use of local cluster-based knowledge, integration of local and global knowledge, and networking capabilities. Design/methodology/approach – Qualitative case studies of nine firms...... in three clusters strongly affected by increasing global division of labour. Findings – The paper suggests that globalization has affected how firms use local resources and combine local and global knowledge. Unexpectedly, clustered firms with explicit procedures and established global fora for exchanging...

Location of brain lesions predicts conversion of clinically isolated syndromes to multiple sclerosis

DEFF Research Database (Denmark)

Giorgio, Antonio; Battaglini, Marco; Rocca, Maria Assunta

2013-01-01

OBJECTIVES: To assess in a large population of patients with clinically isolated syndrome (CIS) the relevance of brain lesion location and frequency in predicting 1-year conversion to multiple sclerosis (MS). METHODS: In this multicenter, retrospective study, clinical and MRI data at onset......: In CIS patients with hemispheric, multifocal, and brainstem/cerebellar onset, lesion probability map clusters were seen in clinically eloquent brain regions. Significant lesion clusters were not found in CIS patients with optic nerve and spinal cord onset. At 1 year, clinically definite MS developed...... in the converting group in projection, association, and commissural WM tracts, with larger clusters being in the corpus callosum, corona radiata, and cingulum. CONCLUSIONS: Higher frequency of lesion occurrence in clinically eloquent WM tracts can characterize CIS subjects with different types of onset...

Novel techniques for enhancement and segmentation of acne vulgaris lesions.

Science.gov (United States)

Malik, A S; Humayun, J; Kamel, N; Yap, F B-B

2014-08-01

More than 99% acne patients suffer from acne vulgaris. While diagnosing the severity of acne vulgaris lesions, dermatologists have observed inter-rater and intra-rater variability in diagnosis results. This is because during assessment, identifying lesion types and their counting is a tedious job for dermatologists. To make the assessment job objective and easier for dermatologists, an automated system based on image processing methods is proposed in this study. There are two main objectives: (i) to develop an algorithm for the enhancement of various acne vulgaris lesions; and (ii) to develop a method for the segmentation of enhanced acne vulgaris lesions. For the first objective, an algorithm is developed based on the theory of high dynamic range (HDR) images. The proposed algorithm uses local rank transform to generate the HDR images from a single acne image followed by the log transformation. Then, segmentation is performed by clustering the pixels based on Mahalanobis distance of each pixel from spectral models of acne vulgaris lesions. Two metrics are used to evaluate the enhancement of acne vulgaris lesions, i.e., contrast improvement factor (CIF) and image contrast normalization (ICN). The proposed algorithm is compared with two other methods. The proposed enhancement algorithm shows better result than both the other methods based on CIF and ICN. In addition, sensitivity and specificity are calculated for the segmentation results. The proposed segmentation method shows higher sensitivity and specificity than other methods. This article specifically discusses the contrast enhancement and segmentation for automated diagnosis system of acne vulgaris lesions. The results are promising that can be used for further classification of acne vulgaris lesions for final grading of the lesions. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Characterizing Heterogeneity within Head and Neck Lesions Using Cluster Analysis of Multi-Parametric MRI Data.

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Marco Borri

Full Text Available To describe a methodology, based on cluster analysis, to partition multi-parametric functional imaging data into groups (or clusters of similar functional characteristics, with the aim of characterizing functional heterogeneity within head and neck tumour volumes. To evaluate the performance of the proposed approach on a set of longitudinal MRI data, analysing the evolution of the obtained sub-sets with treatment.The cluster analysis workflow was applied to a combination of dynamic contrast-enhanced and diffusion-weighted imaging MRI data from a cohort of squamous cell carcinoma of the head and neck patients. Cumulative distributions of voxels, containing pre and post-treatment data and including both primary tumours and lymph nodes, were partitioned into k clusters (k = 2, 3 or 4. Principal component analysis and cluster validation were employed to investigate data composition and to independently determine the optimal number of clusters. The evolution of the resulting sub-regions with induction chemotherapy treatment was assessed relative to the number of clusters.The clustering algorithm was able to separate clusters which significantly reduced in voxel number following induction chemotherapy from clusters with a non-significant reduction. Partitioning with the optimal number of clusters (k = 4, determined with cluster validation, produced the best separation between reducing and non-reducing clusters.The proposed methodology was able to identify tumour sub-regions with distinct functional properties, independently separating clusters which were affected differently by treatment. This work demonstrates that unsupervised cluster analysis, with no prior knowledge of the data, can be employed to provide a multi-parametric characterization of functional heterogeneity within tumour volumes.

blockcluster: An R Package for Model-Based Co-Clustering

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Parmeet Singh Bhatia

2017-02-01

Full Text Available Simultaneous clustering of rows and columns, usually designated by bi-clustering, coclustering or block clustering, is an important technique in two way data analysis. A new standard and efficient approach has been recently proposed based on the latent block model (Govaert and Nadif 2003 which takes into account the block clustering problem on both the individual and variable sets. This article presents our R package blockcluster for co-clustering of binary, contingency and continuous data based on these very models. In this document, we will give a brief review of the model-based block clustering methods, and we will show how the R package blockcluster can be used for co-clustering.

A model of photon cell killing based on the spatio-temporal clustering of DNA damage in higher order chromatin structures.

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Lisa Herr

Full Text Available We present a new approach to model dose rate effects on cell killing after photon radiation based on the spatio-temporal clustering of DNA double strand breaks (DSBs within higher order chromatin structures of approximately 1-2 Mbp size, so called giant loops. The main concept of this approach consists of a distinction of two classes of lesions, isolated and clustered DSBs, characterized by the number of double strand breaks induced in a giant loop. We assume a low lethality and fast component of repair for isolated DSBs and a high lethality and slow component of repair for clustered DSBs. With appropriate rates, the temporal transition between the different lesion classes is expressed in terms of five differential equations. These allow formulating the dynamics involved in the competition of damage induction and repair for arbitrary dose rates and fractionation schemes. Final cell survival probabilities are computable with a cell line specific set of three parameters: The lethality for isolated DSBs, the lethality for clustered DSBs and the half-life time of isolated DSBs. By comparison with larger sets of published experimental data it is demonstrated that the model describes the cell line dependent response to treatments using either continuous irradiation at a constant dose rate or to split dose irradiation well. Furthermore, an analytic investigation of the formulation concerning single fraction treatments with constant dose rates in the limiting cases of extremely high or low dose rates is presented. The approach is consistent with the Linear-Quadratic model extended by the Lea-Catcheside factor up to the second moment in dose. Finally, it is shown that the model correctly predicts empirical findings about the dose rate dependence of incidence probabilities for deterministic radiation effects like pneumonitis and the bone marrow syndrome. These findings further support the general concepts on which the approach is based.

Cluster-based DBMS Management Tool with High-Availability

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Jae-Woo Chang

2005-02-01

Full Text Available A management tool which is needed for monitoring and managing cluster-based DBMSs has been little studied. So, we design and implement a cluster-based DBMS management tool with high-availability that monitors the status of nodes in a cluster system as well as the status of DBMS instances in a node. The tool enables users to recognize a single virtual system image and provides them with the status of all the nodes and resources in the system by using a graphic user interface (GUI. By using a load balancer, our management tool can increase the performance of a cluster-based DBMS as well as can overcome the limitation of the existing parallel DBMSs.

A Flocking Based algorithm for Document Clustering Analysis

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaohui [ORNL; Gao, Jinzhu [ORNL; Potok, Thomas E [ORNL

2006-01-01

Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses stochastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike other partition clustering algorithm such as K-means, the Flocking based algorithm does not require initial partitional seeds. The algorithm generates a clustering of a given set of data through the embedding of the high-dimensional data items on a two-dimensional grid for easy clustering result retrieval and visualization. Inspired by the self-organized behavior of bird flocks, we represent each document object with a flock boid. The simple local rules followed by each flock boid result in the entire document flock generating complex global behaviors, which eventually result in a clustering of the documents. We evaluate the efficiency of our algorithm with both a synthetic dataset and a real document collection that includes 100 news articles collected from the Internet. Our results show that the Flocking clustering algorithm achieves better performance compared to the K- means and the Ant clustering algorithm for real document clustering.

Betel quid oral lichenoid lesions: a hospital based cross-sectional study.

Science.gov (United States)

Arya, Sugandha; Vengal, Manoj; Raju, Bina; Patil, Neelkant; Sathosker, Sujatha; Bateja, Sumit; David, Jamil

2017-02-01

The aim of this study was to assess the prevalence and risk indicators of betel quid oral lichenoid lesions in chewers. A total of 1209 chewers were identified and categorized into three main groups based on the type of lesion: betel quid oral lichenoid lesions only, betel quid oral lichenoid lesions in association with quid-induced other oral mucosal lesions, and no lesions. Multinomial regression analyses were used to determine associations between dependent and independent variables. Betel quid oral lichenoid lesions were more common in individuals who chewed quid comprising both tobacco and areca nut, and in those who chewed it two to three, or greater than three, times a day. Betel quid oral lichenoid lesions + quid-induced other oral mucosal lesions were more likely to occur in females, and in individuals who chewed quid containing both tobacco and areca nut, in their processed and unprocessed forms, and greater than three times a day. The prevalence of betel quid oral lichenoid lesions was higher than that reported in previous studies conducted in India. Increase in the frequency and duration of quid chewing was associated with increased likelihood of developing these oral lichenoid lesions. © 2015 Wiley Publishing Asia Pty Ltd.

A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness Measures

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Shaoyi Liang

2017-09-01

Full Text Available Closeness measures are crucial to clustering methods. In most traditional clustering methods, the closeness between data points or clusters is measured by the geometric distance alone. These metrics quantify the closeness only based on the concerned data points’ positions in the feature space, and they might cause problems when dealing with clustering tasks having arbitrary clusters shapes and different clusters densities. In this paper, we first propose a novel Closeness Measure between data points based on the Neighborhood Chain (CMNC. Instead of using geometric distances alone, CMNC measures the closeness between data points by quantifying the difficulty for one data point to reach another through a chain of neighbors. Furthermore, based on CMNC, we also propose a clustering ensemble framework that combines CMNC and geometric-distance-based closeness measures together in order to utilize both of their advantages. In this framework, the “bad data points” that are hard to cluster correctly are identified; then different closeness measures are applied to different types of data points to get the unified clustering results. With the fusion of different closeness measures, the framework can get not only better clustering results in complicated clustering tasks, but also higher efficiency.

Novel density-based and hierarchical density-based clustering algorithms for uncertain data.

Science.gov (United States)

Zhang, Xianchao; Liu, Han; Zhang, Xiaotong

2017-09-01

Uncertain data has posed a great challenge to traditional clustering algorithms. Recently, several algorithms have been proposed for clustering uncertain data, and among them density-based techniques seem promising for handling data uncertainty. However, some issues like losing uncertain information, high time complexity and nonadaptive threshold have not been addressed well in the previous density-based algorithm FDBSCAN and hierarchical density-based algorithm FOPTICS. In this paper, we firstly propose a novel density-based algorithm PDBSCAN, which improves the previous FDBSCAN from the following aspects: (1) it employs a more accurate method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in FDBSCAN; (2) it introduces new definitions of probability neighborhood, support degree, core object probability, direct reachability probability, thus reducing the complexity and solving the issue of nonadaptive threshold (for core object judgement) in FDBSCAN. Then, we modify the algorithm PDBSCAN to an improved version (PDBSCANi), by using a better cluster assignment strategy to ensure that every object will be assigned to the most appropriate cluster, thus solving the issue of nonadaptive threshold (for direct density reachability judgement) in FDBSCAN. Furthermore, as PDBSCAN and PDBSCANi have difficulties for clustering uncertain data with non-uniform cluster density, we propose a novel hierarchical density-based algorithm POPTICS by extending the definitions of PDBSCAN, adding new definitions of fuzzy core distance and fuzzy reachability distance, and employing a new clustering framework. POPTICS can reveal the cluster structures of the datasets with different local densities in different regions better than PDBSCAN and PDBSCANi, and it addresses the issues in FOPTICS. Experimental results demonstrate the superiority of our proposed algorithms over the existing

White matter lesions in psychiatric patients: a retrospective MRI study

International Nuclear Information System (INIS)

Becker, T.; Teichmann, E.; Hofmann, E.; Schmidtke, A.; Warmuth-Metz, M.; Nadjmi, M.

1992-01-01

T2-weighted MRI scans of psychiatric patients with at least one white matter lesion (WML) were compared to 83 non-psychiatric controls with respect to WML number and distribution. MANOVA resulted in significant effects for sex, age and patient group with respect to WML number. In the psychiatric patients, infratentorial WML prevailed in organic psychoses. WML number was positively correlated with age with the exception of right temporal lobe WML. Based on WML spatial distribution, four patient clusters were found. Clusters with widely distributed WML comprised older patients with late onset of illness; right frontal and temporal WML were associated with mania, euphoria and unstable mood. (orig.)

Beyond Apprenticeship: Knowledge Brokers and Sustainability of Apprentice-Based Clusters

Directory of Open Access Journals (Sweden)

Huasheng Zhu

2016-12-01

Full Text Available Knowledge learning and diffusion have long been discussed in the literature on the dynamics of industrial clusters, but recent literature provides little evidence for how different actors serve as knowledge brokers in the upgrading process of apprentice-based clusters, and does not dynamically consider how to preserve the sustainability of these clusters. This paper uses empirical evidence from an antique furniture manufacturing cluster in Xianyou, Fujian Province, in southeastern China, to examine the growth trajectory of the knowledge learning system of an antique furniture manufacturing cluster. It appears that the apprentice-based learning system is crucial during early stages of the cluster evolution, but later becomes complemented and relatively substituted by the role of both local governments and focal outsiders. This finding addresses the context of economic transformation and provides empirical insights into knowledge acquisition in apprentice-based clusters to question the rationality based on European and North American cases, and to provide a broader perspective for policy makers to trigger and sustain the development of apprentice-based clusters.

An ensemble deep learning based approach for red lesion detection in fundus images.

Science.gov (United States)

Orlando, José Ignacio; Prokofyeva, Elena; Del Fresno, Mariana; Blaschko, Matthew B

2018-01-01

Diabetic retinopathy (DR) is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms (MAs) and hemorrhages (HEs). In daily clinical practice, these lesions are manually detected by physicians using fundus photographs. However, this task is tedious and time consuming, and requires an intensive effort due to the small size of the lesions and their lack of contrast. Computer-assisted diagnosis of DR based on red lesion detection is being actively explored due to its improvement effects both in clinicians consistency and accuracy. Moreover, it provides comprehensive feedback that is easy to assess by the physicians. Several methods for detecting red lesions have been proposed in the literature, most of them based on characterizing lesion candidates using hand crafted features, and classifying them into true or false positive detections. Deep learning based approaches, by contrast, are scarce in this domain due to the high expense of annotating the lesions manually. In this paper we propose a novel method for red lesion detection based on combining both deep learned and domain knowledge. Features learned by a convolutional neural network (CNN) are augmented by incorporating hand crafted features. Such ensemble vector of descriptors is used afterwards to identify true lesion candidates using a Random Forest classifier. We empirically observed that combining both sources of information significantly improve results with respect to using each approach separately. Furthermore, our method reported the highest performance on a per-lesion basis on DIARETDB1 and e-ophtha, and for screening and need for referral on MESSIDOR compared to a second human expert. Results highlight the fact that integrating manually engineered approaches with deep learned features is relevant to improve results when the networks are trained from lesion-level annotated data. An open source implementation of our

Cluster-based analysis of multi-model climate ensembles

Science.gov (United States)

Hyde, Richard; Hossaini, Ryan; Leeson, Amber A.

2018-06-01

Clustering - the automated grouping of similar data - can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model-observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry-climate model (CCM) output of tropospheric ozone - an important greenhouse gas - from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to ˜ 20 % in the global absolute mean bias between the MMM and an observed satellite-based tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at ˜ 62 % of all locations, with the largest bias reductions occurring in the Northern Hemisphere - where ozone concentrations are relatively large. However, the bias is unchanged at 9 % of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and

Managing distance and covariate information with point-based clustering

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Peter A. Whigham

2016-09-01

Full Text Available Abstract Background Geographic perspectives of disease and the human condition often involve point-based observations and questions of clustering or dispersion within a spatial context. These problems involve a finite set of point observations and are constrained by a larger, but finite, set of locations where the observations could occur. Developing a rigorous method for pattern analysis in this context requires handling spatial covariates, a method for constrained finite spatial clustering, and addressing bias in geographic distance measures. An approach, based on Ripley’s K and applied to the problem of clustering with deliberate self-harm (DSH, is presented. Methods Point-based Monte-Carlo simulation of Ripley’s K, accounting for socio-economic deprivation and sources of distance measurement bias, was developed to estimate clustering of DSH at a range of spatial scales. A rotated Minkowski L1 distance metric allowed variation in physical distance and clustering to be assessed. Self-harm data was derived from an audit of 2 years’ emergency hospital presentations (n = 136 in a New Zealand town (population ~50,000. Study area was defined by residential (housing land parcels representing a finite set of possible point addresses. Results Area-based deprivation was spatially correlated. Accounting for deprivation and distance bias showed evidence for clustering of DSH for spatial scales up to 500 m with a one-sided 95 % CI, suggesting that social contagion may be present for this urban cohort. Conclusions Many problems involve finite locations in geographic space that require estimates of distance-based clustering at many scales. A Monte-Carlo approach to Ripley’s K, incorporating covariates and models for distance bias, are crucial when assessing health-related clustering. The case study showed that social network structure defined at the neighbourhood level may account for aspects of neighbourhood clustering of DSH. Accounting for

Lung lesion doubling times: values and variability based on method of volume determination

International Nuclear Information System (INIS)

Eisenbud Quint, Leslie; Cheng, Joan; Schipper, Matthew; Chang, Andrew C.; Kalemkerian, Gregory

2008-01-01

Purpose: To determine doubling times (DTs) of lung lesions based on volumetric measurements from thin-section CT imaging. Methods: Previously untreated patients with ≥ two thin-section CT scans showing a focal lung lesion were identified. Lesion volumes were derived using direct volume measurements and volume calculations based on lesion area and diameter. Growth rates (GRs) were compared by tissue diagnosis and measurement technique. Results: 54 lesions were evaluated including 8 benign lesions, 10 metastases, 3 lymphomas, 15 adenocarcinomas, 11 squamous carcinomas, and 7 miscellaneous lung cancers. Using direct volume measurements, median DTs were 453, 111, 15, 181, 139 and 137 days, respectively. Lung cancer DTs ranged from 23-2239 days. There were no significant differences in GRs among the different lesion types. There was considerable variability among GRs using different volume determination methods. Conclusions: Lung cancer doubling times showed a substantial range, and different volume determination methods gave considerably different DTs

Cluster-based Data Gathering in Long-Strip Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

FANG, W.

2012-02-01

Full Text Available This paper investigates a special class of wireless sensor networks that are different from traditional ones in that the sensor nodes in this class of networks are deployed along narrowly elongated geographical areas and form a long-strip topology. According to hardware capabilities of current sensor nodes, a cluster-based protocol for reliable and efficient data gathering in long-strip wireless sensor networks (LSWSN is proposed. A well-distributed cluster-based architecture is first formed in the whole network through contention-based cluster head election. Cluster heads are responsible for coordination among the nodes within their clusters and aggregation of their sensory data, as well as transmission the data to the sink node on behalf of their own clusters. The intra-cluster coordination is based on the traditional TDMA schedule, in which the inter-cluster interference caused by the border nodes is solved by the multi-channel communication technique. The cluster reporting is based on the CSMA contention, in which a connected overlay network is formed by relay nodes to forward the data from the cluster heads through multi-hops to the sink node. The relay nodes are non-uniformly deployed to resolve the energy-hole problem which is extremely serious in the LSWSN. Extensive simulation results illuminate the distinguished performance of the proposed protocol.

CORECLUSTER: A Degeneracy Based Graph Clustering Framework

OpenAIRE

Giatsidis , Christos; Malliaros , Fragkiskos; Thilikos , Dimitrios M. ,; Vazirgiannis , Michalis

2014-01-01

International audience; Graph clustering or community detection constitutes an important task forinvestigating the internal structure of graphs, with a plethora of applications in several domains. Traditional tools for graph clustering, such asspectral methods, typically suffer from high time and space complexity. In thisarticle, we present \\textsc{CoreCluster}, an efficient graph clusteringframework based on the concept of graph degeneracy, that can be used along withany known graph clusteri...

Flowbca : A flow-based cluster algorithm in Stata

NARCIS (Netherlands)

Meekes, J.; Hassink, W.H.J.

In this article, we introduce the Stata implementation of a flow-based cluster algorithm written in Mata. The main purpose of the flowbca command is to identify clusters based on relational data of flows. We illustrate the command by providing multiple applications, from the research fields of

CBHRP: A Cluster Based Routing Protocol for Wireless Sensor Network

OpenAIRE

Rashed, M. G.; Kabir, M. Hasnat; Rahim, M. Sajjadur; Ullah, Sk. Enayet

2012-01-01

A new two layer hierarchical routing protocol called Cluster Based Hierarchical Routing Protocol (CBHRP) is proposed in this paper. It is an extension of LEACH routing protocol. We introduce cluster head-set idea for cluster-based routing where several clusters are formed with the deployed sensors to collect information from target field. On rotation basis, a head-set member receives data from the neighbor nodes and transmits the aggregated results to the distance base station. This protocol ...

Canonical PSO Based K-Means Clustering Approach for Real Datasets.

Science.gov (United States)

Dey, Lopamudra; Chakraborty, Sanjay

2014-01-01

"Clustering" the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different types of indexes are used to solve different types of problems and indices selection depends on the kind of available data. This paper first proposes Canonical PSO based K-means clustering algorithm and also analyses some important clustering indices (intercluster, intracluster) and then evaluates the effects of those indices on real-time air pollution database, wholesale customer, wine, and vehicle datasets using typical K-means, Canonical PSO based K-means, simple PSO based K-means, DBSCAN, and Hierarchical clustering algorithms. This paper also describes the nature of the clusters and finally compares the performances of these clustering algorithms according to the validity assessment. It also defines which algorithm will be more desirable among all these algorithms to make proper compact clusters on this particular real life datasets. It actually deals with the behaviour of these clustering algorithms with respect to validation indexes and represents their results of evaluation in terms of mathematical and graphical forms.

How distributed processing produces false negatives in voxel-based lesion-deficit analyses.

Science.gov (United States)

Gajardo-Vidal, Andrea; Lorca-Puls, Diego L; Crinion, Jennifer T; White, Jitrachote; Seghier, Mohamed L; Leff, Alex P; Hope, Thomas M H; Ludersdorfer, Philipp; Green, David W; Bowman, Howard; Price, Cathy J

2018-07-01

In this study, we hypothesized that if the same deficit can be caused by damage to one or another part of a distributed neural system, then voxel-based analyses might miss critical lesion sites because preservation of each site will not be consistently associated with preserved function. The first part of our investigation used voxel-based multiple regression analyses of data from 359 right-handed stroke survivors to identify brain regions where lesion load is associated with picture naming abilities after factoring out variance related to object recognition, semantics and speech articulation so as to focus on deficits arising at the word retrieval level. A highly significant lesion-deficit relationship was identified in left temporal and frontal/premotor regions. Post-hoc analyses showed that damage to either of these sites caused the deficit of interest in less than half the affected patients (76/162 = 47%). After excluding all patients with damage to one or both of the identified regions, our second analysis revealed a new region, in the anterior part of the left putamen, which had not been previously detected because many patients had the deficit of interest after temporal or frontal damage that preserved the left putamen. The results illustrate how (i) false negative results arise when the same deficit can be caused by different lesion sites; (ii) some of the missed effects can be unveiled by adopting an iterative approach that systematically excludes patients with lesions to the areas identified in previous analyses, (iii) statistically significant voxel-based lesion-deficit mappings can be driven by a subset of patients; (iv) focal lesions to the identified regions are needed to determine whether the deficit of interest is the consequence of focal damage or much more extensive damage that includes the identified region; and, finally, (v) univariate voxel-based lesion-deficit mappings cannot, in isolation, be used to predict outcome in other patients

APPECT: An Approximate Backbone-Based Clustering Algorithm for Tags

DEFF Research Database (Denmark)

Zong, Yu; Xu, Guandong; Jin, Pin

2011-01-01

algorithm for Tags (APPECT). The main steps of APPECT are: (1) we execute the K-means algorithm on a tag similarity matrix for M times and collect a set of tag clustering results Z={C1,C2,…,Cm}; (2) we form the approximate backbone of Z by executing a greedy search; (3) we fix the approximate backbone...... as the initial tag clustering result and then assign the rest tags into the corresponding clusters based on the similarity. Experimental results on three real world datasets namely MedWorm, MovieLens and Dmoz demonstrate the effectiveness and the superiority of the proposed method against the traditional...... Agglomerative Clustering on tagging data, which possess the inherent drawbacks, such as the sensitivity of initialization. In this paper, we instead make use of the approximate backbone of tag clustering results to find out better tag clusters. In particular, we propose an APProximate backbonE-based Clustering...

Cost/Performance Ratio Achieved by Using a Commodity-Based Cluster

Science.gov (United States)

Lopez, Isaac

2001-01-01

Researchers at the NASA Glenn Research Center acquired a commodity cluster based on Intel Corporation processors to compare its performance with a traditional UNIX cluster in the execution of aeropropulsion applications. Since the cost differential of the clusters was significant, a cost/performance ratio was calculated. After executing a propulsion application on both clusters, the researchers demonstrated a 9.4 cost/performance ratio in favor of the Intel-based cluster. These researchers utilize the Aeroshark cluster as one of the primary testbeds for developing NPSS parallel application codes and system software. The Aero-shark cluster provides 64 Intel Pentium II 400-MHz processors, housed in 32 nodes. Recently, APNASA - a code developed by a Government/industry team for the design and analysis of turbomachinery systems was used for a simulation on Glenn's Aeroshark cluster.

Centroid based clustering of high throughput sequencing reads based on n-mer counts.

Science.gov (United States)

Solovyov, Alexander; Lipkin, W Ian

2013-09-08

Many problems in computational biology require alignment-free sequence comparisons. One of the common tasks involving sequence comparison is sequence clustering. Here we apply methods of alignment-free comparison (in particular, comparison using sequence composition) to the challenge of sequence clustering. We study several centroid based algorithms for clustering sequences based on word counts. Study of their performance shows that using k-means algorithm with or without the data whitening is efficient from the computational point of view. A higher clustering accuracy can be achieved using the soft expectation maximization method, whereby each sequence is attributed to each cluster with a specific probability. We implement an open source tool for alignment-free clustering. It is publicly available from github: https://github.com/luscinius/afcluster. We show the utility of alignment-free sequence clustering for high throughput sequencing analysis despite its limitations. In particular, it allows one to perform assembly with reduced resources and a minimal loss of quality. The major factor affecting performance of alignment-free read clustering is the length of the read.

CT-based needle marking of superficial intracranial lesions for minimal invasive neurosurgery

International Nuclear Information System (INIS)

Marquardt, G.; Wolff, R.; Schick, U.; Lorenz, R.

2000-01-01

A CT-based method of marking superficial intracranial lesions with a needle is presented. This form of neuronavigation can be applied in every neurosurgical centre. Owing to its rapid application it is also suitable for cases of emergency. The neurosurgical approach can be centred precisely over this lesion providing for a minimally invasive operation. The method has proved its efficacy in numerous cases of haematomas and cystic lesions. (author)

Three-dimensional spatiotemporal features for fast content-based retrieval of focal liver lesions.

Science.gov (United States)

Roy, Sharmili; Chi, Yanling; Liu, Jimin; Venkatesh, Sudhakar K; Brown, Michael S

2014-11-01

Content-based image retrieval systems for 3-D medical datasets still largely rely on 2-D image-based features extracted from a few representative slices of the image stack. Most 2 -D features that are currently used in the literature not only model a 3-D tumor incompletely but are also highly expensive in terms of computation time, especially for high-resolution datasets. Radiologist-specified semantic labels are sometimes used along with image-based 2-D features to improve the retrieval performance. Since radiological labels show large interuser variability, are often unstructured, and require user interaction, their use as lesion characterizing features is highly subjective, tedious, and slow. In this paper, we propose a 3-D image-based spatiotemporal feature extraction framework for fast content-based retrieval of focal liver lesions. All the features are computer generated and are extracted from four-phase abdominal CT images. Retrieval performance and query processing times for the proposed framework is evaluated on a database of 44 hepatic lesions comprising of five pathological types. Bull's eye percentage score above 85% is achieved for three out of the five lesion pathologies and for 98% of query lesions, at least one same type of lesion is ranked among the top two retrieved results. Experiments show that the proposed system's query processing is more than 20 times faster than other already published systems that use 2-D features. With fast computation time and high retrieval accuracy, the proposed system has the potential to be used as an assistant to radiologists for routine hepatic tumor diagnosis.

Cluster-based spectrum sensing for cognitive radios with imperfect channel to cluster-head

KAUST Repository

Ben Ghorbel, Mahdi

2012-04-01

Spectrum sensing is considered as the first and main step for cognitive radio systems to achieve an efficient use of spectrum. Cooperation and clustering among cognitive radio users are two techniques that can be employed with spectrum sensing in order to improve the sensing performance by reducing miss-detection and false alarm. In this paper, within the framework of a clustering-based cooperative spectrum sensing scheme, we study the effect of errors in transmitting the local decisions from the secondary users to the cluster heads (or the fusion center), while considering non-identical channel conditions between the secondary users. Closed-form expressions for the global probabilities of detection and false alarm at the cluster head are derived. © 2012 IEEE.

Cluster-based spectrum sensing for cognitive radios with imperfect channel to cluster-head

KAUST Repository

Ben Ghorbel, Mahdi; Nam, Haewoon; Alouini, Mohamed-Slim

2012-01-01

Spectrum sensing is considered as the first and main step for cognitive radio systems to achieve an efficient use of spectrum. Cooperation and clustering among cognitive radio users are two techniques that can be employed with spectrum sensing in order to improve the sensing performance by reducing miss-detection and false alarm. In this paper, within the framework of a clustering-based cooperative spectrum sensing scheme, we study the effect of errors in transmitting the local decisions from the secondary users to the cluster heads (or the fusion center), while considering non-identical channel conditions between the secondary users. Closed-form expressions for the global probabilities of detection and false alarm at the cluster head are derived. © 2012 IEEE.

Performance and role of the breast lesion excision system (BLES) in small clusters of suspicious microcalcifications.

Science.gov (United States)

Scaperrotta, Gianfranco; Ferranti, Claudio; Capalbo, Emanuela; Paolini, Biagio; Marchesini, Monica; Suman, Laura; Folini, Cristina; Mariani, Luigi; Panizza, Pietro

2016-01-01

To assess the diagnostic performance of the BLES as a biopsy tool in patients with ≤ 1 cm clusters of BIRADS 4 microcalcifications, in order to possibly avoid surgical excision in selected patients. This is a retrospective study of 105 patients undergone to stereotactic breast biopsy with the BLES. It excises a single specimen containing the whole mammographic target, allowing better histological assessment due to preserved architecture. Our case series consists of 41 carcinomas (39%) and 64 benign lesions (61%). Cancer involved the specimen margins in 20/41 cases (48.8%) or was close to them (≤ 1 mm) in 14 cases (34.1%); margins were disease-free in only 7 DCIS (17.1%). At subsequent excision of 39/41 malignant cases, underestimation occurred for 5/32 DCIS (15.6%), residual disease was found in 15/39 cancers (38.5%) and no cancer in 19/39 cases (48.7%). For DCIS cases, no residual disease occurred for 66.7% G1-G2 cases and for 35.3% G3 cases (P=0.1556) as well as in 83.3%, 40.0% and 43.8% cases respectively for negative, close and positive BLES margins (P=0.2576). The BLES is a good option for removal of small clusters of breast microcalcifications, giving better histological interpretation, lower underestimation rates and possibly reducing the need of subsequent surgical excision in selected patients. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Pulmonary parenchyma segmentation in thin CT image sequences with spectral clustering and geodesic active contour model based on similarity

Science.gov (United States)

He, Nana; Zhang, Xiaolong; Zhao, Juanjuan; Zhao, Huilan; Qiang, Yan

2017-07-01

While the popular thin layer scanning technology of spiral CT has helped to improve diagnoses of lung diseases, the large volumes of scanning images produced by the technology also dramatically increase the load of physicians in lesion detection. Computer-aided diagnosis techniques like lesions segmentation in thin CT sequences have been developed to address this issue, but it remains a challenge to achieve high segmentation efficiency and accuracy without much involvement of human manual intervention. In this paper, we present our research on automated segmentation of lung parenchyma with an improved geodesic active contour model that is geodesic active contour model based on similarity (GACBS). Combining spectral clustering algorithm based on Nystrom (SCN) with GACBS, this algorithm first extracts key image slices, then uses these slices to generate an initial contour of pulmonary parenchyma of un-segmented slices with an interpolation algorithm, and finally segments lung parenchyma of un-segmented slices. Experimental results show that the segmentation results generated by our method are close to what manual segmentation can produce, with an average volume overlap ratio of 91.48%.

Graph-based clustering and data visualization algorithms

CERN Document Server

Vathy-Fogarassy, Ágnes

2013-01-01

This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on

Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks.

Science.gov (United States)

Aadil, Farhan; Raza, Ali; Khan, Muhammad Fahad; Maqsood, Muazzam; Mehmood, Irfan; Rho, Seungmin

2018-05-03

Flying ad-hoc networks (FANETs) are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs) represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR) and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.

Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks

Directory of Open Access Journals (Sweden)

Farhan Aadil

2018-05-01

Full Text Available Flying ad-hoc networks (FANETs are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.

An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

Science.gov (United States)

Hu, Weiming; Li, Xi; Tian, Guodong; Maybank, Stephen; Zhang, Zhongfei

2013-05-01

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.

Nonuniform Sparse Data Clustering Cascade Algorithm Based on Dynamic Cumulative Entropy

Directory of Open Access Journals (Sweden)

Ning Li

2016-01-01

Full Text Available A small amount of prior knowledge and randomly chosen initial cluster centers have a direct impact on the accuracy of the performance of iterative clustering algorithm. In this paper we propose a new algorithm to compute initial cluster centers for k-means clustering and the best number of the clusters with little prior knowledge and optimize clustering result. It constructs the Euclidean distance control factor based on aggregation density sparse degree to select the initial cluster center of nonuniform sparse data and obtains initial data clusters by multidimensional diffusion density distribution. Multiobjective clustering approach based on dynamic cumulative entropy is adopted to optimize the initial data clusters and the best number of the clusters. The experimental results show that the newly proposed algorithm has good performance to obtain the initial cluster centers for the k-means algorithm and it effectively improves the clustering accuracy of nonuniform sparse data by about 5%.

Localization of occult breast lesions: mammographic findings in non infiltrating carcinoma; Localizacion de lesiones ocultas de mama: hallazgos mamograficos del carcinoma no infiltrante

Energy Technology Data Exchange (ETDEWEB)

Paul, L; Iribar, M; Vilarrasa, A; Lopez, M A; Hernandez, R; Carrasco, A [Departamento de Radiodiagnostico, Hospital Doce de Octubre, Madrid (Spain)

1995-08-01

A review was carried out of 169 biopsies of occult breast lesions, 32 of which were malignant. The radiological findings associated with in situ carcinomas were compared with those of malignant lesions in general. The signs that permit the detection of breast carcinoma in the earliest stages are described, and the positive predictive value of signs, such as a spicular form, clusters of microcalcifications, nodules and architectural disturbances, is discussed. Given the lack of specificity of these findings, many beginning lesions should be biopsied to ensure an early diagnosis of breast cancer. (Author)

Density-Based Clustering with Geographical Background Constraints Using a Semantic Expression Model

Directory of Open Access Journals (Sweden)

Qingyun Du

2016-05-01

Full Text Available A semantics-based method for density-based clustering with constraints imposed by geographical background knowledge is proposed. In this paper, we apply an ontological approach to the DBSCAN (Density-Based Geospatial Clustering of Applications with Noise algorithm in the form of knowledge representation for constraint clustering. When used in the process of clustering geographic information, semantic reasoning based on a defined ontology and its relationships is primarily intended to overcome the lack of knowledge of the relevant geospatial data. Better constraints on the geographical knowledge yield more reasonable clustering results. This article uses an ontology to describe the four types of semantic constraints for geographical backgrounds: “No Constraints”, “Constraints”, “Cannot-Link Constraints”, and “Must-Link Constraints”. This paper also reports the implementation of a prototype clustering program. Based on the proposed approach, DBSCAN can be applied with both obstacle and non-obstacle constraints as a semi-supervised clustering algorithm and the clustering results are displayed on a digital map.

Ontology-based topic clustering for online discussion data

Science.gov (United States)

Wang, Yongheng; Cao, Kening; Zhang, Xiaoming

2013-03-01

With the rapid development of online communities, mining and extracting quality knowledge from online discussions becomes very important for the industrial and marketing sector, as well as for e-commerce applications and government. Most of the existing techniques model a discussion as a social network of users represented by a user-based graph without considering the content of the discussion. In this paper we propose a new multilayered mode to analysis online discussions. The user-based and message-based representation is combined in this model. A novel frequent concept sets based clustering method is used to cluster the original online discussion network into topic space. Domain ontology is used to improve the clustering accuracy. Parallel methods are also used to make the algorithms scalable to very large data sets. Our experimental study shows that the model and algorithms are effective when analyzing large scale online discussion data.

Result Diversification Based on Query-Specific Cluster Ranking

NARCIS (Netherlands)

J. He (Jiyin); E. Meij; M. de Rijke (Maarten)

2011-01-01

htmlabstractResult diversification is a retrieval strategy for dealing with ambiguous or multi-faceted queries by providing documents that cover as many facets of the query as possible. We propose a result diversification framework based on query-specific clustering and cluster ranking,

Microgrids Real-Time Pricing Based on Clustering Techniques

Directory of Open Access Journals (Sweden)

Hao Liu

2018-05-01

Full Text Available Microgrids are widely spreading in electricity markets worldwide. Besides the security and reliability concerns for these microgrids, their operators need to address consumers’ pricing. Considering the growth of smart grids and smart meter facilities, it is expected that microgrids will have some level of flexibility to determine real-time pricing for at least some consumers. As such, the key challenge is finding an optimal pricing model for consumers. This paper, accordingly, proposes a new pricing scheme in which microgrids are able to deploy clustering techniques in order to understand their consumers’ load profiles and then assign real-time prices based on their load profile patterns. An improved weighted fuzzy average k-means is proposed to cluster load curve of consumers in an optimal number of clusters, through which the load profile of each cluster is determined. Having obtained the load profile of each cluster, real-time prices are given to each cluster, which is the best price given to all consumers in that cluster.

Assessing Heterogeneity of Osteolytic Lesions in Multiple Myeloma by 1H HR-MAS NMR Metabolomics

Directory of Open Access Journals (Sweden)

Laurette Tavel

2016-10-01

Full Text Available Multiple myeloma (MM is a malignancy of plasma cells characterized by multifocal osteolytic bone lesions. Macroscopic and genetic heterogeneity has been documented within MM lesions. Understanding the bases of such heterogeneity may unveil relevant features of MM pathobiology. To this aim, we deployed unbiased 1H high-resolution magic-angle spinning (HR-MAS nuclear magnetic resonance (NMR metabolomics to analyze multiple biopsy specimens of osteolytic lesions from one case of pathological fracture caused by MM. Multivariate analyses on normalized metabolite peak integrals allowed clusterization of samples in accordance with a posteriori histological findings. We investigated the relationship between morphological and NMR features by merging morphological data and metabolite profiling into a single correlation matrix. Data-merging addressed tissue heterogeneity, and greatly facilitated the mapping of lesions and nearby healthy tissues. Our proof-of-principle study reveals integrated metabolomics and histomorphology as a promising approach for the targeted study of osteolytic lesions.

Likelihood-based inference for clustered line transect data

DEFF Research Database (Denmark)

Waagepetersen, Rasmus Plenge; Schweder, Tore

The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

Result diversification based on query-specific cluster ranking

NARCIS (Netherlands)

He, J.; Meij, E.; de Rijke, M.

2011-01-01

Result diversification is a retrieval strategy for dealing with ambiguous or multi-faceted queries by providing documents that cover as many facets of the query as possible. We propose a result diversification framework based on query-specific clustering and cluster ranking, in which diversification

Likelihood-based inference for clustered line transect data

DEFF Research Database (Denmark)

Waagepetersen, Rasmus; Schweder, Tore

2006-01-01

The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

Postmortem MR quantification of the heart for characterization and differentiation of ischaemic myocardial lesions

International Nuclear Information System (INIS)

Zech, Wolf-Dieter; Schwendener, Nicole; Jackowski, Christian; Persson, Anders; Warntjes, Marcel J.

2015-01-01

Recently, an MRI quantification sequence has been developed which can be used to acquire T1- and T2-relaxation times as well as proton density (PD) values. Those three quantitative values can be used to describe soft tissue in an objective manner. The purpose of this study was to investigate the applicability of quantitative cardiac MRI for characterization and differentiation of ischaemic myocardial lesions of different age. Fifty post-mortem short axis cardiac 3 T MR examinations have been quantified using a quantification sequence. Myocardial lesions were identified according to histology and appearance in MRI images. Ischaemic lesions were assessed for mean T1-, T2- and proton density values. Quantitative values were plotted in a 3D-coordinate system to investigate the clustering of ischaemic myocardial lesions. A total of 16 myocardial lesions detected in MRI images were histologically characterized as acute lesions (n = 8) with perifocal oedema (n = 8), subacute lesions (n = 6) and chronic lesions (n = 2). In a 3D plot comprising the combined quantitative values of T1, T2 and PD, the clusters of all investigated lesions could be well differentiated from each other. Post-mortem quantitative cardiac MRI is feasible for characterization and discrimination of different age stages of myocardial infarction. (orig.)

Postmortem MR quantification of the heart for characterization and differentiation of ischaemic myocardial lesions

Energy Technology Data Exchange (ETDEWEB)

Zech, Wolf-Dieter; Schwendener, Nicole; Jackowski, Christian [University of Bern, Institute of Forensic Medicine, Bern (Switzerland); Persson, Anders; Warntjes, Marcel J. [University of Linkoeping, Center for Medical Image Science and Visualization (CMIV), Linkoeping (Sweden)

2015-07-15

Recently, an MRI quantification sequence has been developed which can be used to acquire T1- and T2-relaxation times as well as proton density (PD) values. Those three quantitative values can be used to describe soft tissue in an objective manner. The purpose of this study was to investigate the applicability of quantitative cardiac MRI for characterization and differentiation of ischaemic myocardial lesions of different age. Fifty post-mortem short axis cardiac 3 T MR examinations have been quantified using a quantification sequence. Myocardial lesions were identified according to histology and appearance in MRI images. Ischaemic lesions were assessed for mean T1-, T2- and proton density values. Quantitative values were plotted in a 3D-coordinate system to investigate the clustering of ischaemic myocardial lesions. A total of 16 myocardial lesions detected in MRI images were histologically characterized as acute lesions (n = 8) with perifocal oedema (n = 8), subacute lesions (n = 6) and chronic lesions (n = 2). In a 3D plot comprising the combined quantitative values of T1, T2 and PD, the clusters of all investigated lesions could be well differentiated from each other. Post-mortem quantitative cardiac MRI is feasible for characterization and discrimination of different age stages of myocardial infarction. (orig.)

Analyzing Dynamic Probabilistic Risk Assessment Data through Topology-Based Clustering

Energy Technology Data Exchange (ETDEWEB)

Diego Mandelli; Dan Maljovec; BeiWang; Valerio Pascucci; Peer-Timo Bremer

2013-09-01

We investigate the use of a topology-based clustering technique on the data generated by dynamic event tree methodologies. The clustering technique we utilizes focuses on a domain-partitioning algorithm based on topological structures known as the Morse-Smale complex, which partitions the data points into clusters based on their uniform gradient flow behavior. We perform both end state analysis and transient analysis to classify the set of nuclear scenarios. We demonstrate our methodology on a dataset generated for a sodium-cooled fast reactor during an aircraft crash scenario. The simulation tracks the temperature of the reactor as well as the time for a recovery team to fix the passive cooling system. Combined with clustering results obtained previously through mean shift methodology, we present the user with complementary views of the data that help illuminate key features that may be otherwise hidden using a single methodology. By clustering the data, the number of relevant test cases to be selected for further analysis can be drastically reduced by selecting a representative from each cluster. Identifying the similarities of simulations within a cluster can also aid in the drawing of important conclusions with respect to safety analysis.

Fuzzy Weight Cluster-Based Routing Algorithm for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Teng Gao

2015-01-01

Full Text Available Cluster-based protocol is a kind of important routing in wireless sensor networks. However, due to the uneven distribution of cluster heads in classical clustering algorithm, some nodes may run out of energy too early, which is not suitable for large-scale wireless sensor networks. In this paper, a distributed clustering algorithm based on fuzzy weighted attributes is put forward to ensure both energy efficiency and extensibility. On the premise of a comprehensive consideration of all attributes, the corresponding weight of each parameter is assigned by using the direct method of fuzzy engineering theory. Then, each node works out property value. These property values will be mapped to the time axis and be triggered by a timer to broadcast cluster headers. At the same time, the radio coverage method is adopted, in order to avoid collisions and to ensure the symmetrical distribution of cluster heads. The aggregated data are forwarded to the sink node in the form of multihop. The simulation results demonstrate that clustering algorithm based on fuzzy weighted attributes has a longer life expectancy and better extensibility than LEACH-like algorithms.

Clustered DNA damages induced in isolated DNA and in human cells by low doses of ionizing radiation

Science.gov (United States)

Sutherland, B. M.; Bennett, P. V.; Sidorkina, O.; Laval, J.; Lowenstein, D. I. (Principal Investigator)

2000-01-01

Clustered DNA damages-two or more closely spaced damages (strand breaks, abasic sites, or oxidized bases) on opposing strands-are suspects as critical lesions producing lethal and mutagenic effects of ionizing radiation. However, as a result of the lack of methods for measuring damage clusters induced by ionizing radiation in genomic DNA, neither the frequencies of their production by physiological doses of radiation, nor their repairability, nor their biological effects are known. On the basis of methods that we developed for quantitating damages in large DNAs, we have devised and validated a way of measuring ionizing radiation-induced clustered lesions in genomic DNA, including DNA from human cells. DNA is treated with an endonuclease that induces a single-strand cleavage at an oxidized base or abasic site. If there are two closely spaced damages on opposing strands, such cleavage will reduce the size of the DNA on a nondenaturing gel. We show that ionizing radiation does induce clustered DNA damages containing abasic sites, oxidized purines, or oxidized pyrimidines. Further, the frequency of each of these cluster classes is comparable to that of frank double-strand breaks; among all complex damages induced by ionizing radiation, double-strand breaks are only about 20%, with other clustered damage constituting some 80%. We also show that even low doses (0.1-1 Gy) of high linear energy transfer ionizing radiation induce clustered damages in human cells.

An evidence-based review for the management of cystic pancreatic lesions

International Nuclear Information System (INIS)

Planner, A.C.; Anderson, E.M.; Slater, A.; Phillips-Hughes, J.; Bungay, H.K.; Betts, M.

2007-01-01

The incidental finding of pancreatic cysts is becoming more common because of the increased use of cross-sectional imaging. As a result, the perspective from historical series of symptomatic patients is not always applicable to the current cohort of patients with cystic lesions in their pancreas. In this review, the characteristic radiological features that aid diagnosis are highlighted, and the complementary role of different imaging methods and the appropriate use of tissue sampling are identified. Based on the literature regarding the diagnostic role of imaging in characterizing cystic pancreatic lesions, it is possible to recommend a practical imaging algorithm for the diagnosis of cystic pancreatic lesions

Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

Science.gov (United States)

Hallac, David; Vare, Sagar; Boyd, Stephen; Leskovec, Jure

2018-01-01

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios. PMID:29770257

Natural history of chondroid skull base lesions - case report and review

International Nuclear Information System (INIS)

Schmidinger, A.; Rosahl, S.K.; Vorkapic, P.; Samii, M.

2002-01-01

Long-term follow-up reports on chondroid lesions of the skull base are rarely presented in the literature. There are virtually no data on natural growth rates of these tumors based on MRI obtained over a period of 10 years or longer. We followed a patient who has had such a lesion for more than 12 years. A non-progressive, slight abducens palsy has been the only associated symptom so far. Even though the patient was operated on for an additional intracranial arterio-venous malformation, clinical features and chromosomal testing excluded Maffucci's syndrome. The MRI follow-up in this case provides an extraordinary perspective on the natural history of chondroid skull base tumors. (orig.)

Fatigue Feature Extraction Analysis based on a K-Means Clustering Approach

Directory of Open Access Journals (Sweden)

M.F.M. Yunoh

2015-06-01

Full Text Available This paper focuses on clustering analysis using a K-means approach for fatigue feature dataset extraction. The aim of this study is to group the dataset as closely as possible (homogeneity for the scattered dataset. Kurtosis, the wavelet-based energy coefficient and fatigue damage are calculated for all segments after the extraction process using wavelet transform. Kurtosis, the wavelet-based energy coefficient and fatigue damage are used as input data for the K-means clustering approach. K-means clustering calculates the average distance of each group from the centroid and gives the objective function values. Based on the results, maximum values of the objective function can be seen in the two centroid clusters, with a value of 11.58. The minimum objective function value is found at 8.06 for five centroid clusters. It can be seen that the objective function with the lowest value for the number of clusters is equal to five; which is therefore the best cluster for the dataset.

Neural network ensemble based CAD system for focal liver lesions from B-mode ultrasound.

Science.gov (United States)

Virmani, Jitendra; Kumar, Vinod; Kalra, Naveen; Khandelwal, Niranjan

2014-08-01

A neural network ensemble (NNE) based computer-aided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellular carcinoma (HCC) lesions, along with (3) normal (NOR) liver tissue is proposed in the present work. Expert radiologists, visualize the textural characteristics of regions inside and outside the lesions to differentiate between different FLLs, accordingly texture features computed from inside lesion regions of interest (IROIs) and texture ratio features computed from IROIs and surrounding lesion regions of interests (SROIs) are taken as input. Principal component analysis (PCA) is used for reducing the dimensionality of the feature space before classifier design. The first step of classification module consists of a five class PCA-NN based primary classifier which yields probability outputs for five liver image classes. The second step of classification module consists of ten binary PCA-NN based secondary classifiers for NOR/Cyst, NOR/HEM, NOR/HCC, NOR/MET, Cyst/HEM, Cyst/HCC, Cyst/MET, HEM/HCC, HEM/MET and HCC/MET classes. The probability outputs of five class PCA-NN based primary classifier is used to determine the first two most probable classes for a test instance, based on which it is directed to the corresponding binary PCA-NN based secondary classifier for crisp classification between two classes. By including the second step of the classification module, classification accuracy increases from 88.7 % to 95 %. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in differential diagnosis of FLLs.

Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

Science.gov (United States)

Taamneh, Madhar; Taamneh, Salah; Alkheder, Sharaf

2017-09-01

Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

Cluster Based Hierarchical Routing Protocol for Wireless Sensor Network

OpenAIRE

Rashed, Md. Golam; Kabir, M. Hasnat; Rahim, Muhammad Sajjadur; Ullah, Shaikh Enayet

2012-01-01

The efficient use of energy source in a sensor node is most desirable criteria for prolong the life time of wireless sensor network. In this paper, we propose a two layer hierarchical routing protocol called Cluster Based Hierarchical Routing Protocol (CBHRP). We introduce a new concept called head-set, consists of one active cluster head and some other associate cluster heads within a cluster. The head-set members are responsible for control and management of the network. Results show that t...

Recurrent neural networks for breast lesion classification based on DCE-MRIs

Science.gov (United States)

Antropova, Natasha; Huynh, Benjamin; Giger, Maryellen

2018-02-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a significant role in breast cancer screening, cancer staging, and monitoring response to therapy. Recently, deep learning methods are being rapidly incorporated in image-based breast cancer diagnosis and prognosis. However, most of the current deep learning methods make clinical decisions based on 2-dimentional (2D) or 3D images and are not well suited for temporal image data. In this study, we develop a deep learning methodology that enables integration of clinically valuable temporal components of DCE-MRIs into deep learning-based lesion classification. Our work is performed on a database of 703 DCE-MRI cases for the task of distinguishing benign and malignant lesions, and uses the area under the ROC curve (AUC) as the performance metric in conducting that task. We train a recurrent neural network, specifically a long short-term memory network (LSTM), on sequences of image features extracted from the dynamic MRI sequences. These features are extracted with VGGNet, a convolutional neural network pre-trained on a large dataset of natural images ImageNet. The features are obtained from various levels of the network, to capture low-, mid-, and high-level information about the lesion. Compared to a classification method that takes as input only images at a single time-point (yielding an AUC = 0.81 (se = 0.04)), our LSTM method improves lesion classification with an AUC of 0.85 (se = 0.03).

Computer-aided diagnosis of mammographic microcalcification clusters

International Nuclear Information System (INIS)

Kallergi, Maria

2004-01-01

Computer-aided diagnosis techniques in medical imaging are developed for the automated differentiation between benign and malignant lesions and go beyond computer-aided detection by providing cancer likelihood for a detected lesion given image and/or patient characteristics. The goal of this study was the development and evaluation of a computer-aided detection and diagnosis algorithm for mammographic calcification clusters. The emphasis was on the diagnostic component, although the algorithm included automated detection, segmentation, and classification steps based on wavelet filters and artificial neural networks. Classification features were selected primarily from descriptors of the morphology of the individual calcifications and the distribution of the cluster. Thirteen such descriptors were selected and, combined with patient's age, were given as inputs to the network. The features were ranked and evaluated for the classification of 100 high-resolution, digitized mammograms containing biopsy-proven, benign and malignant calcification clusters. The classification performance of the algorithm reached a 100% sensitivity for a specificity of 85% (receiver operating characteristic area index A z =0.98±0.01). Tests of the algorithm under various conditions showed that the selected features were robust morphological and distributional descriptors, relatively insensitive to segmentation and detection errors such as false positive signals. The algorithm could exceed the performance of a similar visual analysis system that was used as basis for development and, combined with a simple image standardization process, could be applied to images from different imaging systems and film digitizers with similar sensitivity and specificity rates

Analyzing the factors affecting network lifetime cluster-based wireless sensor network

International Nuclear Information System (INIS)

Malik, A.S.; Qureshi, A.

2010-01-01

Cluster-based wireless sensor networks enable the efficient utilization of the limited energy resources of the deployed sensor nodes and hence prolong the node as well as network lifetime. Low Energy Adaptive Clustering Hierarchy (Leach) is one of the most promising clustering protocol proposed for wireless sensor networks. This paper provides the energy utilization and lifetime analysis for cluster-based wireless sensor networks based upon LEACH protocol. Simulation results identify some important factors that induce unbalanced energy utilization between the sensor nodes and hence affect the network lifetime in these types of networks. These results highlight the need for a standardized, adaptive and distributed clustering technique that can increase the network lifetime by further balancing the energy utilization among sensor nodes. (author)

Lesion progression in post-treatment persistent endodontic lesions.

Science.gov (United States)

Yu, Victoria Soo Hoon; Messer, Harold Henry; Shen, Liang; Yee, Robert; Hsu, Chin-ying Stephen

2012-10-01

Radiographic lesions related to root-filled teeth may persist for long periods after treatment and are considered to indicate failure of initial treatment. Persistent lesions are found in a proportion of cases, but information on lesion progression is lacking. This study examined the incidence of lesion improvement, remaining unchanged, and deterioration among persistent lesions in a group of patients recruited from a university-based clinic and identified potential predictors for lesion progression. Patients of a university clinic with persistent endodontic lesions at least 4 years since treatment and with original treatment radiographs available were recruited with informed consent. Data were obtained by interview and from dental records and clinical and radiographic examinations. Univariate and multivariate statistical analyses were carried out by using SPSS (version 19). One hundred fifty-one persistent lesions were identified in 114 patients. A majority of the lesions (107, 70.9%) received treatment between 4 and 5 years prior. Eighty-six lesions (57.0%) improved, 18 (11.9%) remained unchanged, and 47 (31.1%) deteriorated since treatment. Potential predictors for lesions that did not improve included recall lesion size, pain on biting at recall examination, history of a postobturation flare-up, and a non-ideal root-filling length (P < .05). Lesions that had persisted for a longer period appeared less likely to be improving (relative risk, 1.038; 95% confidence interval, 1.000-1.077). A specific time interval alone should not be used to conclude that a lesion will not resolve without intervention. This study identified several clinical factors that are associated with deteriorating persistent lesions, which should aid in identifying lesions that require further intervention. Copyright © 2012 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks.

Science.gov (United States)

Gui, Jinsong; Zhou, Kai; Xiong, Naixue

2016-09-25

Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.

A fast density-based clustering algorithm for real-time Internet of Things stream.

Science.gov (United States)

Amini, Amineh; Saboohi, Hadi; Wah, Teh Ying; Herawan, Tutut

2014-01-01

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets.

Cluster Ensemble-Based Image Segmentation

Directory of Open Access Journals (Sweden)

Xiaoru Wang

2013-07-01

Full Text Available Image segmentation is the foundation of computer vision applications. In this paper, we propose a new cluster ensemble-based image segmentation algorithm, which overcomes several problems of traditional methods. We make two main contributions in this paper. First, we introduce the cluster ensemble concept to fuse the segmentation results from different types of visual features effectively, which can deliver a better final result and achieve a much more stable performance for broad categories of images. Second, we exploit the PageRank idea from Internet applications and apply it to the image segmentation task. This can improve the final segmentation results by combining the spatial information of the image and the semantic similarity of regions. Our experiments on four public image databases validate the superiority of our algorithm over conventional single type of feature or multiple types of features-based algorithms, since our algorithm can fuse multiple types of features effectively for better segmentation results. Moreover, our method is also proved to be very competitive in comparison with other state-of-the-art segmentation algorithms.

Pigmented skin lesion detection using random forest and wavelet-based texture

Science.gov (United States)

Hu, Ping; Yang, Tie-jun

2016-10-01

The incidence of cutaneous malignant melanoma, a disease of worldwide distribution and is the deadliest form of skin cancer, has been rapidly increasing over the last few decades. Because advanced cutaneous melanoma is still incurable, early detection is an important step toward a reduction in mortality. Dermoscopy photographs are commonly used in melanoma diagnosis and can capture detailed features of a lesion. A great variability exists in the visual appearance of pigmented skin lesions. Therefore, in order to minimize the diagnostic errors that result from the difficulty and subjectivity of visual interpretation, an automatic detection approach is required. The objectives of this paper were to propose a hybrid method using random forest and Gabor wavelet transformation to accurately differentiate which part belong to lesion area and the other is not in a dermoscopy photographs and analyze segmentation accuracy. A random forest classifier consisting of a set of decision trees was used for classification. Gabor wavelets transformation are the mathematical model of visual cortical cells of mammalian brain and an image can be decomposed into multiple scales and multiple orientations by using it. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain. Texture features based on Gabor wavelets transformation are found by the Gabor filtered image. Experiment results indicate the following: (1) the proposed algorithm based on random forest outperformed the-state-of-the-art in pigmented skin lesions detection (2) and the inclusion of Gabor wavelet transformation based texture features improved segmentation accuracy significantly.

XML documents cluster research based on frequent subpatterns

Science.gov (United States)

Ding, Tienan; Li, Wei; Li, Xiongfei

2015-12-01

XML data is widely used in the information exchange field of Internet, and XML document data clustering is the hot research topic. In the XML document clustering process, measure differences between two XML documents is time costly, and impact the efficiency of XML document clustering. This paper proposed an XML documents clustering method based on frequent patterns of XML document dataset, first proposed a coding tree structure for encoding the XML document, and translate frequent pattern mining from XML documents into frequent pattern mining from string. Further, using the cosine similarity calculation method and cohesive hierarchical clustering method for XML document dataset by frequent patterns. Because of frequent patterns are subsets of the original XML document data, so the time consumption of XML document similarity measure is reduced. The experiment runs on synthetic dataset and the real datasets, the experimental result shows that our method is efficient.

Local Community Detection Algorithm Based on Minimal Cluster

Directory of Open Access Journals (Sweden)

Yong Zhou

2016-01-01

Full Text Available In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.

Fault-tolerant measurement-based quantum computing with continuous-variable cluster states.

Science.gov (United States)

Menicucci, Nicolas C

2014-03-28

A long-standing open question about Gaussian continuous-variable cluster states is whether they enable fault-tolerant measurement-based quantum computation. The answer is yes. Initial squeezing in the cluster above a threshold value of 20.5 dB ensures that errors from finite squeezing acting on encoded qubits are below the fault-tolerance threshold of known qubit-based error-correcting codes. By concatenating with one of these codes and using ancilla-based error correction, fault-tolerant measurement-based quantum computation of theoretically indefinite length is possible with finitely squeezed cluster states.

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks

Science.gov (United States)

Gui, Jinsong; Zhou, Kai; Xiong, Naixue

2016-01-01

Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude. PMID:27681731

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Jinsong Gui

2016-09-01

Full Text Available Multi-Input Multi-Output (MIMO can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs, clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO, which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.

An Energy Centric Cluster-Based Routing Protocol for Wireless Sensor Networks.

Science.gov (United States)

Hosen, A S M Sanwar; Cho, Gi Hwan

2018-05-11

Clustering is an effective way to prolong the lifetime of a wireless sensor network (WSN). The common approach is to elect cluster heads to take routing and controlling duty, and to periodically rotate each cluster head's role to distribute energy consumption among nodes. However, a significant amount of energy dissipates due to control messages overhead, which results in a shorter network lifetime. This paper proposes an energy-centric cluster-based routing mechanism in WSNs. To begin with, cluster heads are elected based on the higher ranks of the nodes. The rank is defined by residual energy and average distance from the member nodes. With the role of data aggregation and data forwarding, a cluster head acts as a caretaker for cluster-head election in the next round, where the ranks' information are piggybacked along with the local data sending during intra-cluster communication. This reduces the number of control messages for the cluster-head election as well as the cluster formation in detail. Simulation results show that our proposed protocol saves the energy consumption among nodes and achieves a significant improvement in the network lifetime.

Carbon based nanostructures: diamond clusters structured with nanotubes

Directory of Open Access Journals (Sweden)

O.A. Shenderova

2003-01-01

Full Text Available Feasibility of designing composites from carbon nanotubes and nanodiamond clusters is discussed based on atomistic simulations. Depending on nanotube size and morphology, some types of open nanotubes can be chemically connected with different facets of diamond clusters. The geometrical relation between different types of nanotubes and different diamond facets for construction of mechanically stable composites with all bonds saturated is summarized. Potential applications of the suggested nanostructures are briefly discussed based on the calculations of their electronic properties using environment dependent self-consistent tight-binding approach.

Communication Base Station Log Analysis Based on Hierarchical Clustering

Directory of Open Access Journals (Sweden)

Zhang Shao-Hua

2017-01-01

Full Text Available Communication base stations generate massive data every day, these base station logs play an important value in mining of the business circles. This paper use data mining technology and hierarchical clustering algorithm to group the scope of business circle for the base station by recording the data of these base stations.Through analyzing the data of different business circle based on feature extraction and comparing different business circle category characteristics, which can choose a suitable area for operators of commercial marketing.

A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream

Science.gov (United States)

Ying Wah, Teh

2014-01-01

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets. PMID:25110753

Variable selection in multivariate calibration based on clustering of variable concept.

Science.gov (United States)

Farrokhnia, Maryam; Karimi, Sadegh

2016-01-01

Recently we have proposed a new variable selection algorithm, based on clustering of variable concept (CLoVA) in classification problem. With the same idea, this new concept has been applied to a regression problem and then the obtained results have been compared with conventional variable selection strategies for PLS. The basic idea behind the clustering of variable is that, the instrument channels are clustered into different clusters via clustering algorithms. Then, the spectral data of each cluster are subjected to PLS regression. Different real data sets (Cargill corn, Biscuit dough, ACE QSAR, Soy, and Tablet) have been used to evaluate the influence of the clustering of variables on the prediction performances of PLS. Almost in the all cases, the statistical parameter especially in prediction error shows the superiority of CLoVA-PLS respect to other variable selection strategies. Finally the synergy clustering of variable (sCLoVA-PLS), which is used the combination of cluster, has been proposed as an efficient and modification of CLoVA algorithm. The obtained statistical parameter indicates that variable clustering can split useful part from redundant ones, and then based on informative cluster; stable model can be reached. Copyright © 2015 Elsevier B.V. All rights reserved.

Utility of K-Means clustering algorithm in differentiating apparent diffusion coefficient values between benign and malignant neck pathologies

Science.gov (United States)

Srinivasan, A.; Galbán, C.J.; Johnson, T.D.; Chenevert, T.L.; Ross, B.D.; Mukherji, S.K.

2014-01-01

Purpose The objective of our study was to analyze the differences between apparent diffusion coefficient (ADC) partitions (created using the K-Means algorithm) between benign and malignant neck lesions and evaluate its benefit in distinguishing these entities. Material and methods MRI studies of 10 benign and 10 malignant proven neck pathologies were post-processed on a PC using in-house software developed in MATLAB (The MathWorks, Inc., Natick, MA). Lesions were manually contoured by two neuroradiologists with the ADC values within each lesion clustered into two (low ADC-ADCL, high ADC-ADCH) and three partitions (ADCL, intermediate ADC-ADCI, ADCH) using the K-Means clustering algorithm. An unpaired two-tailed Student’s t-test was performed for all metrics to determine statistical differences in the means between the benign and malignant pathologies. Results Statistically significant difference between the mean ADCL clusters in benign and malignant pathologies was seen in the 3 cluster models of both readers (p=0.03, 0.022 respectively) and the 2 cluster model of reader 2 (p=0.04) with the other metrics (ADCH, ADCI, whole lesion mean ADC) not revealing any significant differences. Receiver operating characteristics curves demonstrated the quantitative difference in mean ADCH and ADCL in both the 2 and 3 cluster models to be predictive of malignancy (2 clusters: p=0.008, area under curve=0.850, 3 clusters: p=0.01, area under curve=0.825). Conclusion The K-Means clustering algorithm that generates partitions of large datasets may provide a better characterization of neck pathologies and may be of additional benefit in distinguishing benign and malignant neck pathologies compared to whole lesion mean ADC alone. PMID:20007723

Price Formation Based on Particle-Cluster Aggregation

Science.gov (United States)

Wang, Shijun; Zhang, Changshui

In the present work, we propose a microscopic model of financial markets based on particle-cluster aggregation on a two-dimensional small-world information network in order to simulate the dynamics of the stock markets. "Stylized facts" of the financial market time series, such as fat-tail distribution of returns, volatility clustering and multifractality, are observed in the model. The results of the model agree with empirical data taken from historical records of the daily closures of the NYSE composite index.

Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method

Directory of Open Access Journals (Sweden)

Yimei Wang

2018-04-01

Full Text Available To meet the increasing wind power forecasting (WPF demands of newly built wind farms without historical data, physical WPF methods are widely used. The computational fluid dynamics (CFD pre-calculated flow fields (CPFF-based WPF is a promising physical approach, which can balance well the competing demands of computational efficiency and accuracy. To enhance its adaptability for wind farms in complex terrain, a WPF method combining wind turbine clustering with CPFF is first proposed where the wind turbines in the wind farm are clustered and a forecasting is undertaken for each cluster. K-means, hierarchical agglomerative and spectral analysis methods are used to establish the wind turbine clustering models. The Silhouette Coefficient, Calinski-Harabaz index and within-between index are proposed as criteria to evaluate the effectiveness of the established clustering models. Based on different clustering methods and schemes, various clustering databases are built for clustering pre-calculated CFD (CPCC-based short-term WPF. For the wind farm case studied, clustering evaluation criteria show that hierarchical agglomerative clustering has reasonable results, spectral clustering is better and K-means gives the best performance. The WPF results produced by different clustering databases also prove the effectiveness of the three evaluation criteria in turn. The newly developed CPCC model has a much higher WPF accuracy than the CPFF model without using clustering techniques, both on temporal and spatial scales. The research provides supports for both the development and improvement of short-term physical WPF systems.

Spanning Tree Based Attribute Clustering

DEFF Research Database (Denmark)

Zeng, Yifeng; Jorge, Cordero Hernandez

2009-01-01

Attribute clustering has been previously employed to detect statistical dependence between subsets of variables. We propose a novel attribute clustering algorithm motivated by research of complex networks, called the Star Discovery algorithm. The algorithm partitions and indirectly discards...... inconsistent edges from a maximum spanning tree by starting appropriate initial modes, therefore generating stable clusters. It discovers sound clusters through simple graph operations and achieves significant computational savings. We compare the Star Discovery algorithm against earlier attribute clustering...

Fuzzy clustering-based segmented attenuation correction in whole-body PET

CERN Document Server

Zaidi, H; Boudraa, A; Slosman, DO

2001-01-01

Segmented-based attenuation correction is now a widely accepted technique to reduce noise contribution of measured attenuation correction. In this paper, we present a new method for segmenting transmission images in positron emission tomography. This reduces the noise on the correction maps while still correcting for differing attenuation coefficients of specific tissues. Based on the Fuzzy C-Means (FCM) algorithm, the method segments the PET transmission images into a given number of clusters to extract specific areas of differing attenuation such as air, the lungs and soft tissue, preceded by a median filtering procedure. The reconstructed transmission image voxels are therefore segmented into populations of uniform attenuation based on the human anatomy. The clustering procedure starts with an over-specified number of clusters followed by a merging process to group clusters with similar properties and remove some undesired substructures using anatomical knowledge. The method is unsupervised, adaptive and a...

ENERGY OPTIMIZATION IN CLUSTER BASED WIRELESS SENSOR NETWORKS

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T. SHANKAR

2014-04-01

Full Text Available Wireless sensor networks (WSN are made up of sensor nodes which are usually battery-operated devices, and hence energy saving of sensor nodes is a major design issue. To prolong the networks lifetime, minimization of energy consumption should be implemented at all layers of the network protocol stack starting from the physical to the application layer including cross-layer optimization. Optimizing energy consumption is the main concern for designing and planning the operation of the WSN. Clustering technique is one of the methods utilized to extend lifetime of the network by applying data aggregation and balancing energy consumption among sensor nodes of the network. This paper proposed new version of Low Energy Adaptive Clustering Hierarchy (LEACH, protocols called Advanced Optimized Low Energy Adaptive Clustering Hierarchy (AOLEACH, Optimal Deterministic Low Energy Adaptive Clustering Hierarchy (ODLEACH, and Varying Probability Distance Low Energy Adaptive Clustering Hierarchy (VPDL combination with Shuffled Frog Leap Algorithm (SFLA that enables selecting best optimal adaptive cluster heads using improved threshold energy distribution compared to LEACH protocol and rotating cluster head position for uniform energy dissipation based on energy levels. The proposed algorithm optimizing the life time of the network by increasing the first node death (FND time and number of alive nodes, thereby increasing the life time of the network.

KM-FCM: A fuzzy clustering optimization algorithm based on Mahalanobis distance

Directory of Open Access Journals (Sweden)

Zhiwen ZU

2018-04-01

Full Text Available The traditional fuzzy clustering algorithm uses Euclidean distance as the similarity criterion, which is disadvantageous to the multidimensional data processing. In order to solve this situation, Mahalanobis distance is used instead of the traditional Euclidean distance, and the optimization of fuzzy clustering algorithm based on Mahalanobis distance is studied to enhance the clustering effect and ability. With making the initialization means by Heuristic search algorithm combined with k-means algorithm, and in terms of the validity function which could automatically adjust the optimal clustering number, an optimization algorithm KM-FCM is proposed. The new algorithm is compared with FCM algorithm, FCM-M algorithm and M-FCM algorithm in three standard data sets. The experimental results show that the KM-FCM algorithm is effective. It has higher clustering accuracy than FCM, FCM-M and M-FCM, recognizing high-dimensional data clustering well. It has global optimization effect, and the clustering number has no need for setting in advance. The new algorithm provides a reference for the optimization of fuzzy clustering algorithm based on Mahalanobis distance.

Seminal Quality Prediction Using Clustering-Based Decision Forests

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Hong Wang

2014-08-01

Full Text Available Prediction of seminal quality with statistical learning tools is an emerging methodology in decision support systems in biomedical engineering and is very useful in early diagnosis of seminal patients and selection of semen donors candidates. However, as is common in medical diagnosis, seminal quality prediction faces the class imbalance problem. In this paper, we propose a novel supervised ensemble learning approach, namely Clustering-Based Decision Forests, to tackle unbalanced class learning problem in seminal quality prediction. Experiment results on real fertility diagnosis dataset have shown that Clustering-Based Decision Forests outperforms decision tree, Support Vector Machines, random forests, multilayer perceptron neural networks and logistic regression by a noticeable margin. Clustering-Based Decision Forests can also be used to evaluate variables’ importance and the top five important factors that may affect semen concentration obtained in this study are age, serious trauma, sitting time, the season when the semen sample is produced, and high fevers in the last year. The findings could be helpful in explaining seminal concentration problems in infertile males or pre-screening semen donor candidates.

A similarity based agglomerative clustering algorithm in networks

Science.gov (United States)

Liu, Zhiyuan; Wang, Xiujuan; Ma, Yinghong

2018-04-01

The detection of clusters is benefit for understanding the organizations and functions of networks. Clusters, or communities, are usually groups of nodes densely interconnected but sparsely linked with any other clusters. To identify communities, an efficient and effective community agglomerative algorithm based on node similarity is proposed. The proposed method initially calculates similarities between each pair of nodes, and form pre-partitions according to the principle that each node is in the same community as its most similar neighbor. After that, check each partition whether it satisfies community criterion. For the pre-partitions who do not satisfy, incorporate them with others that having the biggest attraction until there are no changes. To measure the attraction ability of a partition, we propose an attraction index that based on the linked node's importance in networks. Therefore, our proposed method can better exploit the nodes' properties and network's structure. To test the performance of our algorithm, both synthetic and empirical networks ranging in different scales are tested. Simulation results show that the proposed algorithm can obtain superior clustering results compared with six other widely used community detection algorithms.

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

Directory of Open Access Journals (Sweden)

Olszewski Kellen L

2007-07-01

Full Text Available Abstract Background The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes. Results We developed Nearest Neighbor Networks (NNN, a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. Conclusion The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the

Managing Carious Lesions

DEFF Research Database (Denmark)

Schwendicke, F; Frencken, J E; Bjørndal, L

2016-01-01

should be prioritized, while in shallow or moderately deep lesions, restoration longevity becomes more important. For teeth with shallow or moderately deep cavitated lesions, carious tissue removal is performed according toselective removal to firm dentine.In deep cavitated lesions in primary......The International Caries Consensus Collaboration undertook a consensus process and here presents clinical recommendations for carious tissue removal and managing cavitated carious lesions, including restoration, based on texture of demineralized dentine. Dentists should manage the disease dental...

A Cluster- Based Secure Active Network Environment

Institute of Scientific and Technical Information of China (English)

CHEN Xiao-lin; ZHOU Jing-yang; DAI Han; LU Sang-lu; CHEN Gui-hai

2005-01-01

We introduce a cluster-based secure active network environment (CSANE) which separates the processing of IP packets from that of active packets in active routers. In this environment, the active code authorized or trusted by privileged users is executed in the secure execution environment (EE) of the active router, while others are executed in the secure EE of the nodes in the distributed shared memory (DSM) cluster. With the supports of a multi-process Java virtual machine and KeyNote, untrusted active packets are controlled to securely consume resource. The DSM consistency management makes that active packets can be parallelly processed in the DSM cluster as if they were processed one by one in ANTS (Active Network Transport System). We demonstrate that CSANE has good security and scalability, but imposing little changes on traditional routers.

A Clustering-Based Automatic Transfer Function Design for Volume Visualization

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Tianjin Zhang

2016-01-01

Full Text Available The two-dimensional transfer functions (TFs designed based on intensity-gradient magnitude (IGM histogram are effective tools for the visualization and exploration of 3D volume data. However, traditional design methods usually depend on multiple times of trial-and-error. We propose a novel method for the automatic generation of transfer functions by performing the affinity propagation (AP clustering algorithm on the IGM histogram. Compared with previous clustering algorithms that were employed in volume visualization, the AP clustering algorithm has much faster convergence speed and can achieve more accurate clustering results. In order to obtain meaningful clustering results, we introduce two similarity measurements: IGM similarity and spatial similarity. These two similarity measurements can effectively bring the voxels of the same tissue together and differentiate the voxels of different tissues so that the generated TFs can assign different optical properties to different tissues. Before performing the clustering algorithm on the IGM histogram, we propose to remove noisy voxels based on the spatial information of voxels. Our method does not require users to input the number of clusters, and the classification and visualization process is automatic and efficient. Experiments on various datasets demonstrate the effectiveness of the proposed method.

DCE: A Distributed Energy-Efficient Clustering Protocol for Wireless Sensor Network Based on Double-Phase Cluster-Head Election.

Science.gov (United States)

Han, Ruisong; Yang, Wei; Wang, Yipeng; You, Kaiming

2017-05-01

Clustering is an effective technique used to reduce energy consumption and extend the lifetime of wireless sensor network (WSN). The characteristic of energy heterogeneity of WSNs should be considered when designing clustering protocols. We propose and evaluate a novel distributed energy-efficient clustering protocol called DCE for heterogeneous wireless sensor networks, based on a Double-phase Cluster-head Election scheme. In DCE, the procedure of cluster head election is divided into two phases. In the first phase, tentative cluster heads are elected with the probabilities which are decided by the relative levels of initial and residual energy. Then, in the second phase, the tentative cluster heads are replaced by their cluster members to form the final set of cluster heads if any member in their cluster has more residual energy. Employing two phases for cluster-head election ensures that the nodes with more energy have a higher chance to be cluster heads. Energy consumption is well-distributed in the proposed protocol, and the simulation results show that DCE achieves longer stability periods than other typical clustering protocols in heterogeneous scenarios.

Radiological appearances of papillary breast lesions

International Nuclear Information System (INIS)

Brookes, M.J.; Bourke, A.G.

2008-01-01

This review illustrates the varied appearances of benign and malignant papillary breast tumours, as identified by a breast cancer-screening programme. The commonest mammographic appearance of a papillary tumour is as a soft-tissue mass, with calcification present in less than half of cases. When calcification is present the pattern is variable, but clusters of pleomorphic calcification can occur, sometimes resembling the mammographic appearance of invasive ductal carcinoma. Ultrasonography of papillary lesions typically shows a solid, oval, intraductal mass, often associated with duct dilatation. A cystic component is also commonly seen, and lesions may appear hypervascular on colour Doppler ultrasound. Magnetic resonance imaging (MRI) has a high sensitivity, but low specificity for detecting papillary tumours, and is useful in establishing the extent and distribution of lesions in patients with multiple papillomatosis. Despite a benign histology on core biopsy, an argument exists for complete surgical excision of all papillary tumours, as a significant proportion of papillomas will contain foci of atypia or overt malignant change

Radiological appearances of papillary breast lesions

Energy Technology Data Exchange (ETDEWEB)

Brookes, M.J. [Sir Charles Gairdner Hospital, Nedlands, Perth, Western Australia (Australia)], E-mail: mattbrookes@doctors.org.uk; Bourke, A.G. [Sir Charles Gairdner Hospital, Nedlands, Perth, Western Australia (Australia)

2008-11-15

This review illustrates the varied appearances of benign and malignant papillary breast tumours, as identified by a breast cancer-screening programme. The commonest mammographic appearance of a papillary tumour is as a soft-tissue mass, with calcification present in less than half of cases. When calcification is present the pattern is variable, but clusters of pleomorphic calcification can occur, sometimes resembling the mammographic appearance of invasive ductal carcinoma. Ultrasonography of papillary lesions typically shows a solid, oval, intraductal mass, often associated with duct dilatation. A cystic component is also commonly seen, and lesions may appear hypervascular on colour Doppler ultrasound. Magnetic resonance imaging (MRI) has a high sensitivity, but low specificity for detecting papillary tumours, and is useful in establishing the extent and distribution of lesions in patients with multiple papillomatosis. Despite a benign histology on core biopsy, an argument exists for complete surgical excision of all papillary tumours, as a significant proportion of papillomas will contain foci of atypia or overt malignant change.

Semantic based cluster content discovery in description first clustering algorithm

International Nuclear Information System (INIS)

Khan, M.W.; Asif, H.M.S.

2017-01-01

In the field of data analytics grouping of like documents in textual data is a serious problem. A lot of work has been done in this field and many algorithms have purposed. One of them is a category of algorithms which firstly group the documents on the basis of similarity and then assign the meaningful labels to those groups. Description first clustering algorithm belong to the category in which the meaningful description is deduced first and then relevant documents are assigned to that description. LINGO (Label Induction Grouping Algorithm) is the algorithm of description first clustering category which is used for the automatic grouping of documents obtained from search results. It uses LSI (Latent Semantic Indexing); an IR (Information Retrieval) technique for induction of meaningful labels for clusters and VSM (Vector Space Model) for cluster content discovery. In this paper we present the LINGO while it is using LSI during cluster label induction and cluster content discovery phase. Finally, we compare results obtained from the said algorithm while it uses VSM and Latent semantic analysis during cluster content discovery phase. (author)

Simulation-based marginal likelihood for cluster strong lensing cosmology

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Killedar, M.; Borgani, S.; Fabjan, D.; Dolag, K.; Granato, G.; Meneghetti, M.; Planelles, S.; Ragone-Figueroa, C.

2018-01-01

Comparisons between observed and predicted strong lensing properties of galaxy clusters have been routinely used to claim either tension or consistency with Λ cold dark matter cosmology. However, standard approaches to such cosmological tests are unable to quantify the preference for one cosmology over another. We advocate approximating the relevant Bayes factor using a marginal likelihood that is based on the following summary statistic: the posterior probability distribution function for the parameters of the scaling relation between Einstein radii and cluster mass, α and β. We demonstrate, for the first time, a method of estimating the marginal likelihood using the X-ray selected z > 0.5 Massive Cluster Survey clusters as a case in point and employing both N-body and hydrodynamic simulations of clusters. We investigate the uncertainty in this estimate and consequential ability to compare competing cosmologies, which arises from incomplete descriptions of baryonic processes, discrepancies in cluster selection criteria, redshift distribution and dynamical state. The relation between triaxial cluster masses at various overdensities provides a promising alternative to the strong lensing test.

Visualization of suspicious lesions in breast MRI based on intelligent neural systems

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Twellmann, Thorsten; Lange, Oliver; Nattkemper, Tim Wilhelm; Meyer-Bäse, Anke

2006-05-01

Intelligent medical systems based on supervised and unsupervised artificial neural networks are applied to the automatic visualization and classification of suspicious lesions in breast MRI. These systems represent an important component of future sophisticated computer-aided diagnosis systems and enable the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By taking into account the heterogenity of the cancerous tissue, these techniques reveal the malignant, benign and normal kinetic signals and and provide a regional subclassification of pathological breast tissue. Intelligent medical systems are expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging.

A new background distribution-based active contour model for three-dimensional lesion segmentation in breast DCE-MRI

Energy Technology Data Exchange (ETDEWEB)

Liu, Hui; Liu, Yiping; Qiu, Tianshuang [Department of Biomedical Engineering, Dalian University of Technology, Dalian 116024 (China); Zhao, Zuowei, E-mail: liuhui@dlut.edu.cn [Second Affiliated Hospital, Dalian Medical University, Dalian 116027 (China); Zhang, Lina [Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian 116027 (China)

2014-08-15

Purpose: To develop and evaluate a computerized semiautomatic segmentation method for accurate extraction of three-dimensional lesions from dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) of the breast. Methods: The authors propose a new background distribution-based active contour model using level set (BDACMLS) to segment lesions in breast DCE-MRIs. The method starts with manual selection of a region of interest (ROI) that contains the entire lesion in a single slice where the lesion is enhanced. Then the lesion volume from the volume data of interest, which is captured automatically, is separated. The core idea of BDACMLS is a new signed pressure function which is based solely on the intensity distribution combined with pathophysiological basis. To compare the algorithm results, two experienced radiologists delineated all lesions jointly to obtain the ground truth. In addition, results generated by other different methods based on level set (LS) are also compared with the authors’ method. Finally, the performance of the proposed method is evaluated by several region-based metrics such as the overlap ratio. Results: Forty-two studies with 46 lesions that contain 29 benign and 17 malignant lesions are evaluated. The dataset includes various typical pathologies of the breast such as invasive ductal carcinoma, ductal carcinomain situ, scar carcinoma, phyllodes tumor, breast cysts, fibroadenoma, etc. The overlap ratio for BDACMLS with respect to manual segmentation is 79.55% ± 12.60% (mean ± s.d.). Conclusions: A new active contour model method has been developed and shown to successfully segment breast DCE-MRI three-dimensional lesions. The results from this model correspond more closely to manual segmentation, solve the weak-edge-passed problem, and improve the robustness in segmenting different lesions.

Collaborative Filtering Based on Sequential Extraction of User-Item Clusters

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Honda, Katsuhiro; Notsu, Akira; Ichihashi, Hidetomo

Collaborative filtering is a computational realization of “word-of-mouth” in network community, in which the items prefered by “neighbors” are recommended. This paper proposes a new item-selection model for extracting user-item clusters from rectangular relation matrices, in which mutual relations between users and items are denoted in an alternative process of “liking or not”. A technique for sequential co-cluster extraction from rectangular relational data is given by combining the structural balancing-based user-item clustering method with sequential fuzzy cluster extraction appraoch. Then, the tecunique is applied to the collaborative filtering problem, in which some items may be shared by several user clusters.

The impact of sample size on the reproducibility of voxel-based lesion-deficit mappings.

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Lorca-Puls, Diego L; Gajardo-Vidal, Andrea; White, Jitrachote; Seghier, Mohamed L; Leff, Alexander P; Green, David W; Crinion, Jenny T; Ludersdorfer, Philipp; Hope, Thomas M H; Bowman, Howard; Price, Cathy J

2018-07-01

This study investigated how sample size affects the reproducibility of findings from univariate voxel-based lesion-deficit analyses (e.g., voxel-based lesion-symptom mapping and voxel-based morphometry). Our effect of interest was the strength of the mapping between brain damage and speech articulation difficulties, as measured in terms of the proportion of variance explained. First, we identified a region of interest by searching on a voxel-by-voxel basis for brain areas where greater lesion load was associated with poorer speech articulation using a large sample of 360 right-handed English-speaking stroke survivors. We then randomly drew thousands of bootstrap samples from this data set that included either 30, 60, 90, 120, 180, or 360 patients. For each resample, we recorded effect size estimates and p values after conducting exactly the same lesion-deficit analysis within the previously identified region of interest and holding all procedures constant. The results show (1) how often small effect sizes in a heterogeneous population fail to be detected; (2) how effect size and its statistical significance varies with sample size; (3) how low-powered studies (due to small sample sizes) can greatly over-estimate as well as under-estimate effect sizes; and (4) how large sample sizes (N ≥ 90) can yield highly significant p values even when effect sizes are so small that they become trivial in practical terms. The implications of these findings for interpreting the results from univariate voxel-based lesion-deficit analyses are discussed. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Information Clustering Based on Fuzzy Multisets.

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Miyamoto, Sadaaki

2003-01-01

Proposes a fuzzy multiset model for information clustering with application to information retrieval on the World Wide Web. Highlights include search engines; term clustering; document clustering; algorithms for calculating cluster centers; theoretical properties concerning clustering algorithms; and examples to show how the algorithms work.…

Parallel Density-Based Clustering for Discovery of Ionospheric Phenomena

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Pankratius, V.; Gowanlock, M.; Blair, D. M.

2015-12-01

Ionospheric total electron content maps derived from global networks of dual-frequency GPS receivers can reveal a plethora of ionospheric features in real-time and are key to space weather studies and natural hazard monitoring. However, growing data volumes from expanding sensor networks are making manual exploratory studies challenging. As the community is heading towards Big Data ionospheric science, automation and Computer-Aided Discovery become indispensable tools for scientists. One problem of machine learning methods is that they require domain-specific adaptations in order to be effective and useful for scientists. Addressing this problem, our Computer-Aided Discovery approach allows scientists to express various physical models as well as perturbation ranges for parameters. The search space is explored through an automated system and parallel processing of batched workloads, which finds corresponding matches and similarities in empirical data. We discuss density-based clustering as a particular method we employ in this process. Specifically, we adapt Density-Based Spatial Clustering of Applications with Noise (DBSCAN). This algorithm groups geospatial data points based on density. Clusters of points can be of arbitrary shape, and the number of clusters is not predetermined by the algorithm; only two input parameters need to be specified: (1) a distance threshold, (2) a minimum number of points within that threshold. We discuss an implementation of DBSCAN for batched workloads that is amenable to parallelization on manycore architectures such as Intel's Xeon Phi accelerator with 60+ general-purpose cores. This manycore parallelization can cluster large volumes of ionospheric total electronic content data quickly. Potential applications for cluster detection include the visualization, tracing, and examination of traveling ionospheric disturbances or other propagating phenomena. Acknowledgments. We acknowledge support from NSF ACI-1442997 (PI V. Pankratius).

Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading.

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Inano, Rika; Oishi, Naoya; Kunieda, Takeharu; Arakawa, Yoshiki; Yamao, Yukihiro; Shibata, Sumiya; Kikuchi, Takayuki; Fukuyama, Hidenao; Miyamoto, Susumu

2014-01-01

Gliomas are the most common intra-axial primary brain tumour; therefore, predicting glioma grade would influence therapeutic strategies. Although several methods based on single or multiple parameters from diagnostic images exist, a definitive method for pre-operatively determining glioma grade remains unknown. We aimed to develop an unsupervised method using multiple parameters from pre-operative diffusion tensor images for obtaining a clustered image that could enable visual grading of gliomas. Fourteen patients with low-grade gliomas and 19 with high-grade gliomas underwent diffusion tensor imaging and three-dimensional T1-weighted magnetic resonance imaging before tumour resection. Seven features including diffusion-weighted imaging, fractional anisotropy, first eigenvalue, second eigenvalue, third eigenvalue, mean diffusivity and raw T2 signal with no diffusion weighting, were extracted as multiple parameters from diffusion tensor imaging. We developed a two-level clustering approach for a self-organizing map followed by the K-means algorithm to enable unsupervised clustering of a large number of input vectors with the seven features for the whole brain. The vectors were grouped by the self-organizing map as protoclusters, which were classified into the smaller number of clusters by K-means to make a voxel-based diffusion tensor-based clustered image. Furthermore, we also determined if the diffusion tensor-based clustered image was really helpful for predicting pre-operative glioma grade in a supervised manner. The ratio of each class in the diffusion tensor-based clustered images was calculated from the regions of interest manually traced on the diffusion tensor imaging space, and the common logarithmic ratio scales were calculated. We then applied support vector machine as a classifier for distinguishing between low- and high-grade gliomas. Consequently, the sensitivity, specificity, accuracy and area under the curve of receiver operating characteristic

Mammographic evaluation of suspicious malignant lesions based on ACR(American College of Radiology) breast imaging reporting and data system(BI-RADS)

International Nuclear Information System (INIS)

Lee, Jei Hee; Oh, Ki Keun; Chang, So Yong; Kim, Eun Kyung; Kim, Mi Hye

1999-01-01

The purpose of this study was to assess the mammographic features and pathologic outcome of category 4 lesions using the Breast Imaging Reporting and Data System(BI-RADS), and to evaluate the significance of final assessment categories. Using BI-RADS, the interpretations of 8,134 mammograms acquired between January 1997 and May 1998 were categorized. From among 161 lesions categorized as '4' ('suspicious abnormality') and pathologically confirmed by surgery or biopsy, we analysed 113, found in 66 patients. The pathologic outcome of these 113 lesions was as follows:infiltrating ductal carcinoma, 17.7%(20/113); DCIS(ductal carcinoma in sitv), 8.0%(9/113); ADH(atypical ductal hyperplasia), 5.3%(6/113); DEH(ductal epithelial hyperplasia), 1.8%(2/113); ductectasia, 0.9%(1/113), FCD(fibrocystic change), 27.4%(31/113); firoadenoma, 7.1%(8/113); stromal fibrosis, 9.7%(11/113); normal parenchyma, 7.1%(8/113); other pathology, 15.0%(17/113). The most frequent mammographic features of BI-RADS category 4 lesions were irregular mass shape(41.2%), spiculated mass margin(52.3%), amorphous calcification(47.3%) and clustered calcification distribution(37.1%). Because category 4 lesions account for about 25.7% of all breast malignancies, mammographic lesions in this category ('suspicious abnormality') should be considered for supplementary study and breast biopsy rather than short-term follow-up. Initial pathologic findings can thus be confirmed

Utility of the k-means clustering algorithm in differentiating apparent diffusion coefficient values of benign and malignant neck pathologies.

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Srinivasan, A; Galbán, C J; Johnson, T D; Chenevert, T L; Ross, B D; Mukherji, S K

2010-04-01

Does the K-means algorithm do a better job of differentiating benign and malignant neck pathologies compared to only mean ADC? The objective of our study was to analyze the differences between ADC partitions to evaluate whether the K-means technique can be of additional benefit to whole-lesion mean ADC alone in distinguishing benign and malignant neck pathologies. MR imaging studies of 10 benign and 10 malignant proved neck pathologies were postprocessed on a PC by using in-house software developed in Matlab. Two neuroradiologists manually contoured the lesions, with the ADC values within each lesion clustered into 2 (low, ADC-ADC(L); high, ADC-ADC(H)) and 3 partitions (ADC(L); intermediate, ADC-ADC(I); ADC(H)) by using the K-means clustering algorithm. An unpaired 2-tailed Student t test was performed for all metrics to determine statistical differences in the means of the benign and malignant pathologies. A statistically significant difference between the mean ADC(L) clusters in benign and malignant pathologies was seen in the 3-cluster models of both readers (P = .03 and .022, respectively) and the 2-cluster model of reader 2 (P = .04), with the other metrics (ADC(H), ADC(I); whole-lesion mean ADC) not revealing any significant differences. ROC curves demonstrated the quantitative differences in mean ADC(H) and ADC(L) in both the 2- and 3-cluster models to be predictive of malignancy (2 clusters: P = .008, area under curve = 0.850; 3 clusters: P = .01, area under curve = 0.825). The K-means clustering algorithm that generates partitions of large datasets may provide a better characterization of neck pathologies and may be of additional benefit in distinguishing benign and malignant neck pathologies compared with whole-lesion mean ADC alone.

An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

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Qingming Zhan

2017-08-01

Full Text Available An adaptive spatial clustering (ASC algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram. It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need for prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.

Cross-layer cluster-based energy-efficient protocol for wireless sensor networks.

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Mammu, Aboobeker Sidhik Koyamparambil; Hernandez-Jayo, Unai; Sainz, Nekane; de la Iglesia, Idoia

2015-04-09

Recent developments in electronics and wireless communications have enabled the improvement of low-power and low-cost wireless sensors networks (WSNs). One of the most important challenges in WSNs is to increase the network lifetime due to the limited energy capacity of the network nodes. Another major challenge in WSNs is the hot spots that emerge as locations under heavy traffic load. Nodes in such areas quickly drain energy resources, leading to disconnection in network services. In such an environment, cross-layer cluster-based energy-efficient algorithms (CCBE) can prolong the network lifetime and energy efficiency. CCBE is based on clustering the nodes to different hexagonal structures. A hexagonal cluster consists of cluster members (CMs) and a cluster head (CH). The CHs are selected from the CMs based on nodes near the optimal CH distance and the residual energy of the nodes. Additionally, the optimal CH distance that links to optimal energy consumption is derived. To balance the energy consumption and the traffic load in the network, the CHs are rotated among all CMs. In WSNs, energy is mostly consumed during transmission and reception. Transmission collisions can further decrease the energy efficiency. These collisions can be avoided by using a contention-free protocol during the transmission period. Additionally, the CH allocates slots to the CMs based on their residual energy to increase sleep time. Furthermore, the energy consumption of CH can be further reduced by data aggregation. In this paper, we propose a data aggregation level based on the residual energy of CH and a cost-aware decision scheme for the fusion of data. Performance results show that the CCBE scheme performs better in terms of network lifetime, energy consumption and throughput compared to low-energy adaptive clustering hierarchy (LEACH) and hybrid energy-efficient distributed clustering (HEED).

Management of Energy Consumption on Cluster Based Routing Protocol for MANET

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Hosseini-Seno, Seyed-Amin; Wan, Tat-Chee; Budiarto, Rahmat; Yamada, Masashi

The usage of light-weight mobile devices is increasing rapidly, leading to demand for more telecommunication services. Consequently, mobile ad hoc networks and their applications have become feasible with the proliferation of light-weight mobile devices. Many protocols have been developed to handle service discovery and routing in ad hoc networks. However, the majority of them did not consider one critical aspect of this type of network, which is the limited of available energy in each node. Cluster Based Routing Protocol (CBRP) is a robust/scalable routing protocol for Mobile Ad hoc Networks (MANETs) and superior to existing protocols such as Ad hoc On-demand Distance Vector (AODV) in terms of throughput and overhead. Therefore, based on this strength, methods to increase the efficiency of energy usage are incorporated into CBRP in this work. In order to increase the stability (in term of life-time) of the network and to decrease the energy consumption of inter-cluster gateway nodes, an Enhanced Gateway Cluster Based Routing Protocol (EGCBRP) is proposed. Three methods have been introduced by EGCBRP as enhancements to the CBRP: improving the election of cluster Heads (CHs) in CBRP which is based on the maximum available energy level, implementing load balancing for inter-cluster traffic using multiple gateways, and implementing sleep state for gateway nodes to further save the energy. Furthermore, we propose an Energy Efficient Cluster Based Routing Protocol (EECBRP) which extends the EGCBRP sleep state concept into all idle member nodes, excluding the active nodes in all clusters. The experiment results show that the EGCBRP decreases the overall energy consumption of the gateway nodes up to 10% and the EECBRP reduces the energy consumption of the member nodes up to 60%, both of which in turn contribute to stabilizing the network.

Cross-Layer Cluster-Based Energy-Efficient Protocol for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Aboobeker Sidhik Koyamparambil Mammu

2015-04-01

Full Text Available Recent developments in electronics and wireless communications have enabled the improvement of low-power and low-cost wireless sensors networks (WSNs. One of the most important challenges in WSNs is to increase the network lifetime due to the limited energy capacity of the network nodes. Another major challenge in WSNs is the hot spots that emerge as locations under heavy traffic load. Nodes in such areas quickly drain energy resources, leading to disconnection in network services. In such an environment, cross-layer cluster-based energy-efficient algorithms (CCBE can prolong the network lifetime and energy efficiency. CCBE is based on clustering the nodes to different hexagonal structures. A hexagonal cluster consists of cluster members (CMs and a cluster head (CH. The CHs are selected from the CMs based on nodes near the optimal CH distance and the residual energy of the nodes. Additionally, the optimal CH distance that links to optimal energy consumption is derived. To balance the energy consumption and the traffic load in the network, the CHs are rotated among all CMs. In WSNs, energy is mostly consumed during transmission and reception. Transmission collisions can further decrease the energy efficiency. These collisions can be avoided by using a contention-free protocol during the transmission period. Additionally, the CH allocates slots to the CMs based on their residual energy to increase sleep time. Furthermore, the energy consumption of CH can be further reduced by data aggregation. In this paper, we propose a data aggregation level based on the residual energy of CH and a cost-aware decision scheme for the fusion of data. Performance results show that the CCBE scheme performs better in terms of network lifetime, energy consumption and throughput compared to low-energy adaptive clustering hierarchy (LEACH and hybrid energy-efficient distributed clustering (HEED.

Construction and application of Red5 cluster based on OpenStack

Science.gov (United States)

Wang, Jiaqing; Song, Jianxin

2017-08-01

With the application and development of cloud computing technology in various fields, the resource utilization rate of the data center has been improved obviously, and the system based on cloud computing platform has also improved the expansibility and stability. In the traditional way, Red5 cluster resource utilization is low and the system stability is poor. This paper uses cloud computing to efficiently calculate the resource allocation ability, and builds a Red5 server cluster based on OpenStack. Multimedia applications can be published to the Red5 cloud server cluster. The system achieves the flexible construction of computing resources, but also greatly improves the stability of the cluster and service efficiency.

Distributed Similarity based Clustering and Compressed Forwarding for wireless sensor networks.

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Arunraja, Muruganantham; Malathi, Veluchamy; Sakthivel, Erulappan

2015-11-01

Wireless sensor networks are engaged in various data gathering applications. The major bottleneck in wireless data gathering systems is the finite energy of sensor nodes. By conserving the on board energy, the life span of wireless sensor network can be well extended. Data communication being the dominant energy consuming activity of wireless sensor network, data reduction can serve better in conserving the nodal energy. Spatial and temporal correlation among the sensor data is exploited to reduce the data communications. Data similar cluster formation is an effective way to exploit spatial correlation among the neighboring sensors. By sending only a subset of data and estimate the rest using this subset is the contemporary way of exploiting temporal correlation. In Distributed Similarity based Clustering and Compressed Forwarding for wireless sensor networks, we construct data similar iso-clusters with minimal communication overhead. The intra-cluster communication is reduced using adaptive-normalized least mean squares based dual prediction framework. The cluster head reduces the inter-cluster data payload using a lossless compressive forwarding technique. The proposed work achieves significant data reduction in both the intra-cluster and the inter-cluster communications, with the optimal data accuracy of collected data. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A Data-origin Authentication Protocol Based on ONOS Cluster

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Qin Hua

2016-01-01

Full Text Available This paper is aim to propose a data-origin authentication protocol based on ONOS cluster. ONOS is a SDN controller which can work under a distributed environment. However, the security of an ONOS cluster is seldom considered, and the communication in an ONOS cluster may suffer from lots of security threats. In this paper, we used a two-tier self-renewable hash chain for identity authentication and data-origin authentication. We analyse the security and overhead of our proposal and made a comparison with current security measure. It showed that with the help of our proposal, communication in an ONOS cluster could be protected from identity forging, replay attacks, data tampering, MITM attacks and repudiation, also the computational overhead would decrease apparently.

Multi scales based sparse matrix spectral clustering image segmentation

Science.gov (United States)

Liu, Zhongmin; Chen, Zhicai; Li, Zhanming; Hu, Wenjin

2018-04-01

In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness.

Agent-based method for distributed clustering of textual information

Science.gov (United States)

Potok, Thomas E [Oak Ridge, TN; Reed, Joel W [Knoxville, TN; Elmore, Mark T [Oak Ridge, TN; Treadwell, Jim N [Louisville, TN

2010-09-28

A computer method and system for storing, retrieving and displaying information has a multiplexing agent (20) that calculates a new document vector (25) for a new document (21) to be added to the system and transmits the new document vector (25) to master cluster agents (22) and cluster agents (23) for evaluation. These agents (22, 23) perform the evaluation and return values upstream to the multiplexing agent (20) based on the similarity of the document to documents stored under their control. The multiplexing agent (20) then sends the document (21) and the document vector (25) to the master cluster agent (22), which then forwards it to a cluster agent (23) or creates a new cluster agent (23) to manage the document (21). The system also searches for stored documents according to a search query having at least one term and identifying the documents found in the search, and displays the documents in a clustering display (80) of similarity so as to indicate similarity of the documents to each other.

A novel clustering algorithm based on quantum games

International Nuclear Information System (INIS)

Li Qiang; He Yan; Jiang Jingping

2009-01-01

Enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum game with the problem of data clustering, and then develop a quantum-game-based clustering algorithm, in which data points in a dataset are considered as players who can make decisions and implement quantum strategies in quantum games. After each round of a quantum game, each player's expected payoff is calculated. Later, he uses a link-removing-and-rewiring (LRR) function to change his neighbors and adjust the strength of links connecting to them in order to maximize his payoff. Further, algorithms are discussed and analyzed in two cases of strategies, two payoff matrixes and two LRR functions. Consequently, the simulation results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the clustering algorithms have fast rates of convergence. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.

Green Clustering Implementation Based on DPS-MOPSO

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Yang Lu

2014-01-01

Full Text Available A green clustering implementation is proposed to be as the first method in the framework of an energy-efficient strategy for centralized enterprise high-density WLANs. Traditionally, to maintain the network coverage, all of the APs within the WLAN have to be powered on. Nevertheless, the new algorithm can power off a large proportion of APs while the coverage is maintained as the always-on counterpart. The proposed algorithm is composed of two parallel and concurrent procedures, which are the faster procedure based on K-means and the more accurate procedure based on Dynamic Population Size Multiple Objective Particle Swarm Optimization (DPS-MOPSO. To implement green clustering efficiently and accurately, dynamic population size and mutational operators are introduced as complements for the classical MOPSO. In addition to the function of AP selection, the new green clustering algorithm has another new function as the reference and guidance for AP deployment. This paper also presents simulations in scenarios modeled with ray-tracing method and FDTD technique, and the results show that about 67% up to 90% of energy consumption can be saved while the original network coverage is maintained during periods when few users are online or when the traffic load is low.

A quasiparticle-based multi-reference coupled-cluster method.

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Rolik, Zoltán; Kállay, Mihály

2014-10-07

The purpose of this paper is to introduce a quasiparticle-based multi-reference coupled-cluster (MRCC) approach. The quasiparticles are introduced via a unitary transformation which allows us to represent a complete active space reference function and other elements of an orthonormal multi-reference (MR) basis in a determinant-like form. The quasiparticle creation and annihilation operators satisfy the fermion anti-commutation relations. On the basis of these quasiparticles, a generalization of the normal-ordered operator products for the MR case can be introduced as an alternative to the approach of Mukherjee and Kutzelnigg [Recent Prog. Many-Body Theor. 4, 127 (1995); Mukherjee and Kutzelnigg, J. Chem. Phys. 107, 432 (1997)]. Based on the new normal ordering any quasiparticle-based theory can be formulated using the well-known diagram techniques. Beyond the general quasiparticle framework we also present a possible realization of the unitary transformation. The suggested transformation has an exponential form where the parameters, holding exclusively active indices, are defined in a form similar to the wave operator of the unitary coupled-cluster approach. The definition of our quasiparticle-based MRCC approach strictly follows the form of the single-reference coupled-cluster method and retains several of its beneficial properties. Test results for small systems are presented using a pilot implementation of the new approach and compared to those obtained by other MR methods.

Learning from unbalanced data: a cascade-based approach for detecting clustered microcalcifications.

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Bria, A; Karssemeijer, N; Tortorella, F

2014-02-01

Finding abnormalities in diagnostic images is a difficult task even for expert radiologists because the normal tissue locations largely outnumber those with suspicious signs which may thus be missed or incorrectly interpreted. For the same reason the design of a Computer-Aided Detection (CADe) system is very complex because the large predominance of normal samples in the training data may hamper the ability of the classifier to recognize the abnormalities on the images. In this paper we present a novel approach for computer-aided detection which faces the class imbalance with a cascade of boosting classifiers where each node is trained by a learning algorithm based on ranking instead of classification error. Such approach is used to design a system (CasCADe) for the automated detection of clustered microcalcifications (μCs), which is a severely unbalanced classification problem because of the vast majority of image locations where no μC is present. The proposed approach was evaluated with a dataset of 1599 full-field digital mammograms from 560 cases and compared favorably with the Hologic R2CAD ImageChecker, one of the most widespread commercial CADe systems. In particular, at the same lesion sensitivity of R2CAD (90%) on biopsy proven malignant cases, CasCADe and R2CAD detected 0.13 and 0.21 false positives per image (FPpi), respectively (p-value=0.09), whereas at the same FPpi of R2CAD (0.21), CasCADe and R2CAD detected 93% and 90% of true lesions respectively (p-value=0.11) thus showing that CasCADe can compete with high-end CADe commercial systems. Copyright © 2013 Elsevier B.V. All rights reserved.

Formal And Informal Macro-Regional Transport Clusters As A Primary Step In The Design And Implementation Of Cluster-Based Strategies

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Nežerenko Olga

2015-09-01

Full Text Available The aim of the study is the identification of a formal macro-regional transport and logistics cluster and its development trends on a macro-regional level in 2007-2011 by means of the hierarchical cluster analysis. The central approach of the study is based on two concepts: 1 the concept of formal and informal macro-regions, and 2 the concept of clustering which is based on the similarities shared by the countries of a macro-region and tightly related to the concept of macro-region. The authors seek to answer the question whether the formation of a formal transport cluster could provide the BSR a stable competitive position in the global transportation and logistics market.

Substructures in DAFT/FADA survey clusters based on XMM and optical data

Science.gov (United States)

Durret, F.; DAFT/FADA Team

2014-07-01

The DAFT/FADA survey was initiated to perform weak lensing tomography on a sample of 90 massive clusters in the redshift range [0.4,0.9] with HST imaging available. The complementary deep multiband imaging constitutes a high quality imaging data base for these clusters. In X-rays, we have analysed the XMM-Newton and/or Chandra data available for 32 clusters, and for 23 clusters we fit the X-ray emissivity with a beta-model and subtract it to search for substructures in the X-ray gas. This study was coupled with a dynamical analysis for the 18 clusters with at least 15 spectroscopic galaxy redshifts in the cluster range, based on a Serna & Gerbal (SG) analysis. We detected ten substructures in eight clusters by both methods (X-rays and SG). The percentage of mass included in substructures is found to be roughly constant with redshift, with values of 5-15%. Most of the substructures detected both in X-rays and with the SG method are found to be relatively recent infalls, probably at their first cluster pericenter approach.

Adaptive density trajectory cluster based on time and space distance

Science.gov (United States)

Liu, Fagui; Zhang, Zhijie

2017-10-01

There are some hotspot problems remaining in trajectory cluster for discovering mobile behavior regularity, such as the computation of distance between sub trajectories, the setting of parameter values in cluster algorithm and the uncertainty/boundary problem of data set. As a result, based on the time and space, this paper tries to define the calculation method of distance between sub trajectories. The significance of distance calculation for sub trajectories is to clearly reveal the differences in moving trajectories and to promote the accuracy of cluster algorithm. Besides, a novel adaptive density trajectory cluster algorithm is proposed, in which cluster radius is computed through using the density of data distribution. In addition, cluster centers and number are selected by a certain strategy automatically, and uncertainty/boundary problem of data set is solved by designed weighted rough c-means. Experimental results demonstrate that the proposed algorithm can perform the fuzzy trajectory cluster effectively on the basis of the time and space distance, and obtain the optimal cluster centers and rich cluster results information adaptably for excavating the features of mobile behavior in mobile and sociology network.

The role of rostral prefrontal cortex in prospective memory: a voxel-based lesion study.

Science.gov (United States)

Volle, Emmanuelle; Gonen-Yaacovi, Gil; Costello, Angela de Lacy; Gilbert, Sam J; Burgess, Paul W

2011-07-01

Patients with lesions in rostral prefrontal cortex (PFC) often experience problems in everyday-life situations requiring multitasking. A key cognitive component that is critical in multitasking situations is prospective memory, defined as the ability to carry out an intended action after a delay period filled with unrelated activity. The few functional imaging studies investigating prospective memory have shown consistent activation in both medial and lateral rostral PFC but also in more posterior prefrontal regions and non-frontal regions. The aim of this study was to determine regions that are necessary for prospective memory performance, using the human lesion approach. We designed an experimental paradigm allowing us to assess time-based (remembering to do something at a particular time) and event-based (remembering to do something in a particular situation) prospective memory, using two types of material, words and pictures. Time estimation tasks and tasks controlling for basic attention, inhibition and multiple instructions processing were also administered. We examined brain-behaviour relationships with a voxelwise lesion method in 45 patients with focal brain lesions and 107 control subjects using this paradigm. The results showed that lesions in the right polar prefrontal region (in Brodmann area 10) were specifically associated with a deficit in time-based prospective memory tasks for both words and pictures. This deficit could not be explained by impairments in basic attention, detection, inhibition or multiple instruction processing, and there was also no deficit in event-based prospective memory conditions. In addition to their prospective memory difficulties, these polar prefrontal patients were significantly impaired in time estimation ability compared to other patients. The same region was found to be involved using both words and pictures, suggesting that right rostral PFC plays a material nonspecific role in prospective memory. This is the first

Personalized PageRank Clustering: A graph clustering algorithm based on random walks

Science.gov (United States)

A. Tabrizi, Shayan; Shakery, Azadeh; Asadpour, Masoud; Abbasi, Maziar; Tavallaie, Mohammad Ali

2013-11-01

Graph clustering has been an essential part in many methods and thus its accuracy has a significant effect on many applications. In addition, exponential growth of real-world graphs such as social networks, biological networks and electrical circuits demands clustering algorithms with nearly-linear time and space complexity. In this paper we propose Personalized PageRank Clustering (PPC) that employs the inherent cluster exploratory property of random walks to reveal the clusters of a given graph. We combine random walks and modularity to precisely and efficiently reveal the clusters of a graph. PPC is a top-down algorithm so it can reveal inherent clusters of a graph more accurately than other nearly-linear approaches that are mainly bottom-up. It also gives a hierarchy of clusters that is useful in many applications. PPC has a linear time and space complexity and has been superior to most of the available clustering algorithms on many datasets. Furthermore, its top-down approach makes it a flexible solution for clustering problems with different requirements.

Novel Clustering Method Based on K-Medoids and Mobility Metric

Directory of Open Access Journals (Sweden)

Y. Hamzaoui

2018-06-01

Full Text Available The structure and constraint of MANETS influence negatively the performance of QoS, moreover the main routing protocols proposed generally operate in flat routing. Hence, this structure gives the bad results of QoS when the network becomes larger and denser. To solve this problem we use one of the most popular methods named clustering. The present paper comes within the frameworks of research to improve the QoS in MANETs. In this paper we propose a new algorithm of clustering based on the new mobility metric and K-Medoid to distribute the nodes into several clusters. Intuitively our algorithm can give good results in terms of stability of the cluster, and can also extend life time of cluster head.

Clustering economies based on multiple criteria decision making techniques

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Mansour Momeni

2011-10-01

Full Text Available One of the primary concerns on many countries is to determine different important factors affecting economic growth. In this paper, we study some factors such as unemployment rate, inflation ratio, population growth, average annual income, etc to cluster different countries. The proposed model of this paper uses analytical hierarchy process (AHP to prioritize the criteria and then uses a K-mean technique to cluster 59 countries based on the ranked criteria into four groups. The first group includes countries with high standards such as Germany and Japan. In the second cluster, there are some developing countries with relatively good economic growth such as Saudi Arabia and Iran. The third cluster belongs to countries with faster rates of growth compared with the countries located in the second group such as China, India and Mexico. Finally, the fourth cluster includes countries with relatively very low rates of growth such as Jordan, Mali, Niger, etc.

Unsupervised active learning based on hierarchical graph-theoretic clustering.

Science.gov (United States)

Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

2009-10-01

Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.

A robust approach based on Weibull distribution for clustering gene expression data

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Gong Binsheng

2011-05-01

Full Text Available Abstract Background Clustering is a widely used technique for analysis of gene expression data. Most clustering methods group genes based on the distances, while few methods group genes according to the similarities of the distributions of the gene expression levels. Furthermore, as the biological annotation resources accumulated, an increasing number of genes have been annotated into functional categories. As a result, evaluating the performance of clustering methods in terms of the functional consistency of the resulting clusters is of great interest. Results In this paper, we proposed the WDCM (Weibull Distribution-based Clustering Method, a robust approach for clustering gene expression data, in which the gene expressions of individual genes are considered as the random variables following unique Weibull distributions. Our WDCM is based on the concept that the genes with similar expression profiles have similar distribution parameters, and thus the genes are clustered via the Weibull distribution parameters. We used the WDCM to cluster three cancer gene expression data sets from the lung cancer, B-cell follicular lymphoma and bladder carcinoma and obtained well-clustered results. We compared the performance of WDCM with k-means and Self Organizing Map (SOM using functional annotation information given by the Gene Ontology (GO. The results showed that the functional annotation ratios of WDCM are higher than those of the other methods. We also utilized the external measure Adjusted Rand Index to validate the performance of the WDCM. The comparative results demonstrate that the WDCM provides the better clustering performance compared to k-means and SOM algorithms. The merit of the proposed WDCM is that it can be applied to cluster incomplete gene expression data without imputing the missing values. Moreover, the robustness of WDCM is also evaluated on the incomplete data sets. Conclusions The results demonstrate that our WDCM produces clusters

A Cluster-based Approach Towards Detecting and Modeling Network Dictionary Attacks

Directory of Open Access Journals (Sweden)

A. Tajari Siahmarzkooh

2016-12-01

Full Text Available In this paper, we provide an approach to detect network dictionary attacks using a data set collected as flows based on which a clustered graph is resulted. These flows provide an aggregated view of the network traffic in which the exchanged packets in the network are considered so that more internally connected nodes would be clustered. We show that dictionary attacks could be detected through some parameters namely the number and the weight of clusters in time series and their evolution over the time. Additionally, the Markov model based on the average weight of clusters,will be also created. Finally, by means of our suggested model, we demonstrate that artificial clusters of the flows are created for normal and malicious traffic. The results of the proposed approach on CAIDA 2007 data set suggest a high accuracy for the model and, therefore, it provides a proper method for detecting the dictionary attack.

IDENTIFICAÇÃO DE CLUSTERS INTERNACIONAIS COM BASE NAS DIMENSÕES CULTURAIS DE HOFSTEDE. / Identification of international clusters based on the hofstede’s cultural dimensions

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Valderí de Castro Alcântara1

2012-08-01

Full Text Available Haja vista que a cultura de um país influencia a cultura organizacional das empresas nele presente e ainda é fator determinante no processo de internacionalização, torna-se relevante compreender e mensurar as características culturais de cada país. Os estudos de Hofstede (1984 apresentam uma metodologia útil para comparação entre culturas. Tal metodologia leva em consideração as características deuma cultura que possibilita diferenciar um país de outro. Dessa forma, é possível observar que determinados países compartilham certos traços culturais e, assim, é possível agrupá-los segundo critérios pré-estabelecidos. O presente trabalho objetiva utilizar-se de procedimentos estatísticos multivariados Clusters Analyses, K-Means Cluster Analysis e Análise Discriminante para determinar e validar agrupamentos de países, com base nas dimensões culturais de Hofstede (Distance Index, Individualism, Masculinity e Uncertainty Avoidance Index. Os resultados determinaram quatro clusters: Cluster 1 - países com cultura masculina e individualista; Cluster 2 - cultura coletivista e aversa à incerteza; Cluster 3 - cultura feminina e com baixa distância hierárquica; e Cluster 4 - cultura com elevada distância hierárquica e propensão à incerteza./ Considering that the culture of a country influences the organizational culture of this company and it is still a determining factor in the internationalization process becomes important to understand and measure the cultural characteristics of each country. The studies of Hofstede (1984 present a useful methodology for comparing cultures, this methodology takes into account the characteristics of a culturethat allows to differentiate one from another country. Thus one can observe that certain countries share certain cultural traits and so it is possible grouping them according to predetermined criteria. The present work aims to utilize multivariate statistical procedures Cluster Analyses

Lesion activity assessment

DEFF Research Database (Denmark)

Ekstrand, K R; Zero, D T; Martignon, S

2009-01-01

in response to cariogenic plaque as well as lesion arrest. Based on this understanding, different clinical scoring systems have been developed to assess the severity/depth and activity of lesions. A recent system has been devised by the International Caries Detection and Assessment System Committee...

PROSPECTS OF THE REGIONAL INTEGRATION POLICY BASED ON CLUSTER FORMATION

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Elena Tsepilova

2018-01-01

Full Text Available The purpose of this article is to develop the theoretical foundations of regional integration policy and to determine its prospects on the basis of cluster formation. The authors use such research methods as systematization, comparative and complex analysis, synthesis, statistical method. Within the framework of the research, the concept of regional integration policy is specified, and its integration core – cluster – is allocated. The authors work out an algorithm of regional clustering, which will ensure the growth of economy and tax income. Measures have been proposed to optimize the organizational mechanism of interaction between the participants of the territorial cluster and the authorities that allow to ensure the effective functioning of clusters, including taxation clusters. Based on the results of studying the existing methods for assessing the effectiveness of cluster policy, the authors propose their own approach to evaluating the consequences of implementing the regional integration policy, according to which the list of quantitative and qualitative indicators is defined. The present article systematizes the experience and results of the cluster policy of certain European countries, that made it possible to determine the prospects and synergetic effect from the development of clusters as an integration foundation of regional policy in the Russian Federation. The authors carry out the analysis of activity of cluster formations using the example of the Rostov region – a leader in the formation of conditions for the cluster policy development in the Southern Federal District. 11 clusters and cluster initiatives are developing in this region. As a result, the authors propose measures for support of the already existing clusters and creation of the new ones.

A Web service substitution method based on service cluster nets

Science.gov (United States)

Du, YuYue; Gai, JunJing; Zhou, MengChu

2017-11-01

Service substitution is an important research topic in the fields of Web services and service-oriented computing. This work presents a novel method to analyse and substitute Web services. A new concept, called a Service Cluster Net Unit, is proposed based on Web service clusters. A service cluster is converted into a Service Cluster Net Unit. Then it is used to analyse whether the services in the cluster can satisfy some service requests. Meanwhile, the substitution methods of an atomic service and a composite service are proposed. The correctness of the proposed method is proved, and the effectiveness is shown and compared with the state-of-the-art method via an experiment. It can be readily applied to e-commerce service substitution to meet the business automation needs.

Characterizing stroke lesions using digital templates and lesion quantification tools in a web-based imaging informatics system for a large-scale stroke rehabilitation clinical trial

Science.gov (United States)

Wang, Ximing; Edwardson, Matthew; Dromerick, Alexander; Winstein, Carolee; Wang, Jing; Liu, Brent

2015-03-01

Previously, we presented an Interdisciplinary Comprehensive Arm Rehabilitation Evaluation (ICARE) imaging informatics system that supports a large-scale phase III stroke rehabilitation trial. The ePR system is capable of displaying anonymized patient imaging studies and reports, and the system is accessible to multiple clinical trial sites and users across the United States via the web. However, the prior multicenter stroke rehabilitation trials lack any significant neuroimaging analysis infrastructure. In stroke related clinical trials, identification of the stroke lesion characteristics can be meaningful as recent research shows that lesion characteristics are related to stroke scale and functional recovery after stroke. To facilitate the stroke clinical trials, we hope to gain insight into specific lesion characteristics, such as vascular territory, for patients enrolled into large stroke rehabilitation trials. To enhance the system's capability for data analysis and data reporting, we have integrated new features with the system: a digital brain template display, a lesion quantification tool and a digital case report form. The digital brain templates are compiled from published vascular territory templates at each of 5 angles of incidence. These templates were updated to include territories in the brainstem using a vascular territory atlas and the Medical Image Processing, Analysis and Visualization (MIPAV) tool. The digital templates are displayed for side-by-side comparisons and transparent template overlay onto patients' images in the image viewer. The lesion quantification tool quantifies planimetric lesion area from user-defined contour. The digital case report form stores user input into a database, then displays contents in the interface to allow for reviewing, editing, and new inputs. In sum, the newly integrated system features provide the user with readily-accessible web-based tools to identify the vascular territory involved, estimate lesion area

Mammographic evaluation of suspicious malignant lesions based on ACR(American College of Radiology) breast imaging reporting and data system(BI-RADS)

Energy Technology Data Exchange (ETDEWEB)

Lee, Jei Hee; Oh, Ki Keun; Chang, So Yong; Kim, Eun Kyung; Kim, Mi Hye [Yonsei Univ. College of Medicine, Research Institute of Radiological Science, Yonsei Univ., Seoul (Korea, Republic of)

1999-12-01

The purpose of this study was to assess the mammographic features and pathologic outcome of category 4 lesions using the Breast Imaging Reporting and Data System(BI-RADS), and to evaluate the significance of final assessment categories. Using BI-RADS, the interpretations of 8,134 mammograms acquired between January 1997 and May 1998 were categorized. From among 161 lesions categorized as '4' ('suspicious abnormality') and pathologically confirmed by surgery or biopsy, we analysed 113, found in 66 patients. The pathologic outcome of these 113 lesions was as follows:infiltrating ductal carcinoma, 17.7%(20/113); DCIS(ductal carcinoma in sitv), 8.0%(9/113); ADH(atypical ductal hyperplasia), 5.3%(6/113); DEH(ductal epithelial hyperplasia), 1.8%(2/113); ductectasia, 0.9%(1/113), FCD(fibrocystic change), 27.4%(31/113); firoadenoma, 7.1%(8/113); stromal fibrosis, 9.7%(11/113); normal parenchyma, 7.1%(8/113); other pathology, 15.0%(17/113). The most frequent mammographic features of BI-RADS category 4 lesions were irregular mass shape(41.2%), spiculated mass margin(52.3%), amorphous calcification(47.3%) and clustered calcification distribution(37.1%). Because category 4 lesions account for about 25.7% of all breast malignancies, mammographic lesions in this category ('suspicious abnormality') should be considered for supplementary study and breast biopsy rather than short-term follow-up. Initial pathologic findings can thus be confirmed.

TRUSTWORTHY OPTIMIZED CLUSTERING BASED TARGET DETECTION AND TRACKING FOR WIRELESS SENSOR NETWORK

Directory of Open Access Journals (Sweden)

C. Jehan

2016-06-01

Full Text Available In this paper, an efficient approach is proposed to address the problem of target tracking in wireless sensor network (WSN. The problem being tackled here uses adaptive dynamic clustering scheme for tracking the target. It is a specific problem in object tracking. The proposed adaptive dynamic clustering target tracking scheme uses three steps for target tracking. The first step deals with the identification of clusters and cluster heads using OGSAFCM. Here, kernel fuzzy c-means (KFCM and gravitational search algorithm (GSA are combined to create clusters. At first, oppositional gravitational search algorithm (OGSA is used to optimize the initial clustering center and then the KFCM algorithm is availed to guide the classification and the cluster formation process. In the OGSA, the concept of the opposition based population initialization in the basic GSA to improve the convergence profile. The identified clusters are changed dynamically. The second step deals with the data transmission to the cluster heads. The third step deals with the transmission of aggregated data to the base station as well as the detection of target. From the experimental results, the proposed scheme efficiently and efficiently identifies the target. As a result the tracking error is minimized.

Dynamic Characteristics Analysis and Stabilization of PV-Based Multiple Microgrid Clusters

DEFF Research Database (Denmark)

Zhao, Zhuoli; Yang, Ping; Wang, Yuewu

2018-01-01

-based multiple microgrid clusters. A detailed small-signal model for PV-based microgrid clusters considering local adaptive dynamic droop control mechanism of the voltage-source PV system is developed. The complete dynamic model is then used to access and compare the dynamic characteristics of the single...... microgrid and interconnected microgrids. In order to enhance system stability of the PV microgrid clusters, a tie-line flow and stabilization strategy is proposed to suppress the introduced interarea and local oscillations. Robustly selecting of the key control parameters is transformed to a multiobjective......As the penetration of PV generation increases, there is a growing operational demand on PV systems to participate in microgrid frequency regulation. It is expected that future distribution systems will consist of multiple microgrid clusters. However, interconnecting PV microgrids may lead to system...

Mass lesions in chronic pancreatitis: benign or malignant? An "evidence-based practice" approach.

LENUS (Irish Health Repository)

Gerstenmaier, Jan F

2012-02-01

The diagnosis of a pancreatic mass lesion in the presence of chronic pancreatitis can be extremely challenging. At the same time, a high level of certainty about the diagnosis is necessary for appropriate management planning. The aim of this study was to establish current best evidence about which imaging methods reliably differentiate a benign from a malignant lesion, and show how that evidence is best applied. A diagnostic algorithm based on Bayesian analysis is proposed.

An improved initialization center k-means clustering algorithm based on distance and density

Science.gov (United States)

Duan, Yanling; Liu, Qun; Xia, Shuyin

2018-04-01

Aiming at the problem of the random initial clustering center of k means algorithm that the clustering results are influenced by outlier data sample and are unstable in multiple clustering, a method of central point initialization method based on larger distance and higher density is proposed. The reciprocal of the weighted average of distance is used to represent the sample density, and the data sample with the larger distance and the higher density are selected as the initial clustering centers to optimize the clustering results. Then, a clustering evaluation method based on distance and density is designed to verify the feasibility of the algorithm and the practicality, the experimental results on UCI data sets show that the algorithm has a certain stability and practicality.

Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance.

Science.gov (United States)

Du, Xiangjun; Shao, Fengjing; Wu, Shunyao; Zhang, Hanlin; Xu, Si

2017-07-01

Water quality assessment is crucial for assessment of marine eutrophication, prediction of harmful algal blooms, and environment protection. Previous studies have developed many numeric modeling methods and data driven approaches for water quality assessment. The cluster analysis, an approach widely used for grouping data, has also been employed. However, there are complex correlations between water quality variables, which play important roles in water quality assessment but have always been overlooked. In this paper, we analyze correlations between water quality variables and propose an alternative method for water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Further, we cluster water quality data collected form coastal water of Bohai Sea and North Yellow Sea of China, and apply clustering results to evaluate its water quality. To evaluate the validity, we also cluster the water quality data with cluster analysis based on Euclidean distance, which are widely adopted by previous studies. The results show that our method is more suitable for water quality assessment with many correlated water quality variables. To our knowledge, it is the first attempt to apply Mahalanobis distance for coastal water quality assessment.

Intraosseous osteolytic lesions

Energy Technology Data Exchange (ETDEWEB)

Adler, C.P.; Wenz, W.

1981-10-01

Any pathological damage occurring in a bone will produce either an osteolytic or osteosclerotic lesion which can be seen in the macroscopic specimen as well as in the roentgenogram. Various bone lesions may lead to local destructions of the bone. An osteoma or osteoplastic osteosarcoma produces an osteosclerotic lesion showing a dense mass in the roentgenogram; a chondroblastoma or an osteoclastoma, on the other hand, induces an osteolytic focal lesion. This paper presents examples of different osteolytic lesions of the humerus. An osteolytic lesion seen in the roentgenogram may be either produced by an underlying non-ossifying fibroma of the bone, by fibrous dysplasia, osteomyelitis or Ewing's sarcoma. Differential diagnostic considerations based on the radiological picture include eosinophilic bone granuloma, juvenile or aneurysmal bone cyst, multiple myeloma or bone metastases. Serious differential diagnostic problems may be involved in case of osteolytic lesions occurring in the humerus. Cases of this type involving complications have been reported and include the presence of an teleangiectatic osteosarcoma as well as that of a hemangiosarcoma of the bone.

Inhomogeneity of epidemic spreading with entropy-based infected clusters.

Science.gov (United States)

Wen-Jie, Zhou; Xing-Yuan, Wang

2013-12-01

Considering the difference in the sizes of the infected clusters in the dynamic complex networks, the normalized entropy based on infected clusters (δ*) is proposed to characterize the inhomogeneity of epidemic spreading. δ* gives information on the variability of the infected clusters in the system. We investigate the variation in the inhomogeneity of the distribution of the epidemic with the absolute velocity v of moving agent, the infection density ρ, and the interaction radius r. By comparing δ* in the dynamic networks with δH* in homogeneous mode, the simulation experiments show that the inhomogeneity of epidemic spreading becomes smaller with the increase of v, ρ, r.

Ionized-cluster source based on high-pressure corona discharge

International Nuclear Information System (INIS)

Lokuliyanage, K.; Huber, D.; Zappa, F.; Scheier, P.

2006-01-01

Full text: It has been demonstrated that energetic beams of large clusters, with thousands of atoms, can be a powerful tool for surface modification. Normally ionized cluster beams are obtained by electron impact on neutral beams produced in a supersonic expansion. At the University of Innsbruck we are pursuing the realization of a high current cluster ion source based on the corona discharge.The idea in the present case is that the ionization should occur prior to the supersonic expansion, thus supersede the need of subsequent electron impact. In this contribution we present the project of our source in its initial stage. The intensity distribution of cluster sizes as a function of the source parameters, such as input pressure, temperature and gap voltage, are investigated with the aid of a custom-built time of flight mass spectrometer. (author)

COMPARISON AND EVALUATION OF CLUSTER BASED IMAGE SEGMENTATION TECHNIQUES

OpenAIRE

Hetangi D. Mehta*, Daxa Vekariya, Pratixa Badelia

2017-01-01

Image segmentation is the classification of an image into different groups. Numerous algorithms using different approaches have been proposed for image segmentation. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity. A review is done on different types of clustering methods used for image segmentation. Also a methodology is proposed to classify and quantify different clustering algorithms based on their consistency in different...

A Coupled User Clustering Algorithm Based on Mixed Data for Web-Based Learning Systems

Directory of Open Access Journals (Sweden)

Ke Niu

2015-01-01

Full Text Available In traditional Web-based learning systems, due to insufficient learning behaviors analysis and personalized study guides, a few user clustering algorithms are introduced. While analyzing the behaviors with these algorithms, researchers generally focus on continuous data but easily neglect discrete data, each of which is generated from online learning actions. Moreover, there are implicit coupled interactions among the data but are frequently ignored in the introduced algorithms. Therefore, a mass of significant information which can positively affect clustering accuracy is neglected. To solve the above issues, we proposed a coupled user clustering algorithm for Wed-based learning systems by taking into account both discrete and continuous data, as well as intracoupled and intercoupled interactions of the data. The experiment result in this paper demonstrates the outperformance of the proposed algorithm.

Model-based Clustering of Categorical Time Series with Multinomial Logit Classification

Science.gov (United States)

Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea

2010-09-01

A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.

Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon

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Moumita Saha

2016-01-01

Full Text Available Forecasting the Indian summer monsoon is a challenging task due to its complex and nonlinear behavior. A large number of global climatic variables with varying interaction patterns over years influence monsoon. Various statistical and neural prediction models have been proposed for forecasting monsoon, but many of them fail to capture variability over years. The skill of predictor variables of monsoon also evolves over time. In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. It groups the years based on prevailing global climatic condition using statistical clustering technique and subsequently for each such group it identifies significant climatic predictor variables which assist in better prediction. Prediction model is designed to frame individual cluster using random forest of regression tree. Prediction of aggregate and regional monsoon is attempted. Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. Errors in predicting the regional monsoons are also comparable in comparison to the high variation of regional precipitation. Proposed joint-clustering based ensemble model is observed to be superior to existing monsoon prediction models and it also surpasses general nonclustering based prediction models.

Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments

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Wen Liu

2016-12-01

Full Text Available Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS. Due to the absence of satellite signal in Global Navigation Satellite System (GNSS, various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP, which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC, is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1 and the XiDan Joy City (Floors 1 and 2, as Test-bed 2, and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means.

Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments.

Science.gov (United States)

Liu, Wen; Fu, Xiao; Deng, Zhongliang

2016-12-02

Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS). Due to the absence of satellite signal in Global Navigation Satellite System (GNSS), various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP), which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC), is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1) and the XiDan Joy City (Floors 1 and 2, as Test-bed 2), and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means.

Comparison of Skin Moisturizer: Consumer-Based Brand Equity (CBBE Factors in Clusters Based on Consumer Ethnocentrism

Directory of Open Access Journals (Sweden)

Yossy Hanna Garlina

2014-09-01

Full Text Available This research aims to analyze relevant factors contributing to the four dimensions of consumer-based brand equity in skin moisturizer industry. It is then followed by the clustering of female consumers of skin moisturizer based on ethnocentrism and differentiating each cluster’s consumer-based brand equity dimensions towards a domestic skin moisturizer brand Mustika Ratu, skin moisturizer. Research used descriptive survey method analysis. Primary data was obtained through questionnaire distribution to 70 female respondents for factor analysis and 120 female respondents for cluster analysis and one way analysis of variance (ANOVA. This research employed factor analysis to obtain relevant factors contributing to the five dimensions of consumer-based brand equity in skin moisturizer industry. Cluster analysis and one way analysis of variance (ANOVA were to see the difference of consumer-based brand equity between highly ethnocentric consumer and low ethnocentric consumer towards the same skin moisturizer domestic brand, Mustika Ratu skin moisturizer. Research found in all individual dimension analysis, all variable means and individual means show distinct difference between the high ethnocentric consumer and the low ethnocentric consumer. The low ethnocentric consumer cluster tends to be lower in mean score of Brand Loyalty, Perceived Quality, Brand Awareness, Brand Association, and Overall Brand Equity than the high ethnocentric consumer cluster. Research concludes consumer ethnocentrism is positively correlated with preferences towards domestic products and negatively correlated with foreign-made product preference. It is, then, highly ethnocentric consumers have positive perception towards domestic product.

[Evidence-based therapy for cartilage lesions in the knee - regenerative treatment options].

Science.gov (United States)

Proffen, B; von Keudell, A; Vavken, P

2012-06-01

The treatment of cartilage defects has seen a shift from replacement to regeneration in the last few years. The rationale behind this development is the improvement in the quality-of-care for the growing segment of young patients who are prone to arthroplasty complications because of their specific characteristics - young age, high level of activity, high demand for functionality. These days, two of the most popular regenerative treatments are microfracture and autologous chondrocyte implantation (ACI). Although these new options show promising results, no final algorithm for the treatment of cartilage lesions has been established as yet. The objective of this review is to describe and compare these two treatment options and to present an evidence-based treatment algorithm for focal cartilage defects. Microfracture is a cost-effective, arthroscopic one-stage procedure, in which by drilling of the subchondral plate, mesenchymal stem cells from the bone marrow migrate into the defect and rebuild the cartilage. ACI is a two-stage procedure in which first chondrocytes are harvested, expanded in cell culture and in a second open procedure reimplanted into the cartilage defect. Microfracture is usually used for focal cartilage defects osteophyte, and for the ACI patient, periosteal hypertrophy and the need for two procedures in ACI. Only a few studies provide detailed and evidence-based information on a comparative assessment. These studies, however, are showing widely similar clinical outcomes but better histological results for ACI, which are likely to translate into better long-term outcomes. Although evidence-based studies comparing microfracture and ACI have not found significant differences in the clinical outcome, the literature does show that choosing the treatment based on the size and characteristics of the osteochondral lesion might be beneficial. The American Association of Orthopedic Surgeons suggest that contained lesions < 4 cm2 should be treated by

Cytomorphologic features of papillary lesions of the male breast: a study of 11 cases.

Science.gov (United States)

Reid-Nicholson, Michelle D; Tong, Guoxia; Cangiarella, Joan F; Moreira, Andre L

2006-08-25

Breast masses occur in men far less commonly than women and are infrequently subjected to fine-needle aspiration (FNA) biopsy. Papillary lesions of the male breast are rare and are comprised of a spectrum of lesions ranging from papillary hyperplasia in gynecomastia to invasive papillary carcinoma. The following study describes the cytomorphology of papillary breast lesions in 11 men. The patients ranged in age from 23 to 78 years old and each presented with an unilateral subareolar or periareolar breast mass that varied in size from 0.5 to 3 cm. Two patients presented with bloody nipple discharge. Archival material (8-year period) from FNA biopsies of papillary lesions of the male breast was reviewed. The reviewed cases were correlated with appropriate clinicopathologic follow-up. The smears had variable cellularity but all showed papillary clusters of mammary epithelial cells with and without fibrovascular cores. Single epithelial cells with a high nuclear-to-cytoplasmic ratio and eccentric nuclei were seen in all smears; however, these were more numerous in cases of adenocarcinoma. Hemosiderin-laden macrophages were present in all cases. Nipple discharge was seen only in the 2 benign lesions. All adenocarcinomas occurred in older men. The only cytologic criteria that differentiated benign from malignant papillary lesions were marked cellularity and the presence of abundant 3-dimensional clusters. To the best of the authors' knowledge, the current series is the largest in the English literature to date that examines the cytomorphologic features of papillary breast lesions in men. Copyright 2006 American Cancer Society.

FRCA: A Fuzzy Relevance-Based Cluster Head Selection Algorithm for Wireless Mobile Ad-Hoc Sensor Networks

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Taegwon Jeong

2011-05-01

Full Text Available Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP, the Weighted-based Adaptive Clustering Algorithm (WACA, and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM. The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

FRCA: a fuzzy relevance-based cluster head selection algorithm for wireless mobile ad-hoc sensor networks.

Science.gov (United States)

Lee, Chongdeuk; Jeong, Taegwon

2011-01-01

Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

An Epidemiological Study of Oral Mucosal Lesions in Karnataka State, India

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K. V. V Prasad

2004-01-01

Full Text Available This article presents oral mucosal lesions findings from a state oral health survey of Karnataka, India. A total of 46,579 subjects aged 1-4 to 65+ years were selected by using multistage-cluster-stratified random sampling method and subjects were examined by 32 dentists trained in standardized clinical diagnostic criteria for oral mucosal lesions. In the present study, 7.53% of subjects had one or more oral mucosal lesions, in which, male subjects (9.41 % had a significantly higher prevalence of lesions compared to female subjects (4.38%; urban subjects (11.61% had a significantly higher prevalence than rural subjects (5.01 % and the Christian subjects had a significantly higher prevalence of lesions than the Hindus, Muslims and others (F=211.594, <0.001, S. The observed prevalence of oral mucosal lesions increased with age (r=0.8174, P<0.05, S, which is statistically significant. The most prevalent lesions observed were Leukoplakia (1.73%, Lichen planus (2.02% Ulceration (0.73%, Candidiasis (0.94% and Abscess (1.05%. The maximum number of lesions was seen in sulci (7.33% and the minimum number of lesions was seen in lips (0.02%. Differences in prevalence were analyzed by sex, religion, location and geographical area.

AES based secure low energy adaptive clustering hierarchy for WSNs

Science.gov (United States)

Kishore, K. R.; Sarma, N. V. S. N.

2013-01-01

Wireless sensor networks (WSNs) provide a low cost solution in diversified application areas. The wireless sensor nodes are inexpensive tiny devices with limited storage, computational capability and power. They are being deployed in large scale in both military and civilian applications. Security of the data is one of the key concerns where large numbers of nodes are deployed. Here, an energy-efficient secure routing protocol, secure-LEACH (Low Energy Adaptive Clustering Hierarchy) for WSNs based on the Advanced Encryption Standard (AES) is being proposed. This crypto system is a session based one and a new session key is assigned for each new session. The network (WSN) is divided into number of groups or clusters and a cluster head (CH) is selected among the member nodes of each cluster. The measured data from the nodes is aggregated by the respective CH's and then each CH relays this data to another CH towards the gateway node in the WSN which in turn sends the same to the Base station (BS). In order to maintain confidentiality of data while being transmitted, it is necessary to encrypt the data before sending at every hop, from a node to the CH and from the CH to another CH or to the gateway node.

REGIONAL DEVELOPMENT BASED ON CLUSTER IN LIVESTOCK DEVELOPMENT. CLUSTER IN LIVESTOCK SECTOR IN THE KYRGYZ REPUBLIC

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Meerim SYDYKOVA

2014-11-01

Full Text Available In most developing countries, where agriculture is the main economical source, clusters have been found as a booster to develop their economy. The Asian countries are now starting to implement agro-food clusters into the mainstream of changes in agriculture, farming and food industry. The long-term growth of meat production in the Kyrgyz Republic during the last decade, as well as the fact that agriculture has become one of the prioritized sectors of the economy, proved the importance of livestock sector in the economy of the Kyrgyz Republic. The research question is “Does the Kyrgyz Republic has strong economic opportunities and prerequisites in agriculture in order to implement an effective agro cluster in the livestock sector?” Paper focuses on describing the prerequisites of the Kyrgyz Republic in agriculture to implement livestock cluster. The main objective of the paper is to analyse the livestock sector of the Kyrgyz Republic and observe the capacity of this sector to implement agro-cluster. The study focuses on investigating livestock sector and a complex S.W.O.T. The analysis was carried out based on local and regional database and official studies. The results of research demonstrate the importance of livestock cluster for national economy. It can be concluded that cluster implementation could provide to its all members with benefits if they could build strong collaborative relationship in order to facilitate the access to the labour market and implicitly, the access to exchange of good practices. Their ability of potential cluster members to act as a convergence pole is critical for acquiring practical skills necessary for the future development of the livestock sector.

The association of hoof lesions at claw trimming with test-day milk yield in Danish Holsteins

DEFF Research Database (Denmark)

Ettema, Jehan Frans; Capion, Nynne; Hill, Ashley E.

2007-01-01

on the stage of lactation in which the lesions were diagnosed; during the first 100 days in milk, between days 101 and 200 and between days 201 and 305. Associations between the presence of hoof lesions at trimming and milk production were analyzed by linear mixed modeling at the cow level, clustered within...

Feature Selection and Kernel Learning for Local Learning-Based Clustering.

Science.gov (United States)

Zeng, Hong; Cheung, Yiu-ming

2011-08-01

The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.

Galaxy clusters in the SDSS Stripe 82 based on photometric redshifts

International Nuclear Information System (INIS)

Durret, F.; Adami, C.; Bertin, E.; Hao, J.; Márquez, I.

2015-01-01

Based on a recent photometric redshift galaxy catalogue, we have searched for galaxy clusters in the Stripe ~82 region of the Sloan Digital Sky Survey by applying the Adami & MAzure Cluster FInder (AMACFI). Extensive tests were made to fine-tune the AMACFI parameters and make the cluster detection as reliable as possible. The same method was applied to the Millennium simulation to estimate our detection efficiency and the approximate masses of the detected clusters. Considering all the cluster galaxies (i.e. within a 1 Mpc radius of the cluster to which they belong and with a photoz differing by less than 0.05 from that of the cluster), we stacked clusters in various redshift bins to derive colour-magnitude diagrams and galaxy luminosity functions (GLFs). For each galaxy with absolute magnitude brighter than -19.0 in the r band, we computed the disk and spheroid components by applying SExtractor, and by stacking clusters we determined how the disk-to-spheroid flux ratio varies with cluster redshift and mass. We also detected 3663 clusters in the redshift range 0.15< z<0.70, with estimated mean masses between 10"1"3 and a few 10"1"4 solar masses. Furthermore, by stacking the cluster galaxies in various redshift bins, we find a clear red sequence in the (g'-r') versus r' colour-magnitude diagrams, and the GLFs are typical of clusters, though with a possible contamination from field galaxies. The morphological analysis of the cluster galaxies shows that the fraction of late-type to early-type galaxies shows an increase with redshift (particularly in high mass clusters) and a decrease with detection level, i.e. cluster mass. From the properties of the cluster galaxies, the majority of the candidate clusters detected here seem to be real clusters with typical cluster properties.

Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks

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Gulnaz Ahmed

2017-02-01

Full Text Available The longer network lifetime of Wireless Sensor Networks (WSNs is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED clustering, Artificial Bee Colony (ABC, Zone Based Routing (ZBR, and Centralized Energy Efficient Clustering (CEEC using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput.

Cluster-based localization and tracking in ubiquitous computing systems

CERN Document Server

Martínez-de Dios, José Ramiro; Torres-González, Arturo; Ollero, Anibal

2017-01-01

Localization and tracking are key functionalities in ubiquitous computing systems and techniques. In recent years a very high variety of approaches, sensors and techniques for indoor and GPS-denied environments have been developed. This book briefly summarizes the current state of the art in localization and tracking in ubiquitous computing systems focusing on cluster-based schemes. Additionally, existing techniques for measurement integration, node inclusion/exclusion and cluster head selection are also described in this book.

Support Policies in Clusters: Prioritization of Support Needs by Cluster Members According to Cluster Life Cycle

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Gulcin Salıngan

2012-07-01

Full Text Available Economic development has always been a moving target. Both the national and local governments have been facing the challenge of implementing the effective and efficient economic policy and program in order to best utilize their limited resources. One of the recent approaches in this area is called cluster-based economic analysis and strategy development. This study reviews key literature and some of the cluster based economic policies adopted by different governments. Based on this review, it proposes “the cluster life cycle” as a determining factor to identify the support requirements of clusters. A survey, designed based on literature review of International Cluster support programs, was conducted with 30 participants from 3 clusters with different maturity stage. This paper discusses the results of this study conducted among the cluster members in Eskişehir- Bilecik-Kütahya Region in Turkey on the requirement of the support to foster the development of related clusters.

Imaging inflammatory acne: lesion detection and tracking

Science.gov (United States)

Cula, Gabriela O.; Bargo, Paulo R.; Kollias, Nikiforos

2010-02-01

It is known that effectiveness of acne treatment increases when the lesions are detected earlier, before they could progress into mature wound-like lesions, which lead to scarring and discoloration. However, little is known about the evolution of acne from early signs until after the lesion heals. In this work we computationally characterize the evolution of inflammatory acne lesions, based on analyzing cross-polarized images that document acne-prone facial skin over time. Taking skin images over time, and being able to follow skin features in these images present serious challenges, due to change in the appearance of skin, difficulty in repositioning the subject, involuntary movement such as breathing. A computational technique for automatic detection of lesions by separating the background normal skin from the acne lesions, based on fitting Gaussian distributions to the intensity histograms, is presented. In order to track and quantify the evolution of lesions, in terms of the degree of progress or regress, we designed a study to capture facial skin images from an acne-prone young individual, followed over the course of 3 different time points. Based on the behavior of the lesions between two consecutive time points, the automatically detected lesions are classified in four categories: new lesions, resolved lesions (i.e. lesions that disappear completely), lesions that are progressing, and lesions that are regressing (i.e. lesions in the process of healing). The classification our methods achieve correlates well with visual inspection of a trained human grader.

Clustering-based analysis for residential district heating data

DEFF Research Database (Denmark)

Gianniou, Panagiota; Liu, Xiufeng; Heller, Alfred

2018-01-01

The wide use of smart meters enables collection of a large amount of fine-granular time series, which can be used to improve the understanding of consumption behavior and used for consumption optimization. This paper presents a clustering-based knowledge discovery in databases method to analyze r....... These findings will be valuable for district heating utilities and energy planners to optimize their operations, design demand-side management strategies, and develop targeting energy-efficiency programs or policies.......The wide use of smart meters enables collection of a large amount of fine-granular time series, which can be used to improve the understanding of consumption behavior and used for consumption optimization. This paper presents a clustering-based knowledge discovery in databases method to analyze...... residential heating consumption data and evaluate information included in national building databases. The proposed method uses the K-means algorithm to segment consumption groups based on consumption intensity and representative patterns and ranks the groups according to daily consumption. This paper also...

Effective Social Relationship Measurement and Cluster Based Routing in Mobile Opportunistic Networks.

Science.gov (United States)

Zeng, Feng; Zhao, Nan; Li, Wenjia

2017-05-12

In mobile opportunistic networks, the social relationship among nodes has an important impact on data transmission efficiency. Motivated by the strong share ability of "circles of friends" in communication networks such as Facebook, Twitter, Wechat and so on, we take a real-life example to show that social relationships among nodes consist of explicit and implicit parts. The explicit part comes from direct contact among nodes, and the implicit part can be measured through the "circles of friends". We present the definitions of explicit and implicit social relationships between two nodes, adaptive weights of explicit and implicit parts are given according to the contact feature of nodes, and the distributed mechanism is designed to construct the "circles of friends" of nodes, which is used for the calculation of the implicit part of social relationship between nodes. Based on effective measurement of social relationships, we propose a social-based clustering and routing scheme, in which each node selects the nodes with close social relationships to form a local cluster, and the self-control method is used to keep all cluster members always having close relationships with each other. A cluster-based message forwarding mechanism is designed for opportunistic routing, in which each node only forwards the copy of the message to nodes with the destination node as a member of the local cluster. Simulation results show that the proposed social-based clustering and routing outperforms the other classic routing algorithms.

A semantics-based method for clustering of Chinese web search results

Science.gov (United States)

Zhang, Hui; Wang, Deqing; Wang, Li; Bi, Zhuming; Chen, Yong

2014-01-01

Information explosion is a critical challenge to the development of modern information systems. In particular, when the application of an information system is over the Internet, the amount of information over the web has been increasing exponentially and rapidly. Search engines, such as Google and Baidu, are essential tools for people to find the information from the Internet. Valuable information, however, is still likely submerged in the ocean of search results from those tools. By clustering the results into different groups based on subjects automatically, a search engine with the clustering feature allows users to select most relevant results quickly. In this paper, we propose an online semantics-based method to cluster Chinese web search results. First, we employ the generalised suffix tree to extract the longest common substrings (LCSs) from search snippets. Second, we use the HowNet to calculate the similarities of the words derived from the LCSs, and extract the most representative features by constructing the vocabulary chain. Third, we construct a vector of text features and calculate snippets' semantic similarities. Finally, we improve the Chameleon algorithm to cluster snippets. Extensive experimental results have shown that the proposed algorithm has outperformed over the suffix tree clustering method and other traditional clustering methods.

A clustering approach to segmenting users of internet-based risk calculators.

Science.gov (United States)

Harle, C A; Downs, J S; Padman, R

2011-01-01

Risk calculators are widely available Internet applications that deliver quantitative health risk estimates to consumers. Although these tools are known to have varying effects on risk perceptions, little is known about who will be more likely to accept objective risk estimates. To identify clusters of online health consumers that help explain variation in individual improvement in risk perceptions from web-based quantitative disease risk information. A secondary analysis was performed on data collected in a field experiment that measured people's pre-diabetes risk perceptions before and after visiting a realistic health promotion website that provided quantitative risk information. K-means clustering was performed on numerous candidate variable sets, and the different segmentations were evaluated based on between-cluster variation in risk perception improvement. Variation in responses to risk information was best explained by clustering on pre-intervention absolute pre-diabetes risk perceptions and an objective estimate of personal risk. Members of a high-risk overestimater cluster showed large improvements in their risk perceptions, but clusters of both moderate-risk and high-risk underestimaters were much more muted in improving their optimistically biased perceptions. Cluster analysis provided a unique approach for segmenting health consumers and predicting their acceptance of quantitative disease risk information. These clusters suggest that health consumers were very responsive to good news, but tended not to incorporate bad news into their self-perceptions much. These findings help to quantify variation among online health consumers and may inform the targeted marketing of and improvements to risk communication tools on the Internet.

On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio

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Tatjana Miljkovic

2018-05-01

Full Text Available We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM and mixture-based clustering for an ordered stereotype model (OSM. The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management.

A novel artificial bee colony based clustering algorithm for categorical data.

Science.gov (United States)

Ji, Jinchao; Pang, Wei; Zheng, Yanlin; Wang, Zhe; Ma, Zhiqiang

2015-01-01

Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee colony approach. In our approach, we first introduce a one-step k-modes procedure, and then integrate this procedure with the artificial bee colony approach to deal with categorical data. In the search process performed by scout bees, we adopt the multi-source search inspired by the idea of batch processing to accelerate the convergence of ABC-K-Modes. The performance of ABC-K-Modes is evaluated by a series of experiments in comparison with that of the other popular algorithms for categorical data.

Beverages-Food Industry Cluster Development Based on Value Chain in Indonesia

OpenAIRE

Lasmono Tri Sunaryanto; Gatot Sasongko; Ira Yumastuti

2014-01-01

This study wants to develop the cluster-based food and beverage industry value chain that corresponds to the potential in the regions in Java Economic Corridor. Targeted research: a description of SME development strategies that have been implemented, composed, and can be applied to an SME cluster development strategy of food and beverage, as well as a proven implementation strategy of SME cluster development of food and beverage. To achieve these objectives, implemented descriptive methods, ...

INTERSECTION DETECTION BASED ON QUALITATIVE SPATIAL REASONING ON STOPPING POINT CLUSTERS

Directory of Open Access Journals (Sweden)

S. Zourlidou

2016-06-01

Full Text Available The purpose of this research is to propose and test a method for detecting intersections by analysing collectively acquired trajectories of moving vehicles. Instead of solely relying on the geometric features of the trajectories, such as heading changes, which may indicate turning points and consequently intersections, we extract semantic features of the trajectories in form of sequences of stops and moves. Under this spatiotemporal prism, the extracted semantic information which indicates where vehicles stop can reveal important locations, such as junctions. The advantage of the proposed approach in comparison with existing turning-points oriented approaches is that it can detect intersections even when not all the crossing road segments are sampled and therefore no turning points are observed in the trajectories. The challenge with this approach is that first of all, not all vehicles stop at the same location – thus, the stop-location is blurred along the direction of the road; this, secondly, leads to the effect that nearby junctions can induce similar stop-locations. As a first step, a density-based clustering is applied on the layer of stop observations and clusters of stop events are found. Representative points of the clusters are determined (one per cluster and in a last step the existence of an intersection is clarified based on spatial relational cluster reasoning, with which less informative geospatial clusters, in terms of whether a junction exists and where its centre lies, are transformed in more informative ones. Relational reasoning criteria, based on the relative orientation of the clusters with their adjacent ones are discussed for making sense of the relation that connects them, and finally for forming groups of stop events that belong to the same junction.

DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.

Science.gov (United States)

Sun, Zhe; Wang, Ting; Deng, Ke; Wang, Xiao-Feng; Lafyatis, Robert; Ding, Ying; Hu, Ming; Chen, Wei

2018-01-01

Single cell transcriptome sequencing (scRNA-Seq) has become a revolutionary tool to study cellular and molecular processes at single cell resolution. Among existing technologies, the recently developed droplet-based platform enables efficient parallel processing of thousands of single cells with direct counting of transcript copies using Unique Molecular Identifier (UMI). Despite the technology advances, statistical methods and computational tools are still lacking for analyzing droplet-based scRNA-Seq data. Particularly, model-based approaches for clustering large-scale single cell transcriptomic data are still under-explored. We developed DIMM-SC, a Dirichlet Mixture Model for clustering droplet-based Single Cell transcriptomic data. This approach explicitly models UMI count data from scRNA-Seq experiments and characterizes variations across different cell clusters via a Dirichlet mixture prior. We performed comprehensive simulations to evaluate DIMM-SC and compared it with existing clustering methods such as K-means, CellTree and Seurat. In addition, we analyzed public scRNA-Seq datasets with known cluster labels and in-house scRNA-Seq datasets from a study of systemic sclerosis with prior biological knowledge to benchmark and validate DIMM-SC. Both simulation studies and real data applications demonstrated that overall, DIMM-SC achieves substantially improved clustering accuracy and much lower clustering variability compared to other existing clustering methods. More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods. DIMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/∼wec47/singlecell.html. wei.chen@chp.edu or hum@ccf.org. Supplementary data are available at Bioinformatics online. © The Author

The analysis for energy distribution and biological effects of the clusters from electrons in the tissue equivalent material

International Nuclear Information System (INIS)

Zhang Wenzhong; Guo Yong; Luo Yisheng; Wang Yong

2004-01-01

Objective: To study energy distribution of the clusters from electrons in the tissue equivalent material, and discuss the important aspects of these clusters on inducing biological effects. Methods: Based on the physical mechanism for electrons interacting with tissue equivalent material, the Monte Carlo (MC) method was used. The electron tracks were lively simulated on an event-by-event (ionization, excitation, elastic scattering, Auger electron emission) basis in the material. The relevant conclusions were drawn from the statistic analysis of these events. Results: The electrons will deposit their energy in the form (30%) of cluster in passing through tissue equivalent material, and most clusters (80%) have the energy amount of more than 50 eV. The cluster density depends on its diameter and energy of electrons, and the deposited energy in the cluster depends on the type and energy of radiation. Conclusion: The deposited energy in cluster is the most important factor in inducing all sort of lesions on DNA molecules in tissue cells

Event-based cluster synchronization of coupled genetic regulatory networks

Science.gov (United States)

Yue, Dandan; Guan, Zhi-Hong; Li, Tao; Liao, Rui-Quan; Liu, Feng; Lai, Qiang

2017-09-01

In this paper, the cluster synchronization of coupled genetic regulatory networks with a directed topology is studied by using the event-based strategy and pinning control. An event-triggered condition with a threshold consisting of the neighbors' discrete states at their own event time instants and a state-independent exponential decay function is proposed. The intra-cluster states information and extra-cluster states information are involved in the threshold in different ways. By using the Lyapunov function approach and the theories of matrices and inequalities, we establish the cluster synchronization criterion. It is shown that both the avoidance of continuous transmission of information and the exclusion of the Zeno behavior are ensured under the presented triggering condition. Explicit conditions on the parameters in the threshold are obtained for synchronization. The stability criterion of a single GRN is also given under the reduced triggering condition. Numerical examples are provided to validate the theoretical results.

Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

Directory of Open Access Journals (Sweden)

Yajie Zou

2017-01-01

Full Text Available Hotspot identification (HSID is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections similar to the target site from which safety performance functions (SPF used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

ZiQi Hao

2015-01-01

Full Text Available As limited energy is one of the tough challenges in wireless sensor networks (WSN, energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We use k-means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Distances between cluster heads and event and energy of clusters are fuzzified, thus to use a fuzzy logic to select the clusters that will participate in data uploading and fusion. Fuzzy logic method is also used by cluster heads for local decision, and then the local decision results are sent to the base station. Decision-level fusion for final decision of event is performed by base station according to the uploaded local decisions and fusion support degree of clusters calculated by fuzzy logic method. The effectiveness of this algorithm is demonstrated by simulation results.

Orbit Clustering Based on Transfer Cost

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Gustafson, Eric D.; Arrieta-Camacho, Juan J.; Petropoulos, Anastassios E.

2013-01-01

We propose using cluster analysis to perform quick screening for combinatorial global optimization problems. The key missing component currently preventing cluster analysis from use in this context is the lack of a useable metric function that defines the cost to transfer between two orbits. We study several proposed metrics and clustering algorithms, including k-means and the expectation maximization algorithm. We also show that proven heuristic methods such as the Q-law can be modified to work with cluster analysis.

GLOBAL CLASSIFICATION OF DERMATITIS DISEASE WITH K-MEANS CLUSTERING IMAGE SEGMENTATION METHODS

OpenAIRE

Prafulla N. Aerkewar1 & Dr. G. H. Agrawal2

2018-01-01

The objective of this paper to presents a global technique for classification of different dermatitis disease lesions using the process of k-Means clustering image segmentation method. The word global is used such that the all dermatitis disease having skin lesion on body are classified in to four category using k-means image segmentation and nntool of Matlab. Through the image segmentation technique and nntool can be analyze and study the segmentation properties of skin lesions occurs in...

Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model

Science.gov (United States)

Li, X. L.; Zhao, Q. H.; Li, Y.

2017-09-01

Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

OMERACT-based fibromyalgia symptom subgroups: an exploratory cluster analysis.

Science.gov (United States)

Vincent, Ann; Hoskin, Tanya L; Whipple, Mary O; Clauw, Daniel J; Barton, Debra L; Benzo, Roberto P; Williams, David A

2014-10-16

The aim of this study was to identify subsets of patients with fibromyalgia with similar symptom profiles using the Outcome Measures in Rheumatology (OMERACT) core symptom domains. Female patients with a diagnosis of fibromyalgia and currently meeting fibromyalgia research survey criteria completed the Brief Pain Inventory, the 30-item Profile of Mood States, the Medical Outcomes Sleep Scale, the Multidimensional Fatigue Inventory, the Multiple Ability Self-Report Questionnaire, the Fibromyalgia Impact Questionnaire-Revised (FIQ-R) and the Short Form-36 between 1 June 2011 and 31 October 2011. Hierarchical agglomerative clustering was used to identify subgroups of patients with similar symptom profiles. To validate the results from this sample, hierarchical agglomerative clustering was repeated in an external sample of female patients with fibromyalgia with similar inclusion criteria. A total of 581 females with a mean age of 55.1 (range, 20.1 to 90.2) years were included. A four-cluster solution best fit the data, and each clustering variable differed significantly (P FIQ-R total scores (P = 0.0004)). In our study, we incorporated core OMERACT symptom domains, which allowed for clustering based on a comprehensive symptom profile. Although our exploratory cluster solution needs confirmation in a longitudinal study, this approach could provide a rationale to support the study of individualized clinical evaluation and intervention.

Scalable Integrated Region-Based Image Retrieval Using IRM and Statistical Clustering.

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Wang, James Z.; Du, Yanping

Statistical clustering is critical in designing scalable image retrieval systems. This paper presents a scalable algorithm for indexing and retrieving images based on region segmentation. The method uses statistical clustering on region features and IRM (Integrated Region Matching), a measure developed to evaluate overall similarity between images…

Cluster Validity Classification Approaches Based on Geometric Probability and Application in the Classification of Remotely Sensed Images

Directory of Open Access Journals (Sweden)

LI Jian-Wei

2014-08-01

Full Text Available On the basis of the cluster validity function based on geometric probability in literature [1, 2], propose a cluster analysis method based on geometric probability to process large amount of data in rectangular area. The basic idea is top-down stepwise refinement, firstly categories then subcategories. On all clustering levels, use the cluster validity function based on geometric probability firstly, determine clusters and the gathering direction, then determine the center of clustering and the border of clusters. Through TM remote sensing image classification examples, compare with the supervision and unsupervised classification in ERDAS and the cluster analysis method based on geometric probability in two-dimensional square which is proposed in literature 2. Results show that the proposed method can significantly improve the classification accuracy.

Compound Poisson Processes and Clustered Damage of Radiation Induced DNA Double Strand Breaks

International Nuclear Information System (INIS)

Gudowska-Nowak, E.; Ritter, S.; Taucher-Scholz, G.; Kraft, G.

2000-01-01

Recent experimental data have demonstrated that DNA damage induced by densely ionizing radiation in mammalian cells is distributed along the DNA molecule in the form of clusters. The principal constituent of DNA damage are double-strand breaks (DSB) which are formed when the breaks occur in both DNA strands and are directly opposite or separated by only a few base pairs. DSBs are believed to be most important lesions produced in chromosomes by radiation; interaction between DSBs can lead to cell killing, mutation or carcinogenesis. The paper discusses a model of clustered DSB formation viewed in terms of compound Poisson process along with the predictive essay of the formalism in application to experimental data. (author)

A Survey on the Taxonomy of Cluster-Based Routing Protocols for Homogeneous Wireless Sensor Networks

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Naeimi, Soroush; Ghafghazi, Hamidreza; Chow, Chee-Onn; Ishii, Hiroshi

2012-01-01

The past few years have witnessed increased interest among researchers in cluster-based protocols for homogeneous networks because of their better scalability and higher energy efficiency than other routing protocols. Given the limited capabilities of sensor nodes in terms of energy resources, processing and communication range, the cluster-based protocols should be compatible with these constraints in either the setup state or steady data transmission state. With focus on these constraints, we classify routing protocols according to their objectives and methods towards addressing the shortcomings of clustering process on each stage of cluster head selection, cluster formation, data aggregation and data communication. We summarize the techniques and methods used in these categories, while the weakness and strength of each protocol is pointed out in details. Furthermore, taxonomy of the protocols in each phase is given to provide a deeper understanding of current clustering approaches. Ultimately based on the existing research, a summary of the issues and solutions of the attributes and characteristics of clustering approaches and some open research areas in cluster-based routing protocols that can be further pursued are provided. PMID:22969350

A novel grain cluster-based homogenization scheme

International Nuclear Information System (INIS)

Tjahjanto, D D; Eisenlohr, P; Roters, F

2010-01-01

An efficient homogenization scheme, termed the relaxed grain cluster (RGC), for elasto-plastic deformations of polycrystals is presented. The scheme is based on a generalization of the grain cluster concept. A volume element consisting of eight (= 2 × 2 × 2) hexahedral grains is considered. The kinematics of the RGC scheme is formulated within a finite deformation framework, where the relaxation of the local deformation gradient of each individual grain is connected to the overall deformation gradient by the, so-called, interface relaxation vectors. The set of relaxation vectors is determined by the minimization of the constitutive energy (or work) density of the overall cluster. An additional energy density associated with the mismatch at the grain boundaries due to relaxations is incorporated as a penalty term into the energy minimization formulation. Effectively, this penalty term represents the kinematical condition of deformation compatibility at the grain boundaries. Simulations have been performed for a dual-phase grain cluster loaded in uniaxial tension. The results of the simulations are presented and discussed in terms of the effective stress–strain response and the overall deformation anisotropy as functions of the penalty energy parameters. In addition, the prediction of the RGC scheme is compared with predictions using other averaging schemes, as well as to the result of direct finite element (FE) simulation. The comparison indicates that the present RGC scheme is able to approximate FE simulation results of relatively fine discretization at about three orders of magnitude lower computational cost

a Web-Based Interactive Platform for Co-Clustering Spatio-Temporal Data

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Wu, X.; Poorthuis, A.; Zurita-Milla, R.; Kraak, M.-J.

2017-09-01

Since current studies on clustering analysis mainly focus on exploring spatial or temporal patterns separately, a co-clustering algorithm is utilized in this study to enable the concurrent analysis of spatio-temporal patterns. To allow users to adopt and adapt the algorithm for their own analysis, it is integrated within the server side of an interactive web-based platform. The client side of the platform, running within any modern browser, is a graphical user interface (GUI) with multiple linked visualizations that facilitates the understanding, exploration and interpretation of the raw dataset and co-clustering results. Users can also upload their own datasets and adjust clustering parameters within the platform. To illustrate the use of this platform, an annual temperature dataset from 28 weather stations over 20 years in the Netherlands is used. After the dataset is loaded, it is visualized in a set of linked visualizations: a geographical map, a timeline and a heatmap. This aids the user in understanding the nature of their dataset and the appropriate selection of co-clustering parameters. Once the dataset is processed by the co-clustering algorithm, the results are visualized in the small multiples, a heatmap and a timeline to provide various views for better understanding and also further interpretation. Since the visualization and analysis are integrated in a seamless platform, the user can explore different sets of co-clustering parameters and instantly view the results in order to do iterative, exploratory data analysis. As such, this interactive web-based platform allows users to analyze spatio-temporal data using the co-clustering method and also helps the understanding of the results using multiple linked visualizations.

Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods.

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Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo

2016-01-01

Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community.

Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods

Science.gov (United States)

Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo

2016-01-01

Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community. PMID:27124610

An ant colony based resilience approach to cascading failures in cluster supply network

Science.gov (United States)

Wang, Yingcong; Xiao, Renbin

2016-11-01

Cluster supply chain network is a typical complex network and easily suffers cascading failures under disruption events, which is caused by the under-load of enterprises. Improving network resilience can increase the ability of recovery from cascading failures. Social resilience is found in ant colony and comes from ant's spatial fidelity zones (SFZ). Starting from the under-load failures, this paper proposes a resilience method to cascading failures in cluster supply chain network by leveraging on social resilience of ant colony. First, the mapping between ant colony SFZ and cluster supply chain network SFZ is presented. Second, a new cascading model for cluster supply chain network is constructed based on under-load failures. Then, the SFZ-based resilience method and index to cascading failures are developed according to ant colony's social resilience. Finally, a numerical simulation and a case study are used to verify the validity of the cascading model and the resilience method. Experimental results show that, the cluster supply chain network becomes resilient to cascading failures under the SFZ-based resilience method, and the cluster supply chain network resilience can be enhanced by improving the ability of enterprises to recover and adjust.

Stroke Lesions in a Large Upper Limb Rehabilitation Trial Cohort Rarely Match Lesions in Common Preclinical Models

Science.gov (United States)

Edwardson, Matthew A.; Wang, Ximing; Liu, Brent; Ding, Li; Lane, Christianne J.; Park, Caron; Nelsen, Monica A.; Jones, Theresa A; Wolf, Steven L; Winstein, Carolee J; Dromerick, Alexander W.

2017-01-01

Background Stroke patients with mild-moderate upper extremity (UE) motor impairments and minimal sensory and cognitive deficits provide a useful model to study recovery and improve rehabilitation. Laboratory-based investigators use lesioning techniques for similar goals. Objective Determine whether stroke lesions in an UE rehabilitation trial cohort match lesions from the preclinical stroke recovery models used to drive translational research. Methods Clinical neuroimages from 297 participants enrolled in the Interdisciplinary Comprehensive Arm Rehabilitation Evaluation (ICARE) study were reviewed. Images were characterized based on lesion type (ischemic or hemorrhagic), volume, vascular territory, depth (cortical gray matter, cortical white matter, subcortical), old strokes, and leukoaraiosis. Lesions were compared with those of preclinical stroke models commonly used to study upper limb recovery. Results Among the ischemic stroke participants, median infarct volume was 1.8 mL, with most lesions confined to subcortical structures (61%) including the anterior choroidal artery territory (30%) and the pons (23%). Of ICARE participants, stroke patients, but they represent a clinically and scientifically important subgroup. Compared to lesions in general stroke populations and widely-studied animal models of recovery, ICARE participants had smaller, more subcortically-based strokes. Improved preclinical-clinical translational efforts may require better alignment of lesions between preclinical and human stroke recovery models. PMID:28337932

Inter-algorithm lesion volumetry comparison of real and 3D simulated lung lesions in CT

Science.gov (United States)

Robins, Marthony; Solomon, Justin; Hoye, Jocelyn; Smith, Taylor; Ebner, Lukas; Samei, Ehsan

2017-03-01

The purpose of this study was to establish volumetric exchangeability between real and computational lung lesions in CT. We compared the overall relative volume estimation performance of segmentation tools when used to measure real lesions in actual patient CT images and computational lesions virtually inserted into the same patient images (i.e., hybrid datasets). Pathologically confirmed malignancies from 30 thoracic patient cases from Reference Image Database to Evaluate Therapy Response (RIDER) were modeled and used as the basis for the comparison. Lesions included isolated nodules as well as those attached to the pleura or other lung structures. Patient images were acquired using a 16 detector row or 64 detector row CT scanner (Lightspeed 16 or VCT; GE Healthcare). Scans were acquired using standard chest protocols during a single breath-hold. Virtual 3D lesion models based on real lesions were developed in Duke Lesion Tool (Duke University), and inserted using a validated image-domain insertion program. Nodule volumes were estimated using multiple commercial segmentation tools (iNtuition, TeraRecon, Inc., Syngo.via, Siemens Healthcare, and IntelliSpace, Philips Healthcare). Consensus based volume comparison showed consistent trends in volume measurement between real and virtual lesions across all software. The average percent bias (+/- standard error) shows -9.2+/-3.2% for real lesions versus -6.7+/-1.2% for virtual lesions with tool A, 3.9+/-2.5% and 5.0+/-0.9% for tool B, and 5.3+/-2.3% and 1.8+/-0.8% for tool C, respectively. Virtual lesion volumes were statistically similar to those of real lesions (.05 in most cases. Results suggest that hybrid datasets had similar inter-algorithm variability compared to real datasets.

Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis.

Science.gov (United States)

Li, Chaoshun; Zhou, Jianzhong

2014-09-01

Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Fast gene ontology based clustering for microarray experiments.

Science.gov (United States)

Ovaska, Kristian; Laakso, Marko; Hautaniemi, Sampsa

2008-11-21

Analysis of a microarray experiment often results in a list of hundreds of disease-associated genes. In order to suggest common biological processes and functions for these genes, Gene Ontology annotations with statistical testing are widely used. However, these analyses can produce a very large number of significantly altered biological processes. Thus, it is often challenging to interpret GO results and identify novel testable biological hypotheses. We present fast software for advanced gene annotation using semantic similarity for Gene Ontology terms combined with clustering and heat map visualisation. The methodology allows rapid identification of genes sharing the same Gene Ontology cluster. Our R based semantic similarity open-source package has a speed advantage of over 2000-fold compared to existing implementations. From the resulting hierarchical clustering dendrogram genes sharing a GO term can be identified, and their differences in the gene expression patterns can be seen from the heat map. These methods facilitate advanced annotation of genes resulting from data analysis.

Profiling physical activity motivation based on self-determination theory: a cluster analysis approach.

Science.gov (United States)

Friederichs, Stijn Ah; Bolman, Catherine; Oenema, Anke; Lechner, Lilian

2015-01-01

In order to promote physical activity uptake and maintenance in individuals who do not comply with physical activity guidelines, it is important to increase our understanding of physical activity motivation among this group. The present study aimed to examine motivational profiles in a large sample of adults who do not comply with physical activity guidelines. The sample for this study consisted of 2473 individuals (31.4% male; age 44.6 ± 12.9). In order to generate motivational profiles based on motivational regulation, a cluster analysis was conducted. One-way analyses of variance were then used to compare the clusters in terms of demographics, physical activity level, motivation to be active and subjective experience while being active. Three motivational clusters were derived based on motivational regulation scores: a low motivation cluster, a controlled motivation cluster and an autonomous motivation cluster. These clusters differed significantly from each other with respect to physical activity behavior, motivation to be active and subjective experience while being active. Overall, the autonomous motivation cluster displayed more favorable characteristics compared to the other two clusters. The results of this study provide additional support for the importance of autonomous motivation in the context of physical activity behavior. The three derived clusters may be relevant in the context of physical activity interventions as individuals within the different clusters might benefit most from different intervention approaches. In addition, this study shows that cluster analysis is a useful method for differentiating between motivational profiles in large groups of individuals who do not comply with physical activity guidelines.

Automatic Detection of Lung and Liver Lesions in 3-D Positron Emission Tomography Images: A Pilot Study

Science.gov (United States)

Lartizien, Carole; Marache-Francisco, Simon; Prost, Rémy

2012-02-01

Positron emission tomography (PET) using fluorine-18 deoxyglucose (18F-FDG) has become an increasingly recommended tool in clinical whole-body oncology imaging for the detection, diagnosis, and follow-up of many cancers. One way to improve the diagnostic utility of PET oncology imaging is to assist physicians facing difficult cases of residual or low-contrast lesions. This study aimed at evaluating different schemes of computer-aided detection (CADe) systems for the guided detection and localization of small and low-contrast lesions in PET. These systems are based on two supervised classifiers, linear discriminant analysis (LDA) and the nonlinear support vector machine (SVM). The image feature sets that serve as input data consisted of the coefficients of an undecimated wavelet transform. An optimization study was conducted to select the best combination of parameters for both the SVM and the LDA. Different false-positive reduction (FPR) methods were evaluated to reduce the number of false-positive detections per image (FPI). This includes the removal of small detected clusters and the combination of the LDA and SVM detection maps. The different CAD schemes were trained and evaluated based on a simulated whole-body PET image database containing 250 abnormal cases with 1230 lesions and 250 normal cases with no lesion. The detection performance was measured on a separate series of 25 testing images with 131 lesions. The combination of the LDA and SVM score maps was shown to produce very encouraging detection performance for both the lung lesions, with 91% sensitivity and 18 FPIs, and the liver lesions, with 94% sensitivity and 10 FPIs. Comparison with human performance indicated that the different CAD schemes significantly outperformed human detection sensitivities, especially regarding the low-contrast lesions.

Large-Scale Multi-Dimensional Document Clustering on GPU Clusters

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaohui [ORNL; Mueller, Frank [North Carolina State University; Zhang, Yongpeng [ORNL; Potok, Thomas E [ORNL

2010-01-01

Document clustering plays an important role in data mining systems. Recently, a flocking-based document clustering algorithm has been proposed to solve the problem through simulation resembling the flocking behavior of birds in nature. This method is superior to other clustering algorithms, including k-means, in the sense that the outcome is not sensitive to the initial state. One limitation of this approach is that the algorithmic complexity is inherently quadratic in the number of documents. As a result, execution time becomes a bottleneck with large number of documents. In this paper, we assess the benefits of exploiting the computational power of Beowulf-like clusters equipped with contemporary Graphics Processing Units (GPUs) as a means to significantly reduce the runtime of flocking-based document clustering. Our framework scales up to over one million documents processed simultaneously in a sixteennode GPU cluster. Results are also compared to a four-node cluster with higher-end GPUs. On these clusters, we observe 30X-50X speedups, which demonstrates the potential of GPU clusters to efficiently solve massive data mining problems. Such speedups combined with the scalability potential and accelerator-based parallelization are unique in the domain of document-based data mining, to the best of our knowledge.

A time-series approach for clustering farms based on slaughterhouse health aberration data.

Science.gov (United States)

Hulsegge, B; de Greef, K H

2018-05-01

A large amount of data is collected routinely in meat inspection in pig slaughterhouses. A time series clustering approach is presented and applied that groups farms based on similar statistical characteristics of meat inspection data over time. A three step characteristic-based clustering approach was used from the idea that the data contain more info than the incidence figures. A stratified subset containing 511,645 pigs was derived as a study set from 3.5 years of meat inspection data. The monthly averages of incidence of pleuritis and of pneumonia of 44 Dutch farms (delivering 5149 batches to 2 pig slaughterhouses) were subjected to 1) derivation of farm level data characteristics 2) factor analysis and 3) clustering into groups of farms. The characteristic-based clustering was able to cluster farms for both lung aberrations. Three groups of data characteristics were informative, describing incidence, time pattern and degree of autocorrelation. The consistency of clustering similar farms was confirmed by repetition of the analysis in a larger dataset. The robustness of the clustering was tested on a substantially extended dataset. This confirmed the earlier results, three data distribution aspects make up the majority of distinction between groups of farms and in these groups (clusters) the majority of the farms was allocated comparable to the earlier allocation (75% and 62% for pleuritis and pneumonia, respectively). The difference between pleuritis and pneumonia in their seasonal dependency was confirmed, supporting the biological relevance of the clustering. Comparison of the identified clusters of statistically comparable farms can be used to detect farm level risk factors causing the health aberrations beyond comparison on disease incidence and trend alone. Copyright © 2018 Elsevier B.V. All rights reserved.

Seniority-based coupled cluster theory

International Nuclear Information System (INIS)

Henderson, Thomas M.; Scuseria, Gustavo E.; Bulik, Ireneusz W.; Stein, Tamar

2014-01-01

Doubly occupied configuration interaction (DOCI) with optimized orbitals often accurately describes strong correlations while working in a Hilbert space much smaller than that needed for full configuration interaction. However, the scaling of such calculations remains combinatorial with system size. Pair coupled cluster doubles (pCCD) is very successful in reproducing DOCI energetically, but can do so with low polynomial scaling (N 3 , disregarding the two-electron integral transformation from atomic to molecular orbitals). We show here several examples illustrating the success of pCCD in reproducing both the DOCI energy and wave function and show how this success frequently comes about. What DOCI and pCCD lack are an effective treatment of dynamic correlations, which we here add by including higher-seniority cluster amplitudes which are excluded from pCCD. This frozen pair coupled cluster approach is comparable in cost to traditional closed-shell coupled cluster methods with results that are competitive for weakly correlated systems and often superior for the description of strongly correlated systems

CT diagnosis of sellar and juxtasellar lesions, 3. Non-tumorous lesions

Energy Technology Data Exchange (ETDEWEB)

Kobayashi, Tatsuya [Nagoya Univ. (Japan). Faculty of Medicine

1982-08-01

A study is made of the usefulness and limitations of the CT diagnosis of sellar and juxtasellar lesions other than tumors. This study is based on 112 verified cases at Nagoya University Hospital from October, 1976, to December, 1981. The lesions included in this study are classified into four groups: vascular, inflammatory, traumatic lesion, and congenital anomaly. Although cerebral angiography is the cardinal method for the diagnosis of vascular lesions, CT is useful for the evaluation of a giant aneurysm, the localization of bleeding, or infarction by a ruptured aneurysm. Radiation brain necrosis, a special form of vascular lesion, can also be diagnosed if the critical analysis is made after previous irradiation. CT findings are helpful for the local diagnosis of acute inflammatory lesions, such as basal meningitis or abscess, but specific diagnosis is made on the basis of clinical signs and CSF study. Abnormal CT findings are obtained from a chronic inflammatory process, such as arachnoiditis adhesiva, glanuloma, or mucocele. Differential diagnosis is necessary with brain tumors. The CT findings of an arachnoid cyst are often diagnostic. Metrizamide or air cisternography, either combined with CT or without it, is important for the diagnosis of basal meningoencephalocele and hypothalamic hamaroma. Pneumocephalus and an intracranial foreign body resulting from a head injury can be diagnosed by plain skull and CT. The diagnosis of CSF leakage or prolapse cerebri associated with a skull-base fracture has been most difficult, but even it is possible by a combination of polytomography and high-resolution CT with metrizamide cisternography.

Axonal lesion-induced microglial proliferation and microglial cluster formation in the mouse

DEFF Research Database (Denmark)

Dissing-Olesen, L; Ladeby, R; Nielsen, Helle Hvilsted

2007-01-01

Microglia are innate immune cells and form the first line of defense of the CNS. Proliferation is a key event in the activation of microglia in acute pathology, and has been extensively characterized in rats, but not in mice. In this study we investigated axonal-lesion-induced microglial prolifer...

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

Science.gov (United States)

Tokuda, Tomoki; Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

Directory of Open Access Journals (Sweden)

Tomoki Tokuda

Full Text Available We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

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Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data. PMID:29049392

An effective trust-based recommendation method using a novel graph clustering algorithm

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Moradi, Parham; Ahmadian, Sajad; Akhlaghian, Fardin

2015-10-01

Recommender systems are programs that aim to provide personalized recommendations to users for specific items (e.g. music, books) in online sharing communities or on e-commerce sites. Collaborative filtering methods are important and widely accepted types of recommender systems that generate recommendations based on the ratings of like-minded users. On the other hand, these systems confront several inherent issues such as data sparsity and cold start problems, caused by fewer ratings against the unknowns that need to be predicted. Incorporating trust information into the collaborative filtering systems is an attractive approach to resolve these problems. In this paper, we present a model-based collaborative filtering method by applying a novel graph clustering algorithm and also considering trust statements. In the proposed method first of all, the problem space is represented as a graph and then a sparsest subgraph finding algorithm is applied on the graph to find the initial cluster centers. Then, the proposed graph clustering algorithm is performed to obtain the appropriate users/items clusters. Finally, the identified clusters are used as a set of neighbors to recommend unseen items to the current active user. Experimental results based on three real-world datasets demonstrate that the proposed method outperforms several state-of-the-art recommender system methods.

Cluster chain based energy efficient routing protocol for moblie WSN

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WU Ziyu

2016-04-01

Full Text Available With the ubiquitous smart devices acting as mobile sensor nodes in the wireless sensor networks(WSNs to sense and transmit physical information,routing protocols should be designed to accommodate the mobility issues,in addition to conventional considerations on energy efficiency.However,due to frequent topology change,traditional routing schemes cannot perform well.Moreover,existence of mobile nodes poses new challenges on energy dissipation and packet loss.In this paper,a novel routing scheme called cluster chain based routing protocol(CCBRP is proposed,which employs a combination of cluster and chain structure to accomplish data collection and transmission and thereafter selects qualified cluster heads as chain leaders to transmit data to the sink.Furthermore,node mobility is handled based on periodical membership update of mobile nodes.Simulation results demonstrate that CCBRP has a good performance in terms of network lifetime and packet delivery,also strikes a better balance between successful packet reception and energy consumption.

Effect of MTA-based sealer on the healing of periapical lesions.

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Gomes-Filho, João Eduardo; Watanabe, Simone; Cintra, Luciano Tavares Angelo; Nery, Mauro Juvenal; Dezan-Júnior, Eloi; Queiroz, India Olinta Azevedo; Lodi, Carolina Simonetti; Basso, Maria Daniela

2013-01-01

Some manufacturers have recently added specific components to improve the ease of handling and insertion material properties of MTA in order to create MTA-based sealers. The aim of this study was to evaluate the healing of periapical lesions in canine teeth after a single session of endodontic treatment with MTA Fillapex® compared with Sealapex® or Endo-CPM-Sealer®. Sixty-two root canals were performed on two 1-year-old male dogs. After coronal access and pulp extirpation, the canals were exposed to the oral cavity for 6 months in order to induce periapical lesions. The root canals were prepared, irrigated with a solution of 2.5% sodium hypochlorite and filled with gutta-percha and different sealers, according to the following groups: 1) Sealapex®; 2) Endo-CPM-Sealer®; and 3) MTA Fillapex®. Some teeth with periapical lesions were left untreated for use as positive controls. Healthy teeth were used as negative controls. After 6 months, the animals were sacrificed and serial sections from the roots were prepared for histomorphologic analysis and stained with hematoxylin and eosin and the Brown and Brenn technique. The lesions were scored according to pre-established histomorphologic parameters and the scores statistically analyzed using the Kruskal-Wallis test. All 3 materials produced similar patterns of healing (p>0.05); in particular, persistent inflammation and absence of complete periapical tissue healing were consistently noted. Preparation of the infected root canals followed by filling with the materials studied was insufficient to provide complete healing of the periapical tissues.

An adaptive clustering algorithm for image matching based on corner feature

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Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-04-01

The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on corner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering. The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same time.

Generating clustered scale-free networks using Poisson based localization of edges

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Türker, İlker

2018-05-01

We introduce a variety of network models using a Poisson-based edge localization strategy, which result in clustered scale-free topologies. We first verify the success of our localization strategy by realizing a variant of the well-known Watts-Strogatz model with an inverse approach, implying a small-world regime of rewiring from a random network through a regular one. We then apply the rewiring strategy to a pure Barabasi-Albert model and successfully achieve a small-world regime, with a limited capacity of scale-free property. To imitate the high clustering property of scale-free networks with higher accuracy, we adapted the Poisson-based wiring strategy to a growing network with the ingredients of both preferential attachment and local connectivity. To achieve the collocation of these properties, we used a routine of flattening the edges array, sorting it, and applying a mixing procedure to assemble both global connections with preferential attachment and local clusters. As a result, we achieved clustered scale-free networks with a computational fashion, diverging from the recent studies by following a simple but efficient approach.

Effective Social Relationship Measurement and Cluster Based Routing in Mobile Opportunistic Networks †

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Zeng, Feng; Zhao, Nan; Li, Wenjia

2017-01-01

In mobile opportunistic networks, the social relationship among nodes has an important impact on data transmission efficiency. Motivated by the strong share ability of “circles of friends” in communication networks such as Facebook, Twitter, Wechat and so on, we take a real-life example to show that social relationships among nodes consist of explicit and implicit parts. The explicit part comes from direct contact among nodes, and the implicit part can be measured through the “circles of friends”. We present the definitions of explicit and implicit social relationships between two nodes, adaptive weights of explicit and implicit parts are given according to the contact feature of nodes, and the distributed mechanism is designed to construct the “circles of friends” of nodes, which is used for the calculation of the implicit part of social relationship between nodes. Based on effective measurement of social relationships, we propose a social-based clustering and routing scheme, in which each node selects the nodes with close social relationships to form a local cluster, and the self-control method is used to keep all cluster members always having close relationships with each other. A cluster-based message forwarding mechanism is designed for opportunistic routing, in which each node only forwards the copy of the message to nodes with the destination node as a member of the local cluster. Simulation results show that the proposed social-based clustering and routing outperforms the other classic routing algorithms. PMID:28498309

Cluster analysis for the probability of DSB site induced by electron tracks

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Yoshii, Y. [Biological Research, Education and Instrumentation Center, Sapporo Medical University, Sapporo 060-8556 (Japan); Graduate School of Health Sciences, Hokkaido University, Sapporo 060-0812 (Japan); Sasaki, K. [Faculty of Health Sciences, Hokkaido University of Science, Sapporo 006-8585 (Japan); Matsuya, Y. [Graduate School of Health Sciences, Hokkaido University, Sapporo 060-0812 (Japan); Date, H., E-mail: date@hs.hokudai.ac.jp [Faculty of Health Sciences, Hokkaido University, Sapporo 060-0812 (Japan)

2015-05-01

To clarify the influence of bio-cells exposed to ionizing radiations, the densely populated pattern of the ionization in the cell nucleus is of importance because it governs the extent of DNA damage which may lead to cell lethality. In this study, we have conducted a cluster analysis of ionization and excitation events to estimate the number of double-strand breaks (DSBs) induced by electron tracks. A Monte Carlo simulation for electrons in liquid water was performed to determine the spatial location of the ionization and excitation events. The events were divided into clusters by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The algorithm enables us to sort out the events into the groups (clusters) in which a minimum number of neighboring events are contained within a given radius. For evaluating the number of DSBs in the extracted clusters, we have introduced an aggregation index (AI). The computational results show that a sub-keV electron produces DSBs in a dense formation more effectively than higher energy electrons. The root-mean square radius (RMSR) of the cluster size is below 5 nm, which is smaller than the chromatin fiber thickness. It was found that this size of clustering events has a high possibility to cause lesions in DNA within the chromatin fiber site.

Evaluation of colonic lesions and pitfalls in CT colonography: A systematic approach based on morphology, attenuation and mobility

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Mang, Thomas; Gryspeerdt, Stefaan; Schima, Wolfgang; Lefere, Philippe

2013-01-01

Computed tomographic colonography is a reliable technique for the detection and classification of neoplastic and non-neoplastic lesions of the colon. It is based on a thin-section CT dataset of the cleansed and air-distended colon, acquired in prone and supine position. Two-dimensional and 3D projections are used in combination for image interpretation. The evaluation of CT colonography datasets is based on two steps, lesion perception to detect a polyp candidate and image interpretation to correctly characterize colonic filling defects. A thorough knowledge of the morphologic and attenuation characteristics of common colonic lesions and artifacts facilitates characterization of the findings. The purpose of this review article is to give an overview of the key CT colonographic imaging criteria to correctly characterize common colorectal lesions and to identify typical pitfalls and pseudolesions

Unsupervised Performance Evaluation Strategy for Bridge Superstructure Based on Fuzzy Clustering and Field Data

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Yubo Jiao

2013-01-01

Full Text Available Performance evaluation of a bridge is critical for determining the optimal maintenance strategy. An unsupervised bridge superstructure state assessment method is proposed in this paper based on fuzzy clustering and bridge field measured data. Firstly, the evaluation index system of bridge is constructed. Secondly, a certain number of bridge health monitoring data are selected as clustering samples to obtain the fuzzy similarity matrix and fuzzy equivalent matrix. Finally, different thresholds are selected to form dynamic clustering maps and determine the best classification based on statistic analysis. The clustering result is regarded as a sample base, and the bridge state can be evaluated by calculating the fuzzy nearness between the unknown bridge state data and the sample base. Nanping Bridge in Jilin Province is selected as the engineering project to verify the effectiveness of the proposed method.

New method for estimating clustering of DNA lesions induced by physical/chemical mutagens using fluorescence anisotropy.

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Akamatsu, Ken; Shikazono, Naoya; Saito, Takeshi

2017-11-01

We have developed a new method for estimating the localization of DNA damage such as apurinic/apyrimidinic sites (APs) on DNA using fluorescence anisotropy. This method is aimed at characterizing clustered DNA damage produced by DNA-damaging agents such as ionizing radiation and genotoxic chemicals. A fluorescent probe with an aminooxy group (AlexaFluor488) was used to label APs. We prepared a pUC19 plasmid with APs by heating under acidic conditions as a model for damaged DNA, and subsequently labeled the APs. We found that the observed fluorescence anisotropy (r obs ) decreases as averaged AP density (λ AP : number of APs per base pair) increases due to homo-FRET, and that the APs were randomly distributed. We applied this method to three DNA-damaging agents, 60 Co γ-rays, methyl methanesulfonate (MMS), and neocarzinostatin (NCS). We found that r obs -λ AP relationships differed significantly between MMS and NCS. At low AP density (λ AP  < 0.001), the APs induced by MMS seemed to not be closely distributed, whereas those induced by NCS were remarkably clustered. In contrast, the AP clustering induced by 60 Co γ-rays was similar to, but potentially more likely to occur than, random distribution. This simple method can be used to estimate mutagenicity of ionizing radiation and genotoxic chemicals. Copyright © 2017 Elsevier Inc. All rights reserved.

Recognition of genetically modified product based on affinity propagation clustering and terahertz spectroscopy

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Liu, Jianjun; Kan, Jianquan

2018-04-01

In this paper, based on the terahertz spectrum, a new identification method of genetically modified material by support vector machine (SVM) based on affinity propagation clustering is proposed. This algorithm mainly uses affinity propagation clustering algorithm to make cluster analysis and labeling on unlabeled training samples, and in the iterative process, the existing SVM training data are continuously updated, when establishing the identification model, it does not need to manually label the training samples, thus, the error caused by the human labeled samples is reduced, and the identification accuracy of the model is greatly improved.

Arrested pneumatization of the sphenoid sinus mimicking intraosseous lesions of the skull base

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Jalali, Elnaz; Tadinada, Aditya [Dept. of Oral and Maxillofacial Radiology, University of Connecticut School of Dental Medicine, Farmington (United States)

2015-03-15

Arrested pneumatization of the sphenoid sinus is a developmental variant that is not always well recognized and is often confused with other pathologies associated with the skull base. This report describes the case of a patient referred for cone-beam computed tomography (CBCT) imaging for dental implant therapy. CBCT demonstrated a well-defined incidental lesion in the left sphenoid sinus with soft tissue-like density and sclerotic borders with internal curvilinear opacifications. The differential diagnoses included intraosseous lipoma, arrested pneumatization of the sphenoid sinus, chondrosarcoma, chondroid chordoma, and ossifying fibroma. The radiographic diagnosis of arrested pneumatization was based on the location of the lesion, its well-defined nature, the presence of internal opacifications, and lack of expansion. Gray-scale CBCT imaging of the area demonstrated values similar to fatty tissue. This case highlighted the fact that benign developmental variants associated with the skull base share similar radiographic features with more serious pathological entities.

A Method for Extracting Suspected Parotid Lesions in CT Images using Feature-based Segmentation and Active Contours based on Stationary Wavelet Transform

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Wu, T. Y.; Lin, S. F.

2013-10-01

Automatic suspected lesion extraction is an important application in computer-aided diagnosis (CAD). In this paper, we propose a method to automatically extract the suspected parotid regions for clinical evaluation in head and neck CT images. The suspected lesion tissues in low contrast tissue regions can be localized with feature-based segmentation (FBS) based on local texture features, and can be delineated with accuracy by modified active contour models (ACM). At first, stationary wavelet transform (SWT) is introduced. The derived wavelet coefficients are applied to derive the local features for FBS, and to generate enhanced energy maps for ACM computation. Geometric shape features (GSFs) are proposed to analyze each soft tissue region segmented by FBS; the regions with higher similarity GSFs with the lesions are extracted and the information is also applied as the initial conditions for fine delineation computation. Consequently, the suspected lesions can be automatically localized and accurately delineated for aiding clinical diagnosis. The performance of the proposed method is evaluated by comparing with the results outlined by clinical experts. The experiments on 20 pathological CT data sets show that the true-positive (TP) rate on recognizing parotid lesions is about 94%, and the dimension accuracy of delineation results can also approach over 93%.

Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval

International Nuclear Information System (INIS)

Xu Jiajing; Napel, Sandy; Greenspan, Hayit; Beaulieu, Christopher F.; Agrawal, Neeraj; Rubin, Daniel

2012-01-01

. Equivalence across deformations was assessed using Schuirmann's paired two one-sided tests. Results: In simulated images, the concordance correlation between measured gradient and actual gradient was 0.994. The mean (s.d.) and standard deviation NDCG score for the retrieval of K images, K = 5, 10, and 15, were 84% (8%), 85% (7%), and 85% (7%) for CT images containing liver lesions, and 82% (7%), 84% (6%), and 85% (4%) for CT images containing lung nodules, respectively. The authors’ proposed method outperformed the two existing margin characterization methods in average NDCG scores over all K, by 1.5% and 3% in datasets containing liver lesion, and 4.5% and 5% in datasets containing lung nodules. Equivalence testing showed that the authors’ feature is more robust across all margin deformations (p < 0.05) than the two existing methods for margin sharpness characterization in both simulated and clinical datasets. Conclusions: The authors have described a new image feature to quantify the margin sharpness of lesions. It has strong correlation with known margin sharpness in simulated images and in clinical CT images containing liver lesions and lung nodules. This image feature has excellent performance for retrieving images with similar margin characteristics, suggesting potential utility, in conjunction with other lesion features, for content-based image retrieval applications.

Particle Swarm Optimization and harmony search based clustering and routing in Wireless Sensor Networks

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Veena Anand

2017-01-01

Full Text Available Wireless Sensor Networks (WSN has the disadvantage of limited and non-rechargeable energy resource in WSN creates a challenge and led to development of various clustering and routing algorithms. The paper proposes an approach for improving network lifetime by using Particle swarm optimization based clustering and Harmony Search based routing in WSN. So in this paper, global optimal cluster head are selected and Gateway nodes are introduced to decrease the energy consumption of the CH while sending aggregated data to the Base station (BS. Next, the harmony search algorithm based Local Search strategy finds best routing path for gateway nodes to the Base Station. Finally, the proposed algorithm is presented.

Fast Gene Ontology based clustering for microarray experiments

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Ovaska Kristian

2008-11-01

Full Text Available Abstract Background Analysis of a microarray experiment often results in a list of hundreds of disease-associated genes. In order to suggest common biological processes and functions for these genes, Gene Ontology annotations with statistical testing are widely used. However, these analyses can produce a very large number of significantly altered biological processes. Thus, it is often challenging to interpret GO results and identify novel testable biological hypotheses. Results We present fast software for advanced gene annotation using semantic similarity for Gene Ontology terms combined with clustering and heat map visualisation. The methodology allows rapid identification of genes sharing the same Gene Ontology cluster. Conclusion Our R based semantic similarity open-source package has a speed advantage of over 2000-fold compared to existing implementations. From the resulting hierarchical clustering dendrogram genes sharing a GO term can be identified, and their differences in the gene expression patterns can be seen from the heat map. These methods facilitate advanced annotation of genes resulting from data analysis.

Environment-based selection effects of Planck clusters

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Kosyra, R.; Gruen, D.; Seitz, S.; Mana, A.; Rozo, E.; Rykoff, E.; Sanchez, A.; Bender, R.

2015-07-24

We investigate whether the large-scale structure environment of galaxy clusters imprints a selection bias on Sunyaev–Zel'dovich (SZ) catalogues. Such a selection effect might be caused by line of sight (LoS) structures that add to the SZ signal or contain point sources that disturb the signal extraction in the SZ survey. We use the Planck PSZ1 union catalogue in the Sloan Digital Sky Survey (SDSS) region as our sample of SZ-selected clusters. We calculate the angular two-point correlation function (2pcf) for physically correlated, foreground and background structure in the RedMaPPer SDSS DR8 catalogue with respect to each cluster. We compare our results with an optically selected comparison cluster sample and with theoretical predictions. In contrast to the hypothesis of no environment-based selection, we find a mean 2pcf for background structures of -0.049 on scales of ≲40 arcmin, significantly non-zero at ~4σ, which means that Planck clusters are more likely to be detected in regions of low background density. We hypothesize this effect arises either from background estimation in the SZ survey or from radio sources in the background. We estimate the defect in SZ signal caused by this effect to be negligibly small, of the order of ~10^-4 of the signal of a typical Planck detection. Analogously, there are no implications on X-ray mass measurements. However, the environmental dependence has important consequences for weak lensing follow up of Planck galaxy clusters: we predict that projection effects account for half of the mass contained within a 15 arcmin radius of Planck galaxy clusters. We did not detect a background underdensity of CMASS LRGs, which also leaves a spatially varying redshift dependence of the Planck SZ selection function as a possible cause for our findings.

Investigating role stress in frontline bank employees: A cluster based approach

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Arti Devi

2013-09-01

Full Text Available An effective role stress management programme would benefit from a segmentation of employees based on their experience of role stressors. This study explores role stressor based segments of frontline bank employees towards providing a framework for designing such a programme. Cluster analysis on a random sample of 501 frontline employees of commercial banks in Jammu and Kashmir (India revealed three distinct segments – “overloaded employees”, “unclear employees”, and “underutilised employees”, based on their experience of role stressors. The findings suggest a customised approach to role stress management, with the role stress management programme designed to address cluster specific needs.

The anterior interhemispheric approach: a safe and effective approach to anterior skull base lesions.

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Mielke, Dorothee; Mayfrank, Lothar; Psychogios, Marios Nikos; Rohde, Veit

2014-04-01

Many approaches to the anterior skull base have been reported. Frequently used are the pterional, the unilateral or bilateral frontobasal, the supraorbital and the frontolateral approach. Recently, endoscopic transnasal approaches have become more popular. The benefits of each approach has to be weighted against its complications and limitations. The aim of this study was to investigate if the anterior interhemispheric approach (AIA) could be a safe and effective alternative approach to tumorous and non-tumorous lesions of the anterior skull base. We screened the operative records of all patients with an anterior skull base lesion undergoing transcranial surgery. We have used the AIA in 61 patients. These were exclusively patients with either olfactory groove meningioma (OGM) (n = 43), ethmoidal dural arteriovenous fistula (dAVF) ( n = 6) or frontobasal fractures of the anterior midline with cerebrospinal fluid (CSF) leakage ( n = 12). Patient records were evaluated concerning accessibility of the lesion, realization of surgical aims (complete tumor removal, dAVF obliteration, closure of the dural tear), and approach related complications. The use of the AIA exclusively in OGMs, ethmoidal dAVFs and midline frontobasal fractures indicated that we considered lateralized frontobasal lesions not suitable to be treated successfully. If restricted to these three pathologies, the AIA is highly effective and safe. The surgical aim (complete tumor removal, complete dAVF occlusion, no rhinorrhea) was achieved in all patients. The complication rate was 11.5 % (wound infection (n = 2; 3.2 %), contusion of the genu of the corpus callosum, subdural hygroma, epileptic seizure, anosmia and asymptomatic bleed into the tumor cavity (n = 1 each). Only the contusion of the corpus callosum was directly related to the approach (1.6 %). Olfaction, if present before surgery, was preserved in all patients, except one (1.6 %). The AIA is an effective and a safe approach

Distribution-based fuzzy clustering of electrical resistivity tomography images for interface detection

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Ward, W. O. C.; Wilkinson, P. B.; Chambers, J. E.; Oxby, L. S.; Bai, L.

2014-04-01

A novel method for the effective identification of bedrock subsurface elevation from electrical resistivity tomography images is described. Identifying subsurface boundaries in the topographic data can be difficult due to smoothness constraints used in inversion, so a statistical population-based approach is used that extends previous work in calculating isoresistivity surfaces. The analysis framework involves a procedure for guiding a clustering approach based on the fuzzy c-means algorithm. An approximation of resistivity distributions, found using kernel density estimation, was utilized as a means of guiding the cluster centroids used to classify data. A fuzzy method was chosen over hard clustering due to uncertainty in hard edges in the topography data, and a measure of clustering uncertainty was identified based on the reciprocal of cluster membership. The algorithm was validated using a direct comparison of known observed bedrock depths at two 3-D survey sites, using real-time GPS information of exposed bedrock by quarrying on one site, and borehole logs at the other. Results show similarly accurate detection as a leading isosurface estimation method, and the proposed algorithm requires significantly less user input and prior site knowledge. Furthermore, the method is effectively dimension-independent and will scale to data of increased spatial dimensions without a significant effect on the runtime. A discussion on the results by automated versus supervised analysis is also presented.

Internet2-based 3D PET image reconstruction using a PC cluster

International Nuclear Information System (INIS)

Shattuck, D.W.; Rapela, J.; Asma, E.; Leahy, R.M.; Chatzioannou, A.; Qi, J.

2002-01-01

We describe an approach to fast iterative reconstruction from fully three-dimensional (3D) PET data using a network of PentiumIII PCs configured as a Beowulf cluster. To facilitate the use of this system, we have developed a browser-based interface using Java. The system compresses PET data on the user's machine, sends these data over a network, and instructs the PC cluster to reconstruct the image. The cluster implements a parallelized version of our preconditioned conjugate gradient method for fully 3D MAP image reconstruction. We report on the speed-up factors using the Beowulf approach and the impacts of communication latencies in the local cluster network and the network connection between the user's machine and our PC cluster. (author)

Clustering Batik Images using Fuzzy C-Means Algorithm Based on Log-Average Luminance

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Ahmad Sanmorino

2012-06-01

Full Text Available Batik is a fabric or clothes that are made ​​with a special staining technique called wax-resist dyeing and is one of the cultural heritage which has high artistic value. In order to improve the efficiency and give better semantic to the image, some researchers apply clustering algorithm for managing images before they can be retrieved. Image clustering is a process of grouping images based on their similarity. In this paper we attempt to provide an alternative method of grouping batik image using fuzzy c-means (FCM algorithm based on log-average luminance of the batik. FCM clustering algorithm is an algorithm that works using fuzzy models that allow all data from all cluster members are formed with different degrees of membership between 0 and 1. Log-average luminance (LAL is the average value of the lighting in an image. We can compare different image lighting from one image to another using LAL. From the experiments that have been made, it can be concluded that fuzzy c-means algorithm can be used for batik image clustering based on log-average luminance of each image possessed.

A user credit assessment model based on clustering ensemble for broadband network new media service supervision

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Liu, Fang; Cao, San-xing; Lu, Rui

2012-04-01

This paper proposes a user credit assessment model based on clustering ensemble aiming to solve the problem that users illegally spread pirated and pornographic media contents within the user self-service oriented broadband network new media platforms. Its idea is to do the new media user credit assessment by establishing indices system based on user credit behaviors, and the illegal users could be found according to the credit assessment results, thus to curb the bad videos and audios transmitted on the network. The user credit assessment model based on clustering ensemble proposed by this paper which integrates the advantages that swarm intelligence clustering is suitable for user credit behavior analysis and K-means clustering could eliminate the scattered users existed in the result of swarm intelligence clustering, thus to realize all the users' credit classification automatically. The model's effective verification experiments are accomplished which are based on standard credit application dataset in UCI machine learning repository, and the statistical results of a comparative experiment with a single model of swarm intelligence clustering indicates this clustering ensemble model has a stronger creditworthiness distinguishing ability, especially in the aspect of predicting to find user clusters with the best credit and worst credit, which will facilitate the operators to take incentive measures or punitive measures accurately. Besides, compared with the experimental results of Logistic regression based model under the same conditions, this clustering ensemble model is robustness and has better prediction accuracy.

Constraints on Ωm and σ8 from the potential-based cluster temperature function

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Angrick, Christian; Pace, Francesco; Bartelmann, Matthias; Roncarelli, Mauro

2015-12-01

The abundance of galaxy clusters is in principle a powerful tool to constrain cosmological parameters, especially Ωm and σ8, due to the exponential dependence in the high-mass regime. While the best observables are the X-ray temperature and luminosity, the abundance of galaxy clusters, however, is conventionally predicted as a function of mass. Hence, the intrinsic scatter and the uncertainties in the scaling relations between mass and either temperature or luminosity lower the reliability of galaxy clusters to constrain cosmological parameters. In this article, we further refine the X-ray temperature function for galaxy clusters by Angrick et al., which is based on the statistics of perturbations in the cosmic gravitational potential and proposed to replace the classical mass-based temperature function, by including a refined analytic merger model and compare the theoretical prediction to results from a cosmological hydrodynamical simulation. Although we find already a good agreement if we compare with a cluster temperature function based on the mass-weighted temperature, including a redshift-dependent scaling between mass-based and spectroscopic temperature yields even better agreement between theoretical model and numerical results. As a proof of concept, incorporating this additional scaling in our model, we constrain the cosmological parameters Ωm and σ8 from an X-ray sample of galaxy clusters and tentatively find agreement with the recent cosmic microwave background based results from the Planck mission at 1σ-level.

DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer.

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Saha, Abhijoy; Banerjee, Sayantan; Kurtek, Sebastian; Narang, Shivali; Lee, Joonsang; Rao, Ganesh; Martinez, Juan; Bharath, Karthik; Rao, Arvind U K; Baladandayuthapani, Veerabhadran

2016-01-01

Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data has enabled us to evaluate tumor heterogeneity on multiple levels. In this work, we examine magnetic resonance imaging (MRI) in patients with brain cancer to assess image-based tumor heterogeneity. Standard approaches to this problem use scalar summary measures (e.g., intensity-based histogram statistics) that do not adequately capture the complete and finer scale information in the voxel-level data. In this paper, we introduce a novel technique, DEMARCATE (DEnsity-based MAgnetic Resonance image Clustering for Assessing Tumor hEterogeneity) to explore the entire tumor heterogeneity density profiles (THDPs) obtained from the full tumor voxel space. THDPs are smoothed representations of the probability density function of the tumor images. We develop tools for analyzing such objects under the Fisher-Rao Riemannian framework that allows us to construct metrics for THDP comparisons across patients, which can be used in conjunction with standard clustering approaches. Our analyses of The Cancer Genome Atlas (TCGA) based Glioblastoma dataset reveal two significant clusters of patients with marked differences in tumor morphology, genomic characteristics and prognostic clinical outcomes. In addition, we see enrichment of image-based clusters with known molecular subtypes of glioblastoma multiforme, which further validates our representation of tumor heterogeneity and subsequent clustering techniques.

Collaborative filtering recommendation model based on fuzzy clustering algorithm

Science.gov (United States)

Yang, Ye; Zhang, Yunhua

2018-05-01

As one of the most widely used algorithms in recommender systems, collaborative filtering algorithm faces two serious problems, which are the sparsity of data and poor recommendation effect in big data environment. In traditional clustering analysis, the object is strictly divided into several classes and the boundary of this division is very clear. However, for most objects in real life, there is no strict definition of their forms and attributes of their class. Concerning the problems above, this paper proposes to improve the traditional collaborative filtering model through the hybrid optimization of implicit semantic algorithm and fuzzy clustering algorithm, meanwhile, cooperating with collaborative filtering algorithm. In this paper, the fuzzy clustering algorithm is introduced to fuzzy clustering the information of project attribute, which makes the project belong to different project categories with different membership degrees, and increases the density of data, effectively reduces the sparsity of data, and solves the problem of low accuracy which is resulted from the inaccuracy of similarity calculation. Finally, this paper carries out empirical analysis on the MovieLens dataset, and compares it with the traditional user-based collaborative filtering algorithm. The proposed algorithm has greatly improved the recommendation accuracy.

Neural network based cluster creation in the ATLAS silicon pixel detector

CERN Document Server

Selbach, K E; The ATLAS collaboration

2012-01-01

The read-out from individual pixels on planar semi-conductor sensors are grouped into clusters to reconstruct the location where a charged particle passed through the sensor. The resolution given by individual pixel sizes is significantly improved by using the information from the charge sharing between pixels. Such analog cluster creation techniques have been used by the ATLAS experiment for many years to obtain an excellent performance. However, in dense environments, such as those inside high-energy jets, clusters have an increased probability of merging the charge deposited by multiple particles. Recently, a neural network based algorithm which estimates both the cluster position and whether a cluster should be split has been developed for the ATLAS pixel detector. The algorithm significantly reduces ambiguities in the assignment of pixel detector measurement to tracks within jets and improves the position accuracy with respect to standard interpolation techniques by taking into account the 2-dimensional ...

Neural network based cluster creation in the ATLAS silicon Pixel Detector

CERN Document Server

Andreazza, A; The ATLAS collaboration

2013-01-01

The read-out from individual pixels on planar semi-conductor sensors are grouped into clusters to reconstruct the location where a charged particle passed through the sensor. The resolution given by individual pixel sizes is significantly improved by using the information from the charge sharing between pixels. Such analog cluster creation techniques have been used by the ATLAS experiment for many years to obtain an excellent performance. However, in dense environments, such as those inside high-energy jets, clusters have an increased probability of merging the charge deposited by multiple particles. Recently, a neural network based algorithm which estimates both the cluster position and whether a cluster should be split has been developed for the ATLAS Pixel Detector. The algorithm significantly reduces ambiguities in the assignment of pixel detector measurement to tracks within jets and improves the position accuracy with respect to standard interpolation techniques by taking into account the 2-dimensional ...

Effect of MTA-based sealer on the healing of periapical lesions

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Joao Eduardo GOMES-FILHO

2013-06-01

Full Text Available Some manufacturers have recently added specific components to improve the ease of handling and insertion material properties of MTA in order to create MTA-based sealers. Objective The aim of this study was to evaluate the healing of periapical lesions in canine teeth after a single session of endodontic treatment with MTA Fillapex® compared with Sealapex® or Endo-CPM-Sealer®. Material and Methods Sixty-two root canals were performed on two 1-year-old male dogs. After coronal access and pulp extirpation, the canals were exposed to the oral cavity for 6 months in order to induce periapical lesions. The root canals were prepared, irrigated with a solution of 2.5% sodium hypochlorite and filled with gutta-percha and different sealers, according to the following groups: 1 Sealapex®; 2 Endo-CPM-Sealer®; and 3 MTA Fillapex®. Some teeth with periapical lesions were left untreated for use as positive controls. Healthy teeth were used as negative controls. After 6 months, the animals were sacrificed and serial sections from the roots were prepared for histomorphologic analysis and stained with hematoxylin and eosin and the Brown and Brenn technique. The lesions were scored according to pre-established histomorphologic parameters and the scores statistically analyzed using the Kruskal-Wallis test. Results All 3 materials produced similar patterns of healing (p>0.05; in particular, persistent inflammation and absence of complete periapical tissue healing were consistently noted. Conclusions Preparation of the infected root canals followed by filling with the materials studied was insufficient to provide complete healing of the periapical tissues.

Candidate lesion-based criteria for defining a positive sacroiliac joint MRI in two cohorts of patients with axial spondyloarthritis

DEFF Research Database (Denmark)

Weber, Ulrich; Østergaard, Mikkel; Lambert, Robert G W

2015-01-01

OBJECTIVE: To determine candidate lesion-based criteria for a positive sacroiliac joint (SIJ) MRI based on bone marrow oedema (BMO) and/or erosion in non-radiographic axial spondyloarthritis (nr-axSpA); to compare the performance of lesion-based criteria with global evaluation by expert readers. ...... for classification of axial SpA, reflecting the contextual information provided by T1SE and STIR sequences....

Impact of a new respiratory amplitude-based gating technique in evaluation of upper abdominal PET lesions

Energy Technology Data Exchange (ETDEWEB)

Van Der Gucht, Axel, E-mail: axel.vandergucht@gmail.com [Department of Nuclear Medicine, Centre Hospitalier Princesse Grace, Monaco (Monaco); Serrano, Benjamin [Department of Medical Physics, Centre Hospitalier Princesse Grace, Monaco (Monaco); Hugonnet, Florent; Paulmier, Benoît [Department of Nuclear Medicine, Centre Hospitalier Princesse Grace, Monaco (Monaco); Garnier, Nicolas [Department of Medical Physics, Centre Hospitalier Princesse Grace, Monaco (Monaco); Faraggi, Marc [Department of Nuclear Medicine, Centre Hospitalier Princesse Grace, Monaco (Monaco)

2014-03-15

PET acquisition requires several minutes which can lead to respiratory motion blurring, to increase partial volume effect and SUV under-estimation. To avoid these artifacts, conventional 10-min phase-based respiratory gating (PBRG) can be performed but is time-consuming and difficult with a non-compliant patient. We evaluated an automatic amplitude-based gating method (AABG) which keeps 35% of the counts at the end of expiration to minimize respiratory motion. We estimated the impact of AABG on upper abdominal lesion detectability, quantification and patient management. Methods: We consecutively included 31 patients (82 hepatic and 25 perihepatic known lesions). Each patient underwent 3 acquisitions on a Siemens Biograph mCT (4 rings and time-of-flight): a standard free-breathing whole-body (SWB, 5–7 steps/2.5 min per step, 3.3 ± 0.4 MBq/kg of 18F-FDG), a 10-min PBRG with six bins and a 5-min AABG method. All gated acquisitions were performed with an ANZAI respiratory gating system. SUV{sub max} and target to background ratio (TBR, defined as the maximum SUV of the lesion divided by the mean SUV of a region of interest drawn in healthy liver) were compared. Results: All 94 lesions in SWB images were detected in the gated images. 10-min PBRG and 5-min AABG acquisitions respectively revealed 9 and 13 new lesions and relocated 7 and 8 lesions. Four lesions revealed by 5-min AABG were missed by 10-min PBRG in 3 non-compliant patients. Both gated methods failed to relocate 2 lesions seen on SWB acquisition. Compared to SWB, TBR increased significantly with 10-min PBRG and with 5-min AABG (respectively 41 ± 59%, p = 4.10–3 and 66 ± 75%, p = 6.10–5) whereas SUV{sub max} did not (respectively 14 ± 43%, p = 0.29 with 10-min PBRG, and 24 ± 46%, p = 0.11 with 5-min AABG). Conclusion: The AABG is a fast and a user-friendly respiratory gating method to increase detectability and quantification of upper abdominal lesions compared to the conventional PBRG procedure and

Reconstruction of a digital core containing clay minerals based on a clustering algorithm.

Science.gov (United States)

He, Yanlong; Pu, Chunsheng; Jing, Cheng; Gu, Xiaoyu; Chen, Qingdong; Liu, Hongzhi; Khan, Nasir; Dong, Qiaoling

2017-10-01

It is difficult to obtain a core sample and information for digital core reconstruction of mature sandstone reservoirs around the world, especially for an unconsolidated sandstone reservoir. Meanwhile, reconstruction and division of clay minerals play a vital role in the reconstruction of the digital cores, although the two-dimensional data-based reconstruction methods are specifically applicable as the microstructure reservoir simulation methods for the sandstone reservoir. However, reconstruction of clay minerals is still challenging from a research viewpoint for the better reconstruction of various clay minerals in the digital cores. In the present work, the content of clay minerals was considered on the basis of two-dimensional information about the reservoir. After application of the hybrid method, and compared with the model reconstructed by the process-based method, the digital core containing clay clusters without the labels of the clusters' number, size, and texture were the output. The statistics and geometry of the reconstruction model were similar to the reference model. In addition, the Hoshen-Kopelman algorithm was used to label various connected unclassified clay clusters in the initial model and then the number and size of clay clusters were recorded. At the same time, the K-means clustering algorithm was applied to divide the labeled, large connecting clusters into smaller clusters on the basis of difference in the clusters' characteristics. According to the clay minerals' characteristics, such as types, textures, and distributions, the digital core containing clay minerals was reconstructed by means of the clustering algorithm and the clay clusters' structure judgment. The distributions and textures of the clay minerals of the digital core were reasonable. The clustering algorithm improved the digital core reconstruction and provided an alternative method for the simulation of different clay minerals in the digital cores.

Reconstruction of a digital core containing clay minerals based on a clustering algorithm

Science.gov (United States)

He, Yanlong; Pu, Chunsheng; Jing, Cheng; Gu, Xiaoyu; Chen, Qingdong; Liu, Hongzhi; Khan, Nasir; Dong, Qiaoling

2017-10-01

It is difficult to obtain a core sample and information for digital core reconstruction of mature sandstone reservoirs around the world, especially for an unconsolidated sandstone reservoir. Meanwhile, reconstruction and division of clay minerals play a vital role in the reconstruction of the digital cores, although the two-dimensional data-based reconstruction methods are specifically applicable as the microstructure reservoir simulation methods for the sandstone reservoir. However, reconstruction of clay minerals is still challenging from a research viewpoint for the better reconstruction of various clay minerals in the digital cores. In the present work, the content of clay minerals was considered on the basis of two-dimensional information about the reservoir. After application of the hybrid method, and compared with the model reconstructed by the process-based method, the digital core containing clay clusters without the labels of the clusters' number, size, and texture were the output. The statistics and geometry of the reconstruction model were similar to the reference model. In addition, the Hoshen-Kopelman algorithm was used to label various connected unclassified clay clusters in the initial model and then the number and size of clay clusters were recorded. At the same time, the K -means clustering algorithm was applied to divide the labeled, large connecting clusters into smaller clusters on the basis of difference in the clusters' characteristics. According to the clay minerals' characteristics, such as types, textures, and distributions, the digital core containing clay minerals was reconstructed by means of the clustering algorithm and the clay clusters' structure judgment. The distributions and textures of the clay minerals of the digital core were reasonable. The clustering algorithm improved the digital core reconstruction and provided an alternative method for the simulation of different clay minerals in the digital cores.

Research on Bridge Sensor Validation Based on Correlation in Cluster

Directory of Open Access Journals (Sweden)

Huang Xiaowei

2016-01-01

Full Text Available In order to avoid the false alarm and alarm failure caused by sensor malfunction or failure, it has been critical to diagnose the fault and analyze the failure of the sensor measuring system in major infrastructures. Based on the real time monitoring of bridges and the study on the correlation probability distribution between multisensors adopted in the fault diagnosis system, a clustering algorithm based on k-medoid is proposed, by dividing sensors of the same type into k clusters. Meanwhile, the value of k is optimized by a specially designed evaluation function. Along with the further study of the correlation of sensors within the same cluster, this paper presents the definition and corresponding calculation algorithm of the sensor’s validation. The algorithm is applied to the analysis of the sensor data from an actual health monitoring system. The result reveals that the algorithm can not only accurately measure the failure degree and orientate the malfunction in time domain but also quantitatively evaluate the performance of sensors and eliminate error of diagnosis caused by the failure of the reference sensor.

Hidden in Plain Sight: Subtle Effects of the 8-Oxoguanine Lesion on the Structure, Dynamics, and Thermodynamics of a 15-Base-Pair Oligodeoxynucleotide Duplex†

Science.gov (United States)

Crenshaw, Charisse M.; Wade, Jacqueline E.; Arthanari, Haribabu; Frueh, Dominique; Lane, Benjamin F.; Núñez, Megan E.

2011-01-01

The base lesion 8-oxoguanine is formed readily by oxidation of DNA, potentially leading to G→T transversion mutations. Despite the apparent similarity of 8-oxoguanine-cytosine base pairs to normal guanine-cytosine base pairs, cellular base excision repair systems effectively recognize the lesion base. Here we apply several techniques to examine a single 8-oxoguanine lesion at the center of a nonpalindromic 15-mer duplex oligonucleotide in an effort to determine what, if anything, distinguishes an 8-oxoguanine-cytosine base pair from a normal base pair. The lesion duplex is globally almost indistinguishable from the unmodified parent duplex using CD spectroscopy and UV melting thermodynamics. The DNA mismatch-detecting photocleavage agent Rh(bpy)2chrysi3+ cleaves only weakly and nonspecifically, revealing that the 8oxoG-C pair is locally stable at the level of the individual base pairs. NMR spectra are also consistent with a well-conserved B-form duplex structure. In the 2D NOESY spectra, base-sugar and imino-imino crosspeaks are strikingly similar between parent and lesion duplexes. Changes in chemical shift due to the 8oxoG lesion are localized to its complementary cytosine and to the 2–3 base pairs immediately flanking the lesion on the lesion strand. Residues further removed from the lesion are shown to be unperturbed by its presence. Notably, imino exchange experiments indicate that the 8-oxoguanine-cytosine pair is strong and stable, with an apparent equilibrium constant for opening equal to that of other internal guanine-cytosine base pairs, on the order of 10−6. This collection of experiments shows that the 8-oxoguanine-cytosine base pair is incredibly stable and similar to the native pair. PMID:21902242

Cosmological constraints with clustering-based redshifts

Science.gov (United States)

Kovetz, Ely D.; Raccanelli, Alvise; Rahman, Mubdi

2017-07-01

We demonstrate that observations lacking reliable redshift information, such as photometric and radio continuum surveys, can produce robust measurements of cosmological parameters when empowered by clustering-based redshift estimation. This method infers the redshift distribution based on the spatial clustering of sources, using cross-correlation with a reference data set with known redshifts. Applying this method to the existing Sloan Digital Sky Survey (SDSS) photometric galaxies, and projecting to future radio continuum surveys, we show that sources can be efficiently divided into several redshift bins, increasing their ability to constrain cosmological parameters. We forecast constraints on the dark-energy equation of state and on local non-Gaussianity parameters. We explore several pertinent issues, including the trade-off between including more sources and minimizing the overlap between bins, the shot-noise limitations on binning and the predicted performance of the method at high redshifts, and most importantly pay special attention to possible degeneracies with the galaxy bias. Remarkably, we find that once this technique is implemented, constraints on dynamical dark energy from the SDSS imaging catalogue can be competitive with, or better than, those from the spectroscopic BOSS survey and even future planned experiments. Further, constraints on primordial non-Gaussianity from future large-sky radio-continuum surveys can outperform those from the Planck cosmic microwave background experiment and rival those from future spectroscopic galaxy surveys. The application of this method thus holds tremendous promise for cosmology.

Possible world based consistency learning model for clustering and classifying uncertain data.

Science.gov (United States)

Liu, Han; Zhang, Xianchao; Zhang, Xiaotong

2018-06-01

Possible world has shown to be effective for handling various types of data uncertainty in uncertain data management. However, few uncertain data clustering and classification algorithms are proposed based on possible world. Moreover, existing possible world based algorithms suffer from the following issues: (1) they deal with each possible world independently and ignore the consistency principle across different possible worlds; (2) they require the extra post-processing procedure to obtain the final result, which causes that the effectiveness highly relies on the post-processing method and the efficiency is also not very good. In this paper, we propose a novel possible world based consistency learning model for uncertain data, which can be extended both for clustering and classifying uncertain data. This model utilizes the consistency principle to learn a consensus affinity matrix for uncertain data, which can make full use of the information across different possible worlds and then improve the clustering and classification performance. Meanwhile, this model imposes a new rank constraint on the Laplacian matrix of the consensus affinity matrix, thereby ensuring that the number of connected components in the consensus affinity matrix is exactly equal to the number of classes. This also means that the clustering and classification results can be directly obtained without any post-processing procedure. Furthermore, for the clustering and classification tasks, we respectively derive the efficient optimization methods to solve the proposed model. Experimental results on real benchmark datasets and real world uncertain datasets show that the proposed model outperforms the state-of-the-art uncertain data clustering and classification algorithms in effectiveness and performs competitively in efficiency. Copyright © 2018 Elsevier Ltd. All rights reserved.

Hierarchical video summarization based on context clustering

Science.gov (United States)

Tseng, Belle L.; Smith, John R.

2003-11-01

A personalized video summary is dynamically generated in our video personalization and summarization system based on user preference and usage environment. The three-tier personalization system adopts the server-middleware-client architecture in order to maintain, select, adapt, and deliver rich media content to the user. The server stores the content sources along with their corresponding MPEG-7 metadata descriptions. In this paper, the metadata includes visual semantic annotations and automatic speech transcriptions. Our personalization and summarization engine in the middleware selects the optimal set of desired video segments by matching shot annotations and sentence transcripts with user preferences. Besides finding the desired contents, the objective is to present a coherent summary. There are diverse methods for creating summaries, and we focus on the challenges of generating a hierarchical video summary based on context information. In our summarization algorithm, three inputs are used to generate the hierarchical video summary output. These inputs are (1) MPEG-7 metadata descriptions of the contents in the server, (2) user preference and usage environment declarations from the user client, and (3) context information including MPEG-7 controlled term list and classification scheme. In a video sequence, descriptions and relevance scores are assigned to each shot. Based on these shot descriptions, context clustering is performed to collect consecutively similar shots to correspond to hierarchical scene representations. The context clustering is based on the available context information, and may be derived from domain knowledge or rules engines. Finally, the selection of structured video segments to generate the hierarchical summary efficiently balances between scene representation and shot selection.

Incidence of chronic knee lesions in long-distance runners based on training level: Findings at MRI

International Nuclear Information System (INIS)

Schueller-Weidekamm, Claudia; Schueller, Gerd; Uffmann, Martin; Bader, Till

2006-01-01

Purpose: The purpose of this study was to evaluate the incidence of chronic knee changes in long-distance runners based on the training status, including distance, running frequency, training pace, and running experience. Methods: MRI of the knee was performed in 26 non-professional runners 5 days after their last training unit. Lesions of the menisci and cartilage (5-point scale), bone marrow and ligaments (3-point scale), and joint effusion were evaluated. A total score comprising all knee lesions in each runner was evaluated. The incidence of the knee changes was correlated with the training level, gender, and age of the runners. Results: Grade 1 lesions of the menisci were found in six runners with a high training level, and in only four runners with a low training level. Grade 1 cartilage lesions were found in three high-trained runners and in one low-trained runner, and grade 2 lesions were found in one high-trained runner and in two low-trained runners, respectively. Grade 1 anterior cruciate ligament lesions were seen in three runners with a high- and in two runners with a low-training level. Runners with a higher training level showed a statistically significant higher score for all chronic knee lesions than those with a lower training level (p < 0.05). Conclusions: MRI findings indicate that a higher training level in long-distance runners is a risk factor for chronic knee lesions

Incidence of chronic knee lesions in long-distance runners based on training level: Findings at MRI

Energy Technology Data Exchange (ETDEWEB)

Schueller-Weidekamm, Claudia [Department of Radiology, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna (Austria)]. E-mail: claudia.schueller-weidekamm@meduniwien.ac.at; Schueller, Gerd [Department of Radiology, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna (Austria); Uffmann, Martin [Department of Radiology, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna (Austria); Bader, Till [Department of Radiology, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna (Austria)

2006-05-15

Purpose: The purpose of this study was to evaluate the incidence of chronic knee changes in long-distance runners based on the training status, including distance, running frequency, training pace, and running experience. Methods: MRI of the knee was performed in 26 non-professional runners 5 days after their last training unit. Lesions of the menisci and cartilage (5-point scale), bone marrow and ligaments (3-point scale), and joint effusion were evaluated. A total score comprising all knee lesions in each runner was evaluated. The incidence of the knee changes was correlated with the training level, gender, and age of the runners. Results: Grade 1 lesions of the menisci were found in six runners with a high training level, and in only four runners with a low training level. Grade 1 cartilage lesions were found in three high-trained runners and in one low-trained runner, and grade 2 lesions were found in one high-trained runner and in two low-trained runners, respectively. Grade 1 anterior cruciate ligament lesions were seen in three runners with a high- and in two runners with a low-training level. Runners with a higher training level showed a statistically significant higher score for all chronic knee lesions than those with a lower training level (p < 0.05). Conclusions: MRI findings indicate that a higher training level in long-distance runners is a risk factor for chronic knee lesions.

K-Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data

Directory of Open Access Journals (Sweden)

Cheng Lu

2015-01-01

Full Text Available The Affinity Propagation (AP algorithm is an effective algorithm for clustering analysis, but it can not be directly applicable to the case of incomplete data. In view of the prevalence of missing data and the uncertainty of missing attributes, we put forward a modified AP clustering algorithm based on K-nearest neighbor intervals (KNNI for incomplete data. Based on an Improved Partial Data Strategy, the proposed algorithm estimates the KNNI representation of missing attributes by using the attribute distribution information of the available data. The similarity function can be changed by dealing with the interval data. Then the improved AP algorithm can be applicable to the case of incomplete data. Experiments on several UCI datasets show that the proposed algorithm achieves impressive clustering results.

Imaging of lipids in atherosclerotic lesion in aorta from ApoE/LDLR-/- mice by FT-IR spectroscopy and Hierarchical Cluster Analysis.

Science.gov (United States)

P Wrobel, Tomasz; Mateuszuk, Lukasz; Chlopicki, Stefan; Malek, Kamilla; Baranska, Malgorzata

2011-12-21

Spectroscopy-based approaches can provide an insight into the biochemical composition of a tissue sample. In the present work Fourier transform infrared (FT-IR) spectroscopy was used to develop a reliable methodology to study the content of free fatty acids, triglycerides, cholesteryl esters as well as cholesterol in aorta from mice with atherosclerosis (ApoE/LDLR(-/-) mice). In particular, distribution and concentration of palmitic, oleic and linoleic acid derivatives were analyzed. Spectral analysis of pure compounds allowed for clear discrimination between free fatty acids and other similar moieties based on the carbonyl band position (1699-1710 cm(-1) range). In order to distinguish cholesteryl esters from triglycerides a ratio of carbonyl band to signal at 1010 cm(-1) was used. Imaging of lipids in atherosclerotic aortic lesions in ApoE/LDLR(-/-) mice was followed by Hierarchical Cluster Analysis (HCA). The aorta from C57Bl/6J control mice (fed with chow diet) was used for comparison. The measurements were completed with an FT-IR spectrometer equipped with a 128 × 128 FPA detector. In cross-section of aorta from ApoE/LDLR(-/-) mice a region of atherosclerotic plaque was clearly identified by HCA, which was later divided into 2 sub-regions, one characterized by the higher content of cholesterol, while the other by higher contents of cholesteryl esters. HCA of tissues deposited on normal microscopic glass, hence limited to the 2200-3800 cm(-1) spectral range, also identified a region of atherosclerotic plaque. Importantly, this region correlates with the area stained by standard histological staining for atherosclerotic plaque (Oil Red O). In conclusion, the use of FT-IR and HCA may provide a novel tool for qualitative and quantitative analysis of contents and distribution of lipids in atherosclerotic plaque.

Cluster management.

Science.gov (United States)

Katz, R

1992-11-01

Cluster management is a management model that fosters decentralization of management, develops leadership potential of staff, and creates ownership of unit-based goals. Unlike shared governance models, there is no formal structure created by committees and it is less threatening for managers. There are two parts to the cluster management model. One is the formation of cluster groups, consisting of all staff and facilitated by a cluster leader. The cluster groups function for communication and problem-solving. The second part of the cluster management model is the creation of task forces. These task forces are designed to work on short-term goals, usually in response to solving one of the unit's goals. Sometimes the task forces are used for quality improvement or system problems. Clusters are groups of not more than five or six staff members, facilitated by a cluster leader. A cluster is made up of individuals who work the same shift. For example, people with job titles who work days would be in a cluster. There would be registered nurses, licensed practical nurses, nursing assistants, and unit clerks in the cluster. The cluster leader is chosen by the manager based on certain criteria and is trained for this specialized role. The concept of cluster management, criteria for choosing leaders, training for leaders, using cluster groups to solve quality improvement issues, and the learning process necessary for manager support are described.

Energy-Efficient Cluster Based Routing Protocol in Mobile Ad Hoc Networks Using Network Coding

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Srinivas Kanakala

2014-01-01

Full Text Available In mobile ad hoc networks, all nodes are energy constrained. In such situations, it is important to reduce energy consumption. In this paper, we consider the issues of energy efficient communication in MANETs using network coding. Network coding is an effective method to improve the performance of wireless networks. COPE protocol implements network coding concept to reduce number of transmissions by mixing the packets at intermediate nodes. We incorporate COPE into cluster based routing protocol to further reduce the energy consumption. The proposed energy-efficient coding-aware cluster based routing protocol (ECCRP scheme applies network coding at cluster heads to reduce number of transmissions. We also modify the queue management procedure of COPE protocol to further improve coding opportunities. We also use an energy efficient scheme while selecting the cluster head. It helps to increase the life time of the network. We evaluate the performance of proposed energy efficient cluster based protocol using simulation. Simulation results show that the proposed ECCRP algorithm reduces energy consumption and increases life time of the network.

Base-pairing preferences, physicochemical properties and mutational behaviour of the DNA lesion 8-nitroguanine.

Science.gov (United States)

Bhamra, Inder; Compagnone-Post, Patricia; O'Neil, Ian A; Iwanejko, Lesley A; Bates, Andrew D; Cosstick, Richard

2012-11-01

8-Nitro-2'-deoxyguanosine (8-nitrodG) is a relatively unstable, mutagenic lesion of DNA that is increasingly believed to be associated with tissue inflammation. Due to the lability of the glycosidic bond, 8-nitrodG cannot be incorporated into oligodeoxynucleotides (ODNs) by chemical DNA synthesis and thus very little is known about its physicochemical properties and base-pairing preferences. Here we describe the synthesis of 8-nitro-2'-O-methylguanosine, a ribonucleoside analogue of this lesion, which is sufficiently stable to be incorporated into ODNs. Physicochemical studies demonstrated that 8-nitro-2'-O-methylguanosine adopts a syn conformation about the glycosidic bond; thermal melting studies and molecular modelling suggest a relatively stable syn-8-nitroG·anti-G base pair. Interestingly, when this lesion analogue was placed in a primer-template system, extension of the primer by either avian myeloblastosis virus reverse transcriptase (AMV-RT) or human DNA polymerase β (pol β), was significantly impaired, but where incorporation opposite 8-nitroguanine did occur, pol β showed a 2:1 preference to insert dA over dC, while AMV-RT incorporated predominantly dC. The fact that no 8-nitroG·G base pairing is seen in the primer extension products suggests that the polymerases may discriminate against this pairing system on the basis of its poor geometric match to a Watson-Crick pair.

Base-pairing preferences, physicochemical properties and mutational behaviour of the DNA lesion 8-nitroguanine†

Science.gov (United States)

Bhamra, Inder; Compagnone-Post, Patricia; O’Neil, Ian A.; Iwanejko, Lesley A.; Bates, Andrew D.; Cosstick, Richard

2012-01-01

8-Nitro-2′-deoxyguanosine (8-nitrodG) is a relatively unstable, mutagenic lesion of DNA that is increasingly believed to be associated with tissue inflammation. Due to the lability of the glycosidic bond, 8-nitrodG cannot be incorporated into oligodeoxynucleotides (ODNs) by chemical DNA synthesis and thus very little is known about its physicochemical properties and base-pairing preferences. Here we describe the synthesis of 8-nitro-2′-O-methylguanosine, a ribonucleoside analogue of this lesion, which is sufficiently stable to be incorporated into ODNs. Physicochemical studies demonstrated that 8-nitro-2′-O-methylguanosine adopts a syn conformation about the glycosidic bond; thermal melting studies and molecular modelling suggest a relatively stable syn-8-nitroG·anti-G base pair. Interestingly, when this lesion analogue was placed in a primer-template system, extension of the primer by either avian myeloblastosis virus reverse transcriptase (AMV-RT) or human DNA polymerase β (pol β), was significantly impaired, but where incorporation opposite 8-nitroguanine did occur, pol β showed a 2:1 preference to insert dA over dC, while AMV-RT incorporated predominantly dC. The fact that no 8-nitroG·G base pairing is seen in the primer extension products suggests that the polymerases may discriminate against this pairing system on the basis of its poor geometric match to a Watson–Crick pair. PMID:22965127

Convergence Analysis of Micro-Lesions (CAML: An approach to mapping of diffuse lesions from carotid revascularization

Directory of Open Access Journals (Sweden)

Allyson C. Rosen

Full Text Available Carotid revascularization (endarterectomy, stenting prevents stroke; however, procedure-related embolization is common and results in small brain lesions easily identified by diffusion weighted magnetic resonance imaging (DWI. A crucial barrier to understanding the clinical significance of these lesions has been the lack of a statistical approach to identify vulnerable brain areas. The problem is that the lesions are small, numerous, and non-overlapping. Here we address this problem with a new method, the Convergence Analysis of Micro-Lesions (CAML technique, an extension of the Anatomic Likelihood Analysis (ALE. The method combines manual lesion tracing, constraints based on known lesion patterns, and convergence analysis to represent regions vulnerable to lesions as probabilistic brain atlases. Two studies were conducted over the course of 12 years in an active, vascular surgery clinic. An analysis in an initial group of 126 patients at 1.5 T MRI was cross-validated in a second group of 80 patients at 3T MRI. In CAML, lesions were manually defined and center points identified. Brains were aligned according to side of surgery since this factor powerfully determines lesion distribution. A convergence based analysis, was performed on each of these groups. Results indicated the most consistent region of vulnerability was in motor and premotor cortex regions. Smaller regions common to both groups included the dorsolateral prefrontal cortex and medial parietal regions. Vulnerability of motor cortex is consistent with previous work showing changes in hand dexterity associated with these procedures. The consistency of CAML also demonstrates the feasibility of this new approach to characterize small, diffuse, non-overlapping lesions in patients with multifocal pathologies. Keywords: Embolization, DWI, ALE

Object-action dissociation: A voxel-based lesion-symptom mapping study on 102 patients after glioma removal

Directory of Open Access Journals (Sweden)

Alberto Pisoni

Full Text Available Data concerning the neural basis of noun and verb processing are inconsistent. Some authors assume that action-verb processing is based on frontal areas while nouns processing relies on temporal regions; others argue that the circuits processing verbs and nouns are closely interconnected in a predominantly left-lateralized fronto-temporal-parietal network; yet, other researchers consider that the primary motor cortex plays a crucial role in processing action verbs. In the present study, one hundred and two patients with a tumour either in the right or left hemisphere were submitted to picture naming of objects and actions before and after surgery. To test the effect of specific brain regions in object and action naming, patients' lesions were mapped and voxel-lesion-symptom mapping (VLSM was computed. Behavioural results showed that left-brain damaged patients were significantly more impaired than right brain-damaged patients. The VLSM showed that these two grammatical classes are segregated in the left hemisphere. In particular, scores in naming of objects correlated with damage to the anterior temporal region, while scores in naming of actions correlated with lesions in the parietal areas and in the posterior temporal cortex. In addition, VLSM analyses carried out on non-linguistic tasks were not significant, confirming that the regions associated with deficits in object and action naming were not generally engaged in all cognitive tasks. Finally, the involvement of subcortical pathways was investigated and the inferior longitudinal fasciculus proved to play a role in object naming, while no specific bundle was identified for actions. Keywords: Object action dissociation, Temporal lesion, Frontal lesion, Voxel-based lesion symptom mapping

Cluster Physics with Merging Galaxy Clusters

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Sandor M. Molnar

2016-02-01

Full Text Available Collisions between galaxy clusters provide a unique opportunity to study matter in a parameter space which cannot be explored in our laboratories on Earth. In the standard LCDM model, where the total density is dominated by the cosmological constant ($Lambda$ and the matter density by cold dark matter (CDM, structure formation is hierarchical, and clusters grow mostly by merging.Mergers of two massive clusters are the most energetic events in the universe after the Big Bang,hence they provide a unique laboratory to study cluster physics.The two main mass components in clusters behave differently during collisions:the dark matter is nearly collisionless, responding only to gravity, while the gas is subject to pressure forces and dissipation, and shocks and turbulenceare developed during collisions. In the present contribution we review the different methods used to derive the physical properties of merging clusters. Different physical processes leave their signatures on different wavelengths, thusour review is based on a multifrequency analysis. In principle, the best way to analyze multifrequency observations of merging clustersis to model them using N-body/HYDRO numerical simulations. We discuss the results of such detailed analyses.New high spatial and spectral resolution ground and space based telescopeswill come online in the near future. Motivated by these new opportunities,we briefly discuss methods which will be feasible in the near future in studying merging clusters.

Seamless lesion insertion in digital mammography: methodology and reader study

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Pezeshk, Aria; Petrick, Nicholas; Sahiner, Berkman

2016-03-01

Collection of large repositories of clinical images containing verified cancer locations is costly and time consuming due to difficulties associated with both the accumulation of data and establishment of the ground truth. This problem poses a significant challenge to the development of machine learning algorithms that require large amounts of data to properly train and avoid overfitting. In this paper we expand the methods in our previous publications by making several modifications that significantly increase the speed of our insertion algorithms, thereby allowing them to be used for inserting lesions that are much larger in size. These algorithms have been incorporated into an image composition tool that we have made publicly available. This tool allows users to modify or supplement existing datasets by seamlessly inserting a real breast mass or micro-calcification cluster extracted from a source digital mammogram into a different location on another mammogram. We demonstrate examples of the performance of this tool on clinical cases taken from the University of South Florida Digital Database for Screening Mammography (DDSM). Finally, we report the results of a reader study evaluating the realism of inserted lesions compared to clinical lesions. Analysis of the radiologist scores in the study using receiver operating characteristic (ROC) methodology indicates that inserted lesions cannot be reliably distinguished from clinical lesions.

MODEL-BASED CLUSTERING FOR CLASSIFICATION OF AQUATIC SYSTEMS AND DIAGNOSIS OF ECOLOGICAL STRESS

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Clustering approaches were developed using the classification likelihood, the mixture likelihood, and also using a randomization approach with a model index. Using a clustering approach based on the mixture and classification likelihoods, we have developed an algorithm that...

Stigmergy based behavioural coordination for satellite clusters

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Tripp, Howard; Palmer, Phil

2010-04-01

Multi-platform swarm/cluster missions are an attractive prospect for improved science return as they provide a natural capability for temporal, spatial and signal separation with further engineering and economic advantages. As spacecraft numbers increase and/or the round-trip communications delay from Earth lengthens, the traditional "remote-control" approach begins to break down. It is therefore essential to push control into space; to make spacecraft more autonomous. An autonomous group of spacecraft requires coordination, but standard terrestrial paradigms such as negotiation, require high levels of inter-spacecraft communication, which is nontrivial in space. This article therefore introduces the principals of stigmergy as a novel method for coordinating a cluster. Stigmergy is an agent-based, behavioural approach that allows for infrequent communication with decisions based on local information. Behaviours are selected dynamically using a genetic algorithm onboard. supervisors/ground stations occasionally adjust parameters and disseminate a "common environment" that is used for local decisions. After outlining the system, an analysis of some crucial parameters such as communications overhead and number of spacecraft is presented to demonstrate scalability. Further scenarios are considered to demonstrate the natural ability to deal with dynamic situations such as the failure of spacecraft, changing mission objectives and responding to sudden bursts of high priority tasks.

Subtypes of autism by cluster analysis based on structural MRI data.

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Hrdlicka, Michal; Dudova, Iva; Beranova, Irena; Lisy, Jiri; Belsan, Tomas; Neuwirth, Jiri; Komarek, Vladimir; Faladova, Ludvika; Havlovicova, Marketa; Sedlacek, Zdenek; Blatny, Marek; Urbanek, Tomas

2005-05-01

The aim of our study was to subcategorize Autistic Spectrum Disorders (ASD) using a multidisciplinary approach. Sixty four autistic patients (mean age 9.4+/-5.6 years) were entered into a cluster analysis. The clustering analysis was based on MRI data. The clusters obtained did not differ significantly in the overall severity of autistic symptomatology as measured by the total score on the Childhood Autism Rating Scale (CARS). The clusters could be characterized as showing significant differences: Cluster 1: showed the largest sizes of the genu and splenium of the corpus callosum (CC), the lowest pregnancy order and the lowest frequency of facial dysmorphic features. Cluster 2: showed the largest sizes of the amygdala and hippocampus (HPC), the least abnormal visual response on the CARS, the lowest frequency of epilepsy and the least frequent abnormal psychomotor development during the first year of life. Cluster 3: showed the largest sizes of the caput of the nucleus caudatus (NC), the smallest sizes of the HPC and facial dysmorphic features were always present. Cluster 4: showed the smallest sizes of the genu and splenium of the CC, as well as the amygdala, and caput of the NC, the most abnormal visual response on the CARS, the highest frequency of epilepsy, the highest pregnancy order, abnormal psychomotor development during the first year of life was always present and facial dysmorphic features were always present. This multidisciplinary approach seems to be a promising method for subtyping autism.

Nucleotide Excision Repair Lesion-Recognition Protein Rad4 Captures a Pre-Flipped Partner Base in a Benzo[a]pyrene-Derived DNA Lesion: How Structure Impacts the Binding Pathway.

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Mu, Hong; Geacintov, Nicholas E; Min, Jung-Hyun; Zhang, Yingkai; Broyde, Suse

2017-06-19

The xeroderma pigmentosum C protein complex (XPC) recognizes a variety of environmentally induced DNA lesions and is the key in initiating their repair by the nucleotide excision repair (NER) pathway. When bound to a lesion, XPC flips two nucleotide pairs that include the lesion out of the DNA duplex, yielding a productively bound complex that can lead to successful lesion excision. Interestingly, the efficiencies of NER vary greatly among different lesions, influencing their toxicity and mutagenicity in cells. Though differences in XPC binding may influence NER efficiency, it is not understood whether XPC utilizes different mechanisms to achieve productive binding with different lesions. Here, we investigated the well-repaired 10R-(+)-cis-anti-benzo[a]pyrene-N 2 -dG (cis-B[a]P-dG) DNA adduct in a duplex containing normal partner C opposite the lesion. This adduct is derived from the environmental pro-carcinogen benzo[a]pyrene and is likely to be encountered by NER in the cell. We have extensively investigated its binding to the yeast XPC orthologue, Rad4, using umbrella sampling with restrained molecular dynamics simulations and free energy calculations. The NMR solution structure of this lesion in duplex DNA has shown that the dC complementary to the adducted dG is flipped out of the DNA duplex in the absence of XPC. However, it is not known whether the "pre-flipped" base would play a role in its recognition by XPC. Our results show that Rad4 first captures the displaced dC, which is followed by a tightly coupled lesion-extruding pathway for productive binding. This binding path differs significantly from the one deduced for the small cis-syn cyclobutane pyrimidine dimer lesion opposite mismatched thymines [ Mu , H. , ( 2015 ) Biochemistry , 54 ( 34 ), 5263 - 7 ]. The possibility of multiple paths that lead to productive binding to XPC is consistent with the versatile lesion recognition by XPC that is required for successful NER.

Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach

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Bellón, Beatriz; Bégué, Agnès; Lo Seen, Danny; Lebourgeois, Valentine; Evangelista, Balbino Antônio; Simões, Margareth; Demonte Ferraz, Rodrigo Peçanha

2018-06-01

Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014-2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape-clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions.

Role of isolated and clustered DNA damage and the post-irradiating repair process in the effects of heavy ion beam irradiation

International Nuclear Information System (INIS)

Tokuyama, Yuka; Terato, Hiroaki; Furusawa, Yoshiya; Ide, Hiroshi; Yasui, Akira

2015-01-01

Clustered DNA damage is a specific type of DNA damage induced by ionizing radiation. Any type of ionizing radiation traverses the target DNA molecule as a beam, inducing damage along its track. Our previous study showed that clustered DNA damage yields decreased with increased linear energy transfer (LET), leading us to investigate the importance of clustered DNA damage in the biological effects of heavy ion beam radiation. In this study, we analyzed the yield of clustered base damage (comprising multiple base lesions) in cultured cells irradiated with various heavy ion beams, and investigated isolated base damage and the repair process in post-irradiation cultured cells. Chinese hamster ovary (CHO) cells were irradiated by carbon, silicon, argon and iron ion beams with LETs of 13, 55, 90 and 200 keV µm -1 , respectively. Agarose gel electrophoresis of the cells with enzymatic treatments indicated that clustered base damage yields decreased as the LET increased. The aldehyde reactive probe procedure showed that isolated base damage yields in the irradiated cells followed the same pattern. To analyze the cellular base damage process, clustered DNA damage repair was investigated using DNA repair mutant cells. DNA double-strand breaks accumulated in CHO mutant cells lacking Xrcc1 after irradiation, and the cell viability decreased. On the other hand, mouse embryonic fibroblast (Mef) cells lacking both Nth1 and Ogg1 became more resistant than the wild type Mef. Thus, clustered base damage seems to be involved in the expression of heavy ion beam biological effects via the repair process. (author)

The Effect of Cluster-Based Instruction on Mathematic Achievement in Inclusive Schools

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Gunarhadi, Sunardi; Anwar, Mohammad; Andayani, Tri Rejeki; Shaari, Abdull Sukor

2016-01-01

The research aimed to investigate the effect of Cluster-Based Instruction (CBI) on the academic achievement of Mathematics in inclusive schools. The sample was 68 students in two intact classes, including those with learning disabilities, selected using a cluster random technique among 17 inclusive schools in the regency of Surakarta. The two…

CINtec PLUS immunocytochemistry as a tool for the cytologic diagnosis of glandular lesions of the cervix uteri.

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Ravarino, Alberto; Nemolato, Sonia; Macciocu, Elena; Fraschini, Matteo; Senes, Giancarlo; Faa, Gavino; Negri, Giovanni

2012-11-01

Cytologic findings of glandular lesions of the cervix uteri are often difficult to evaluate. We studied the usefulness of CINtec PLUS p16/Ki-67 double stain (mtm laboratories, Heidelberg, Germany) for the diagnosis of glandular lesions. The study included 47 abnormal results on liquid-based cytologic tests with a subsequent histologic diagnosis of adenocarcinoma in situ or with early invasion, and 16 samples with negative results on follow-up. All samples were stained with CINtec PLUS p16/Ki-67 double stain. Of the neoplastic samples, 7 were excluded because of insufficient residual cellularity or loss of neoplastic cells. Of the samples that were adequate, 92.5% were stained with CINtec PLUS, whereas 7.5% were judged inconclusive. All inconclusive cases were at least 3 years old. Of the 16 negative samples, 15 (93.8%) stained negative and only 1 (6.2%) showed several positive clusters of cells. Our study shows that CINtec PLUS is a robust and useful tool for the diagnosis of glandular lesions of the cervix uteri.

Core Business Selection Based on Ant Colony Clustering Algorithm

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Yu Lan

2014-01-01

Full Text Available Core business is the most important business to the enterprise in diversified business. In this paper, we first introduce the definition and characteristics of the core business and then descript the ant colony clustering algorithm. In order to test the effectiveness of the proposed method, Tianjin Port Logistics Development Co., Ltd. is selected as the research object. Based on the current situation of the development of the company, the core business of the company can be acquired by ant colony clustering algorithm. Thus, the results indicate that the proposed method is an effective way to determine the core business for company.

GENERALISED MODEL BASED CONFIDENCE INTERVALS IN TWO STAGE CLUSTER SAMPLING

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Christopher Ouma Onyango

2010-09-01

Full Text Available Chambers and Dorfman (2002 constructed bootstrap confidence intervals in model based estimation for finite population totals assuming that auxiliary values are available throughout a target population and that the auxiliary values are independent. They also assumed that the cluster sizes are known throughout the target population. We now extend to two stage sampling in which the cluster sizes are known only for the sampled clusters, and we therefore predict the unobserved part of the population total. Jan and Elinor (2008 have done similar work, but unlike them, we use a general model, in which the auxiliary values are not necessarily independent. We demonstrate that the asymptotic properties of our proposed estimator and its coverage rates are better than those constructed under the model assisted local polynomial regression model.

Automatic insertion of simulated microcalcification clusters in a software breast phantom

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Shankla, Varsha; Pokrajac, David D.; Weinstein, Susan P.; DeLeo, Michael; Tuite, Catherine; Roth, Robyn; Conant, Emily F.; Maidment, Andrew D.; Bakic, Predrag R.

2014-03-01

An automated method has been developed to insert realistic clusters of simulated microcalcifications (MCs) into computer models of breast anatomy. This algorithm has been developed as part of a virtual clinical trial (VCT) software pipeline, which includes the simulation of breast anatomy, mechanical compression, image acquisition, image processing, display and interpretation. An automated insertion method has value in VCTs involving large numbers of images. The insertion method was designed to support various insertion placement strategies, governed by probability distribution functions (pdf). The pdf can be predicated on histological or biological models of tumor growth, or estimated from the locations of actual calcification clusters. To validate the automated insertion method, a 2-AFC observer study was designed to compare two placement strategies, undirected and directed. The undirected strategy could place a MC cluster anywhere within the phantom volume. The directed strategy placed MC clusters within fibroglandular tissue on the assumption that calcifications originate from epithelial breast tissue. Three radiologists were asked to select between two simulated phantom images, one from each placement strategy. Furthermore, questions were posed to probe the rationale behind the observer's selection. The radiologists found the resulting cluster placement to be realistic in 92% of cases, validating the automated insertion method. There was a significant preference for the cluster to be positioned on a background of adipose or mixed adipose/fibroglandular tissues. Based upon these results, this automated lesion placement method will be included in our VCT simulation pipeline.

Multi-documents summarization based on clustering of learning object using hierarchical clustering

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Mustamiin, M.; Budi, I.; Santoso, H. B.

2018-03-01

The Open Educational Resources (OER) is a portal of teaching, learning and research resources that is available in public domain and freely accessible. Learning contents or Learning Objects (LO) are granular and can be reused for constructing new learning materials. LO ontology-based searching techniques can be used to search for LO in the Indonesia OER. In this research, LO from search results are used as an ingredient to create new learning materials according to the topic searched by users. Summarizing-based grouping of LO use Hierarchical Agglomerative Clustering (HAC) with the dependency context to the user’s query which has an average value F-Measure of 0.487, while summarizing by K-Means F-Measure only has an average value of 0.336.

Progressive Amalgamation of Building Clusters for Map Generalization Based on Scaling Subgroups

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Xianjin He

2018-03-01

Full Text Available Map generalization utilizes transformation operations to derive smaller-scale maps from larger-scale maps, and is a key procedure for the modelling and understanding of geographic space. Studies to date have largely applied a fixed tolerance to aggregate clustered buildings into a single object, resulting in the loss of details that meet cartographic constraints and may be of importance for users. This study aims to develop a method that amalgamates clustered buildings gradually without significant modification of geometry, while preserving the map details as much as possible under cartographic constraints. The amalgamation process consists of three key steps. First, individual buildings are grouped into distinct clusters by using the graph-based spatial clustering application with random forest (GSCARF method. Second, building clusters are decomposed into scaling subgroups according to homogeneity with regard to the mean distance of subgroups. Thus, hierarchies of building clusters can be derived based on scaling subgroups. Finally, an amalgamation operation is progressively performed from the bottom-level subgroups to the top-level subgroups using the maximum distance of each subgroup as the amalgamating tolerance instead of using a fixed tolerance. As a consequence of this step, generalized intermediate scaling results are available, which can form the multi-scale representation of buildings. The experimental results show that the proposed method can generate amalgams with correct details, statistical area balance and orthogonal shape while satisfying cartographic constraints (e.g., minimum distance and minimum area.

A Model-Based Cluster Analysis of Maternal Emotion Regulation and Relations to Parenting Behavior.

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Shaffer, Anne; Whitehead, Monica; Davis, Molly; Morelen, Diana; Suveg, Cynthia

2017-10-15

In a diverse community sample of mothers (N = 108) and their preschool-aged children (M age  = 3.50 years), this study conducted person-oriented analyses of maternal emotion regulation (ER) based on a multimethod assessment incorporating physiological, observational, and self-report indicators. A model-based cluster analysis was applied to five indicators of maternal ER: maternal self-report, observed negative affect in a parent-child interaction, baseline respiratory sinus arrhythmia (RSA), and RSA suppression across two laboratory tasks. Model-based cluster analyses revealed four maternal ER profiles, including a group of mothers with average ER functioning, characterized by socioeconomic advantage and more positive parenting behavior. A dysregulated cluster demonstrated the greatest challenges with parenting and dyadic interactions. Two clusters of intermediate dysregulation were also identified. Implications for assessment and applications to parenting interventions are discussed. © 2017 Family Process Institute.

A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation.

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Rosemary M McCloskey

2017-11-01

Full Text Available Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis-where individuals are sampled sooner post-infection-rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP, which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85% and specificity (91% than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46% as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where

[Arthroscopic treatment of chondral lesions of the ankle joint. Evidence-based therapy].

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Thomas, M; Jordan, M; Hamborg-Petersen, E

2016-02-01

Ankle sprains are the most relevant injuries of the lower extremities and can lead to damage to ligaments and osteochondral lesions. Up to 50 % of patients with a sprained ankle later develop a lesion of the cartilage in the ankle joint or an osteochondral lesion of the talus. This can lead to osteoarthritis of the injured ankle joint. Spontaneous healing is possible in all age groups in cases of a bone bruise in the subchondral bone but in isolated chondral injuries is only useful in pediatric patients. In many cases chondral and osteochondral injuries lead to increasing demarcation of the affected area and can result in progressive degeneration of the joint if not recognized in time. There also exist a certain number of osteochondral changes of the articular surface of the talus without any history of relevant trauma, which are collectively grouped under the term osteochondrosis dissecans. Perfusion disorders are discussed as one of many possible causes of these alterations. Nowadays, chondral and osteochondral defects can be treated earlier due to detection using very sensitive magnetic resonance imaging (MRI) and computed tomography (CT) techniques. The use of conservative treatment only has a chance of healing in pediatric patients. Conservative measures for adults should only be considered as adjuvant treatment to surgery.Based on a comprehensive analysis of the current literature, this article gives an overview and critical analysis of the current concepts for treatment of chondral and osteochondral injuries and lesions of the talus. With arthroscopic therapy curettage and microfracture of talar lesions are the predominant approaches or retrograde drilling of the defect is another option when the chondral coating is retained. Implantation of autologous chondral cells or homologous juvenile cartilage tissue is also possible with arthroscopic techniques. Osteochondral fractures (flake fracture) are usually performed as a mini-open procedure supported by

Evidence of end-effector based gait machines in gait rehabilitation after CNS lesion.

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Hesse, S; Schattat, N; Mehrholz, J; Werner, C

2013-01-01

A task-specific repetitive approach in gait rehabilitation after CNS lesion is well accepted nowadays. To ease the therapists' and patients' physical effort, the past two decades have seen the introduction of gait machines to intensify the amount of gait practice. Two principles have emerged, an exoskeleton- and an endeffector-based approach. Both systems share the harness and the body weight support. With the end-effector-based devices, the patients' feet are positioned on two foot plates, whose movements simulate stance and swing phase. This article provides an overview on the end-effector based machine's effectiveness regarding the restoration of gait. For the electromechanical gait trainer GT I, a meta analysis identified nine controlled trials (RCT) in stroke subjects (n = 568) and were analyzed to detect differences between end-effector-based locomotion + physiotherapy and physiotherapy alone. Patients practising with the machine effected in a superior gait ability (210 out of 319 patients, 65.8% vs. 96 out of 249 patients, 38.6%, respectively, Z = 2.29, p = 0.020), due to a larger training intensity. Only single RCTs have been reported for other devices and etiologies. The introduction of end-effector based gait machines has opened a new succesful chapter in gait rehabilitation after CNS lesion.

Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data

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Arvind Sharma

2016-01-01

Full Text Available There are many techniques available in the field of data mining and its subfield spatial data mining is to understand relationships between data objects. Data objects related with spatial features are called spatial databases. These relationships can be used for prediction and trend detection between spatial and nonspatial objects for social and scientific reasons. A huge data set may be collected from different sources as satellite images, X-rays, medical images, traffic cameras, and GIS system. To handle this large amount of data and set relationship between them in a certain manner with certain results is our primary purpose of this paper. This paper gives a complete process to understand how spatial data is different from other kinds of data sets and how it is refined to apply to get useful results and set trends to predict geographic information system and spatial data mining process. In this paper a new improved algorithm for clustering is designed because role of clustering is very indispensable in spatial data mining process. Clustering methods are useful in various fields of human life such as GIS (Geographic Information System, GPS (Global Positioning System, weather forecasting, air traffic controller, water treatment, area selection, cost estimation, planning of rural and urban areas, remote sensing, and VLSI designing. This paper presents study of various clustering methods and algorithms and an improved algorithm of DBSCAN as IDBSCAN (Improved Density Based Spatial Clustering of Application of Noise. The algorithm is designed by addition of some important attributes which are responsible for generation of better clusters from existing data sets in comparison of other methods.

Energy Aware Cluster Based Routing Scheme For Wireless Sensor Network

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Roy Sohini

2015-09-01

Full Text Available Wireless Sensor Network (WSN has emerged as an important supplement to the modern wireless communication systems due to its wide range of applications. The recent researches are facing the various challenges of the sensor network more gracefully. However, energy efficiency has still remained a matter of concern for the researches. Meeting the countless security needs, timely data delivery and taking a quick action, efficient route selection and multi-path routing etc. can only be achieved at the cost of energy. Hierarchical routing is more useful in this regard. The proposed algorithm Energy Aware Cluster Based Routing Scheme (EACBRS aims at conserving energy with the help of hierarchical routing by calculating the optimum number of cluster heads for the network, selecting energy-efficient route to the sink and by offering congestion control. Simulation results prove that EACBRS performs better than existing hierarchical routing algorithms like Distributed Energy-Efficient Clustering (DEEC algorithm for heterogeneous wireless sensor networks and Energy Efficient Heterogeneous Clustered scheme for Wireless Sensor Network (EEHC.

Characterization of oral precancerous lesions based on higher-harmonic generation microscopy

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Lin, Chen-Yu; Lin, Chih-Feng; Sun, Chi-Kuang

2013-03-01

It is generally accepted that oral cancer arises in the presence of oral precancerous lesions. However, the clinical courses of these lesions are quite unpredictable, and a fundamental enigma remains that when and how these lesions turn to malignant growth. Characterization of these potentially malignant lesions is thus important and could serve as early indicators of this neoplastic transformation process, potentially facilitates the treatment outcome and improves the survival rate. Higher harmonic generation microscope (HGM), providing images with a leaving photodamages in the tissues, was used for this purpose. Oral cavity biopsies were obtained from 18 patients with clinical suspected oral precancerous lesions scheduled for surgical biopsy. HGM images were compared with histological images to determine the results. By visualization of subtle cellular and morphological changes, the preliminary result of this HGM image discloses excellent consistency with traditional histolopathology studies, without the need for fixation, sectioning and staining. More specifically speaking, the keratin thickness was found to be increased comparing with normal adjacent controls. In some cases, variations in cell size, nuclear size and increased nuclear/cytoplasmic ratio, and increased size of nucleoli were identified, indicating different stages of malignant transformation. These results together indicated that HGM provides the capability to characterize features of oral precancerous lesions as well as oral cancer progression, and holds the greatest potential as an ideal tool for clinical screening and surveillance of suspicious oral lesions.

Hessian regularization based non-negative matrix factorization for gene expression data clustering.

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Liu, Xiao; Shi, Jun; Wang, Congzhi

2015-01-01

Since a key step in the analysis of gene expression data is to detect groups of genes that have similar expression patterns, clustering technique is then commonly used to analyze gene expression data. Data representation plays an important role in clustering analysis. The non-negative matrix factorization (NMF) is a widely used data representation method with great success in machine learning. Although the traditional manifold regularization method, Laplacian regularization (LR), can improve the performance of NMF, LR still suffers from the problem of its weak extrapolating power. Hessian regularization (HR) is a newly developed manifold regularization method, whose natural properties make it more extrapolating, especially for small sample data. In this work, we propose the HR-based NMF (HR-NMF) algorithm, and then apply it to represent gene expression data for further clustering task. The clustering experiments are conducted on five commonly used gene datasets, and the results indicate that the proposed HR-NMF outperforms LR-based NMM and original NMF, which suggests the potential application of HR-NMF for gene expression data.

WebGimm: An integrated web-based platform for cluster analysis, functional analysis, and interactive visualization of results.

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Joshi, Vineet K; Freudenberg, Johannes M; Hu, Zhen; Medvedovic, Mario

2011-01-17

Cluster analysis methods have been extensively researched, but the adoption of new methods is often hindered by technical barriers in their implementation and use. WebGimm is a free cluster analysis web-service, and an open source general purpose clustering web-server infrastructure designed to facilitate easy deployment of integrated cluster analysis servers based on clustering and functional annotation algorithms implemented in R. Integrated functional analyses and interactive browsing of both, clustering structure and functional annotations provides a complete analytical environment for cluster analysis and interpretation of results. The Java Web Start client-based interface is modeled after the familiar cluster/treeview packages making its use intuitive to a wide array of biomedical researchers. For biomedical researchers, WebGimm provides an avenue to access state of the art clustering procedures. For Bioinformatics methods developers, WebGimm offers a convenient avenue to deploy their newly developed clustering methods. WebGimm server, software and manuals can be freely accessed at http://ClusterAnalysis.org/.

A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm.

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Ju, Chunhua; Xu, Chonghuan

2013-01-01

Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm

Directory of Open Access Journals (Sweden)

Chunhua Ju

2013-01-01

Full Text Available Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users’ preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

Insight into acid-base nucleation experiments by comparison of the chemical composition of positive, negative, and neutral clusters.

Science.gov (United States)

Bianchi, Federico; Praplan, Arnaud P; Sarnela, Nina; Dommen, Josef; Kürten, Andreas; Ortega, Ismael K; Schobesberger, Siegfried; Junninen, Heikki; Simon, Mario; Tröstl, Jasmin; Jokinen, Tuija; Sipilä, Mikko; Adamov, Alexey; Amorim, Antonio; Almeida, Joao; Breitenlechner, Martin; Duplissy, Jonathan; Ehrhart, Sebastian; Flagan, Richard C; Franchin, Alessandro; Hakala, Jani; Hansel, Armin; Heinritzi, Martin; Kangasluoma, Juha; Keskinen, Helmi; Kim, Jaeseok; Kirkby, Jasper; Laaksonen, Ari; Lawler, Michael J; Lehtipalo, Katrianne; Leiminger, Markus; Makhmutov, Vladimir; Mathot, Serge; Onnela, Antti; Petäjä, Tuukka; Riccobono, Francesco; Rissanen, Matti P; Rondo, Linda; Tomé, António; Virtanen, Annele; Viisanen, Yrjö; Williamson, Christina; Wimmer, Daniela; Winkler, Paul M; Ye, Penglin; Curtius, Joachim; Kulmala, Markku; Worsnop, Douglas R; Donahue, Neil M; Baltensperger, Urs

2014-12-02

We investigated the nucleation of sulfuric acid together with two bases (ammonia and dimethylamine), at the CLOUD chamber at CERN. The chemical composition of positive, negative, and neutral clusters was studied using three Atmospheric Pressure interface-Time Of Flight (APi-TOF) mass spectrometers: two were operated in positive and negative mode to detect the chamber ions, while the third was equipped with a nitrate ion chemical ionization source allowing detection of neutral clusters. Taking into account the possible fragmentation that can happen during the charging of the ions or within the first stage of the mass spectrometer, the cluster formation proceeded via essentially one-to-one acid-base addition for all of the clusters, independent of the type of the base. For the positive clusters, the charge is carried by one excess protonated base, while for the negative clusters it is carried by a deprotonated acid; the same is true for the neutral clusters after these have been ionized. During the experiments involving sulfuric acid and dimethylamine, it was possible to study the appearance time for all the clusters (positive, negative, and neutral). It appeared that, after the formation of the clusters containing three molecules of sulfuric acid, the clusters grow at a similar speed, independent of their charge. The growth rate is then probably limited by the arrival rate of sulfuric acid or cluster-cluster collision.

GTV-based prescription in SBRT for lung lesions using advanced dose calculation algorithms

International Nuclear Information System (INIS)

Lacornerie, Thomas; Lisbona, Albert; Mirabel, Xavier; Lartigau, Eric; Reynaert, Nick

2014-01-01

The aim of current study was to investigate the way dose is prescribed to lung lesions during SBRT using advanced dose calculation algorithms that take into account electron transport (type B algorithms). As type A algorithms do not take into account secondary electron transport, they overestimate the dose to lung lesions. Type B algorithms are more accurate but still no consensus is reached regarding dose prescription. The positive clinical results obtained using type A algorithms should be used as a starting point. In current work a dose-calculation experiment is performed, presenting different prescription methods. Three cases with three different sizes of peripheral lung lesions were planned using three different treatment platforms. For each individual case 60 Gy to the PTV was prescribed using a type A algorithm and the dose distribution was recalculated using a type B algorithm in order to evaluate the impact of the secondary electron transport. Secondly, for each case a type B algorithm was used to prescribe 48 Gy to the PTV, and the resulting doses to the GTV were analyzed. Finally, prescriptions based on specific GTV dose volumes were evaluated. When using a type A algorithm to prescribe the same dose to the PTV, the differences regarding median GTV doses among platforms and cases were always less than 10% of the prescription dose. The prescription to the PTV based on type B algorithms, leads to a more important variability of the median GTV dose among cases and among platforms, (respectively 24%, and 28%). However, when 54 Gy was prescribed as median GTV dose, using a type B algorithm, the variability observed was minimal. Normalizing the prescription dose to the median GTV dose for lung lesions avoids variability among different cases and treatment platforms of SBRT when type B algorithms are used to calculate the dose. The combination of using a type A algorithm to optimize a homogeneous dose in the PTV and using a type B algorithm to prescribe the

Cluster fusion algorithm: application to Lennard-Jones clusters

DEFF Research Database (Denmark)

Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

2006-01-01

paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...

Cluster fusion algorithm: application to Lennard-Jones clusters

DEFF Research Database (Denmark)

Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

2008-01-01

paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...

Design and implementation of streaming media server cluster based on FFMpeg.

Science.gov (United States)

Zhao, Hong; Zhou, Chun-long; Jin, Bao-zhao

2015-01-01

Poor performance and network congestion are commonly observed in the streaming media single server system. This paper proposes a scheme to construct a streaming media server cluster system based on FFMpeg. In this scheme, different users are distributed to different servers according to their locations and the balance among servers is maintained by the dynamic load-balancing algorithm based on active feedback. Furthermore, a service redirection algorithm is proposed to improve the transmission efficiency of streaming media data. The experiment results show that the server cluster system has significantly alleviated the network congestion and improved the performance in comparison with the single server system.

Design and Implementation of Streaming Media Server Cluster Based on FFMpeg

Science.gov (United States)

Zhao, Hong; Zhou, Chun-long; Jin, Bao-zhao

2015-01-01

Poor performance and network congestion are commonly observed in the streaming media single server system. This paper proposes a scheme to construct a streaming media server cluster system based on FFMpeg. In this scheme, different users are distributed to different servers according to their locations and the balance among servers is maintained by the dynamic load-balancing algorithm based on active feedback. Furthermore, a service redirection algorithm is proposed to improve the transmission efficiency of streaming media data. The experiment results show that the server cluster system has significantly alleviated the network congestion and improved the performance in comparison with the single server system. PMID:25734187

Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging.

Science.gov (United States)

Agner, Shannon C; Xu, Jun; Madabhushi, Anant

2013-03-01

Segmentation of breast lesions on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is the first step in lesion diagnosis in a computer-aided diagnosis framework. Because manual segmentation of such lesions is both time consuming and highly susceptible to human error and issues of reproducibility, an automated lesion segmentation method is highly desirable. Traditional automated image segmentation methods such as boundary-based active contour (AC) models require a strong gradient at the lesion boundary. Even when region-based terms are introduced to an AC model, grayscale image intensities often do not allow for clear definition of foreground and background region statistics. Thus, there is a need to find alternative image representations that might provide (1) strong gradients at the margin of the object of interest (OOI); and (2) larger separation between intensity distributions and region statistics for the foreground and background, which are necessary to halt evolution of the AC model upon reaching the border of the OOI. In this paper, the authors introduce a spectral embedding (SE) based AC (SEAC) for lesion segmentation on breast DCE-MRI. SE, a nonlinear dimensionality reduction scheme, is applied to the DCE time series in a voxelwise fashion to reduce several time point images to a single parametric image where every voxel is characterized by the three dominant eigenvectors. This parametric eigenvector image (PrEIm) representation allows for better capture of image region statistics and stronger gradients for use with a hybrid AC model, which is driven by both boundary and region information. They compare SEAC to ACs that employ fuzzy c-means (FCM) and principal component analysis (PCA) as alternative image representations. Segmentation performance was evaluated by boundary and region metrics as well as comparing lesion classification using morphological features from SEAC, PCA+AC, and FCM+AC. On a cohort of 50 breast DCE-MRI studies, Pr

DIRECTIONAL OPPORTUNISTIC MECHANISM IN CLUSTER MESSAGE CRITICALITY LEVEL BASED ZIGBEE ROUTING

OpenAIRE

B.Rajeshkanna *1, Dr.M.Anitha 2

2018-01-01

The cluster message criticality level based zigbee routing(CMCLZOR) has been proposed for routing the cluster messages in wireless smart energy home area networks. It employs zigbee opportunistic shortcut tree routing(ZOSTR) and AODV individually for routing normal messages and highly critical messages respectively. ZOSTR allows the receiving nodes to compete for forwarding a packet with the priority of left-over hops rather than stating single next hop node like unicast protocols. Since it h...

Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis

Directory of Open Access Journals (Sweden)

Sen Zhang

2015-01-01

Full Text Available One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO, inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.

Weighted similarity-based clustering of chemical structures and bioactivity data in early drug discovery.

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Perualila-Tan, Nolen Joy; Shkedy, Ziv; Talloen, Willem; Göhlmann, Hinrich W H; Moerbeke, Marijke Van; Kasim, Adetayo

2016-08-01

The modern process of discovering candidate molecules in early drug discovery phase includes a wide range of approaches to extract vital information from the intersection of biology and chemistry. A typical strategy in compound selection involves compound clustering based on chemical similarity to obtain representative chemically diverse compounds (not incorporating potency information). In this paper, we propose an integrative clustering approach that makes use of both biological (compound efficacy) and chemical (structural features) data sources for the purpose of discovering a subset of compounds with aligned structural and biological properties. The datasets are integrated at the similarity level by assigning complementary weights to produce a weighted similarity matrix, serving as a generic input in any clustering algorithm. This new analysis work flow is semi-supervised method since, after the determination of clusters, a secondary analysis is performed wherein it finds differentially expressed genes associated to the derived integrated cluster(s) to further explain the compound-induced biological effects inside the cell. In this paper, datasets from two drug development oncology projects are used to illustrate the usefulness of the weighted similarity-based clustering approach to integrate multi-source high-dimensional information to aid drug discovery. Compounds that are structurally and biologically similar to the reference compounds are discovered using this proposed integrative approach.

Conveyor Performance based on Motor DC 12 Volt Eg-530ad-2f using K-Means Clustering

Science.gov (United States)

Arifin, Zaenal; Artini, Sri DP; Much Ibnu Subroto, Imam

2017-04-01

To produce goods in industry, a controlled tool to improve production is required. Separation process has become a part of production process. Separation process is carried out based on certain criteria to get optimum result. By knowing the characteristics performance of a controlled tools in separation process the optimum results is also possible to be obtained. Clustering analysis is popular method for clustering data into smaller segments. Clustering analysis is useful to divide a group of object into a k-group in which the member value of the group is homogeny or similar. Similarity in the group is set based on certain criteria. The work in this paper based on K-Means method to conduct clustering of loading in the performance of a conveyor driven by a dc motor 12 volt eg-530-2f. This technique gives a complete clustering data for a prototype of conveyor driven by dc motor to separate goods in term of height. The parameters involved are voltage, current, time of travelling. These parameters give two clusters namely optimal cluster with center of cluster 10.50 volt, 0.3 Ampere, 10.58 second, and unoptimal cluster with center of cluster 10.88 volt, 0.28 Ampere and 40.43 second.

The simulation of 3D microcalcification clusters in 2D digital mammography and breast tomosynthesis

International Nuclear Information System (INIS)

Shaheen, Eman; Van Ongeval, Chantal; Zanca, Federica; Cockmartin, Lesley; Marshall, Nicholas; Jacobs, Jurgen; Young, Kenneth C.; Dance, David R.; Bosmans, Hilde

2011-01-01

Purpose: This work proposes a new method of building 3D models of microcalcification clusters and describes the validation of their realistic appearance when simulated into 2D digital mammograms and into breast tomosynthesis images. Methods: A micro-CT unit was used to scan 23 breast biopsy specimens of microcalcification clusters with malignant and benign characteristics and their 3D reconstructed datasets were segmented to obtain 3D models of microcalcification clusters. These models were then adjusted for the x-ray spectrum used and for the system resolution and simulated into 2D projection images to obtain mammograms after image processing and into tomographic sequences of projection images, which were then reconstructed to form 3D tomosynthesis datasets. Six radiologists were asked to distinguish between 40 real and 40 simulated clusters of microcalcifications in two separate studies on 2D mammography and tomosynthesis datasets. Receiver operating characteristic (ROC) analysis was used to test the ability of each observer to distinguish between simulated and real microcalcification clusters. The kappa statistic was applied to assess how often the individual simulated and real microcalcification clusters had received similar scores (''agreement'') on their realistic appearance in both modalities. This analysis was performed for all readers and for the real and the simulated group of microcalcification clusters separately. ''Poor'' agreement would reflect radiologists' confusion between simulated and real clusters, i.e., lesions not systematically evaluated in both modalities as either simulated or real, and would therefore be interpreted as a success of the present models. Results: The area under the ROC curve, averaged over the observers, was 0.55 (95% confidence interval [0.44, 0.66]) for the 2D study, and 0.46 (95% confidence interval [0.29, 0.64]) for the tomosynthesis study, indicating no statistically significant difference between real and simulated

Hierarchical clustering of HPV genotype patterns in the ASCUS-LSIL triage study

Science.gov (United States)

Wentzensen, Nicolas; Wilson, Lauren E.; Wheeler, Cosette M.; Carreon, Joseph D.; Gravitt, Patti E.; Schiffman, Mark; Castle, Philip E.

2010-01-01

Anogenital cancers are associated with about 13 carcinogenic HPV types in a broader group that cause cervical intraepithelial neoplasia (CIN). Multiple concurrent cervical HPV infections are common which complicate the attribution of HPV types to different grades of CIN. Here we report the analysis of HPV genotype patterns in the ASCUS-LSIL triage study using unsupervised hierarchical clustering. Women who underwent colposcopy at baseline (n = 2780) were grouped into 20 disease categories based on histology and cytology. Disease groups and HPV genotypes were clustered using complete linkage. Risk of 2-year cumulative CIN3+, viral load, colposcopic impression, and age were compared between disease groups and major clusters. Hierarchical clustering yielded four major disease clusters: Cluster 1 included all CIN3 histology with abnormal cytology; Cluster 2 included CIN3 histology with normal cytology and combinations with either CIN2 or high-grade squamous intraepithelial lesion (HSIL) cytology; Cluster 3 included older women with normal or low grade histology/cytology and low viral load; Cluster 4 included younger women with low grade histology/cytology, multiple infections, and the highest viral load. Three major groups of HPV genotypes were identified: Group 1 included only HPV16; Group 2 included nine carcinogenic types plus non-carcinogenic HPV53 and HPV66; and Group 3 included non-carcinogenic types plus carcinogenic HPV33 and HPV45. Clustering results suggested that colposcopy missed a prevalent precancer in many women with no biopsy/normal histology and HSIL. This result was confirmed by an elevated 2-year risk of CIN3+ in these groups. Our novel approach to study multiple genotype infections in cervical disease using unsupervised hierarchical clustering can address complex genotype distributions on a population level. PMID:20959485

Radiofrequency tissue ablation with cooled-tip electrodes:an experimental study in a bovine liver model on variables influencing lesion size

International Nuclear Information System (INIS)

Han, Hyun Young; Lee, Jeong Min; Kim, Chong Soo

2001-01-01

The purpose of this study was to determine the influence of various factors on the extent of thermal coagulation necrosis after radiofrequency (RF) tissue ablation using a cooled-tip electrode in bovine liver. RF ablation was induced by a monopolar 500 KHz-RF generator (CC-1; Radionics, Burlington, Mass., U.S.A.) and an 18-G cooled-tip with single or clustered electrodes. The ablation protocol involved a combination of varying current, ablation time, power output, gradual or abrupt increase of this out-put, and pulsed radiofrequency techniques. The maximum diameter of all thermal lesions which showed a color change was measured perpendicular to the electrode axis by two observers who reached their decisions by consensus. Twenty representative lesions were pathologically examined. With increasing current lesion diameter also increased, but above 1500 mA no further increase was induced. Extending the ablation time to 9 minutes for a single electrode and 15 minutes for a clustered electrode increased lesion diameter until a steady state was reached. Higher power levels caused larger lesions, but above 100 W no increase was observed. Ample exposure time coupled with a stepwise increase in power level induced a lesion larger than that resulting from an abrupt increase. Continuous pulsed RF with a high current led to increased coagulation necrosis diameter. These experimental findings may be useful thermotherapy. The data suggest that all involved factors significantly affect lesion size:if the factors are better understood, cancer thermotherapy can be better controlled

A nonparametric Bayesian approach for clustering bisulfate-based DNA methylation profiles.

Science.gov (United States)

Zhang, Lin; Meng, Jia; Liu, Hui; Huang, Yufei

2012-01-01

DNA methylation occurs in the context of a CpG dinucleotide. It is an important epigenetic modification, which can be inherited through cell division. The two major types of methylation include hypomethylation and hypermethylation. Unique methylation patterns have been shown to exist in diseases including various types of cancer. DNA methylation analysis promises to become a powerful tool in cancer diagnosis, treatment and prognostication. Large-scale methylation arrays are now available for studying methylation genome-wide. The Illumina methylation platform simultaneously measures cytosine methylation at more than 1500 CpG sites associated with over 800 cancer-related genes. Cluster analysis is often used to identify DNA methylation subgroups for prognosis and diagnosis. However, due to the unique non-Gaussian characteristics, traditional clustering methods may not be appropriate for DNA and methylation data, and the determination of optimal cluster number is still problematic. A Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation expressions as an infinite number of beta mixture distribution. The model allows automatic learning of the relevant parameters such as the cluster mixing proportion, the parameters of beta distribution for each cluster, and especially the number of potential clusters. Since the model is high dimensional and analytically intractable, we proposed a Gibbs sampling "no-gaps" solution for computing the posterior distributions, hence the estimates of the parameters. The proposed algorithm was tested on simulated data as well as methylation data from 55 Glioblastoma multiform (GBM) brain tissue samples. To reduce the computational burden due to the high data dimensionality, a dimension reduction method is adopted. The two GBM clusters yielded by DPBMM are based on data of different number of loci (P-value < 0.1), while hierarchical clustering cannot yield statistically significant clusters.

Cluster Matters

DEFF Research Database (Denmark)

Gulati, Mukesh; Lund-Thomsen, Peter; Suresh, Sangeetha

2018-01-01

sell their products successfully in international markets, but there is also an increasingly large consumer base within India. Indeed, Indian industrial clusters have contributed to a substantial part of this growth process, and there are several hundred registered clusters within the country...... of this handbook, which focuses on the role of CSR in MSMEs. Hence we contribute to the literature on CSR in industrial clusters and specifically CSR in Indian industrial clusters by investigating the drivers of CSR in India’s industrial clusters....

3.5D dynamic PET image reconstruction incorporating kinetics-based clusters

International Nuclear Information System (INIS)

Lu Lijun; Chen Wufan; Karakatsanis, Nicolas A; Rahmim, Arman; Tang Jing

2012-01-01

Standard 3D dynamic positron emission tomographic (PET) imaging consists of independent image reconstructions of individual frames followed by application of appropriate kinetic model to the time activity curves at the voxel or region-of-interest (ROI). The emerging field of 4D PET reconstruction, by contrast, seeks to move beyond this scheme and incorporate information from multiple frames within the image reconstruction task. Here we propose a novel reconstruction framework aiming to enhance quantitative accuracy of parametric images via introduction of priors based on voxel kinetics, as generated via clustering of preliminary reconstructed dynamic images to define clustered neighborhoods of voxels with similar kinetics. This is then followed by straightforward maximum a posteriori (MAP) 3D PET reconstruction as applied to individual frames; and as such the method is labeled ‘3.5D’ image reconstruction. The use of cluster-based priors has the advantage of further enhancing quantitative performance in dynamic PET imaging, because: (a) there are typically more voxels in clusters than in conventional local neighborhoods, and (b) neighboring voxels with distinct kinetics are less likely to be clustered together. Using realistic simulated 11 C-raclopride dynamic PET data, the quantitative performance of the proposed method was investigated. Parametric distribution-volume (DV) and DV ratio (DVR) images were estimated from dynamic image reconstructions using (a) maximum-likelihood expectation maximization (MLEM), and MAP reconstructions using (b) the quadratic prior (QP-MAP), (c) the Green prior (GP-MAP) and (d, e) two proposed cluster-based priors (CP-U-MAP and CP-W-MAP), followed by graphical modeling, and were qualitatively and quantitatively compared for 11 ROIs. Overall, the proposed dynamic PET reconstruction methodology resulted in substantial visual as well as quantitative accuracy improvements (in terms of noise versus bias performance) for parametric DV

Group analyses of connectivity-based cortical parcellation using repeated k-means clustering.

Science.gov (United States)

Nanetti, Luca; Cerliani, Leonardo; Gazzola, Valeria; Renken, Remco; Keysers, Christian

2009-10-01

K-means clustering has become a popular tool for connectivity-based cortical segmentation using Diffusion Weighted Imaging (DWI) data. A sometimes ignored issue is, however, that the output of the algorithm depends on the initial placement of starting points, and that different sets of starting points therefore could lead to different solutions. In this study we explore this issue. We apply k-means clustering a thousand times to the same DWI dataset collected in 10 individuals to segment two brain regions: the SMA-preSMA on the medial wall, and the insula. At the level of single subjects, we found that in both brain regions, repeatedly applying k-means indeed often leads to a variety of rather different cortical based parcellations. By assessing the similarity and frequency of these different solutions, we show that approximately 256 k-means repetitions are needed to accurately estimate the distribution of possible solutions. Using nonparametric group statistics, we then propose a method to employ the variability of clustering solutions to assess the reliability with which certain voxels can be attributed to a particular cluster. In addition, we show that the proportion of voxels that can be attributed significantly to either cluster in the SMA and preSMA is relatively higher than in the insula and discuss how this difference may relate to differences in the anatomy of these regions.

Analytical network process based optimum cluster head selection in wireless sensor network.

Science.gov (United States)

Farman, Haleem; Javed, Huma; Jan, Bilal; Ahmad, Jamil; Ali, Shaukat; Khalil, Falak Naz; Khan, Murad

2017-01-01

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of

Difficulty of MRI based identification of lesion age by acute infra-tentorial ischemic stroke.

Directory of Open Access Journals (Sweden)

Florian Grosse-Dresselhaus

Full Text Available Systemic thrombolysis in acute ischemic stroke is restricted to the 4.5 h time window. Many patients are excluded from this treatment because symptom onset is unknown. Magnetic resonance imaging (MRI studies have shown that stroke patients presenting with acute supra-tentorial diffusion-weighted imaging (DWI lesions that do not have matching lesions on fluid attenuated inversion recovery (FLAIR are likely to be within a 4.5 hour time window. This study examines the DWI-FLAIR mismatch in infra-tentorial stroke.This was a retrospectively conducted substudy of the "1000+" study; a prospective, single-center observational study (http://clinicaltrials.gov; NCT00715533. Fifty-six patients with infra-tentorial stroke confirmed by MRI and known symptom onset who underwent the scan within 24 h after symptom onset were analysed. Two neurologists blinded to clinical information separately rated the DWI lesion visibility on FLAIR. Lesion volume, relative signal intensities of DWI and relative apparent diffusion coefficient values were determined.Regarding baseline characteristics our study population had a median age of 66 years, a median time from symptom onset to MRI of 616.5 minutes, a median NIHSS of 3 and a median DWI lesion volume of 0.26 ml. A negative FLAIR allocated patients to a time window under 4.5 h correctly with a sensitivity of 55% and a specificity of 61%, a positive predictive value of 44% and a negative predictive value of 71%. FLAIR positivity decreased with age (p = 0.018, and showed no significant correlation to lesion volume (p = 0.145.In our study the DWI-FLAIR-Mismatch does not help to reliably identify patients within 4.5 h of symptom onset in acute ischemic infra-tentorial stroke. Thus therapeutical decisions based on the DWI-FLAIR mismatch estimation of time from onset cannot be recommended in patients with infra-tentorial stroke.

Energy Threshold-based Cluster Head Rotation for Routing Protocol in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Hadi Raheem Ali

2018-05-01

Full Text Available Energy efficiency represents a fundamental issue in WSNs, since the network lifetime period entirely depends on the energy of sensor nodes, which are usually battery-operated. In this article, an unequal clustering-based routing protocol has been suggested, where parameters of energy, distance, and density are involved in the cluster head election. Besides, the sizes of clusters are unequal according to distance, energy, and density. Furthermore, the cluster heads are not changed every round unless the residual energy reaches a specific threshold of energy. The outcomes of the conducted simulation confirmed that the performance of the suggested protocol achieves improvement in energy efficiency.

Are clusters of dietary patterns and cluster membership stable over time? Results of a longitudinal cluster analysis study.

Science.gov (United States)

Walthouwer, Michel Jean Louis; Oenema, Anke; Soetens, Katja; Lechner, Lilian; de Vries, Hein

2014-11-01

Developing nutrition education interventions based on clusters of dietary patterns can only be done adequately when it is clear if distinctive clusters of dietary patterns can be derived and reproduced over time, if cluster membership is stable, and if it is predictable which type of people belong to a certain cluster. Hence, this study aimed to: (1) identify clusters of dietary patterns among Dutch adults, (2) test the reproducibility of these clusters and stability of cluster membership over time, and (3) identify sociodemographic predictors of cluster membership and cluster transition. This study had a longitudinal design with online measurements at baseline (N=483) and 6 months follow-up (N=379). Dietary intake was assessed with a validated food frequency questionnaire. A hierarchical cluster analysis was performed, followed by a K-means cluster analysis. Multinomial logistic regression analyses were conducted to identify the sociodemographic predictors of cluster membership and cluster transition. At baseline and follow-up, a comparable three-cluster solution was derived, distinguishing a healthy, moderately healthy, and unhealthy dietary pattern. Male and lower educated participants were significantly more likely to have a less healthy dietary pattern. Further, 251 (66.2%) participants remained in the same cluster, 45 (11.9%) participants changed to an unhealthier cluster, and 83 (21.9%) participants shifted to a healthier cluster. Men and people living alone were significantly more likely to shift toward a less healthy dietary pattern. Distinctive clusters of dietary patterns can be derived. Yet, cluster membership is unstable and only few sociodemographic factors were associated with cluster membership and cluster transition. These findings imply that clusters based on dietary intake may not be suitable as a basis for nutrition education interventions. Copyright © 2014 Elsevier Ltd. All rights reserved.

A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.

Science.gov (United States)

Spanier, A B; Caplan, N; Sosna, J; Acar, B; Joskowicz, L

2018-01-01

The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases. We present a fully automatic end-to-end method for the retrieval of CT scans with similar liver lesion annotations. The input is a database of abdominal CT scans labeled with liver lesions, a query CT scan, and optionally one radiologist-specified lesion annotation of interest. The output is an ordered list of the database CT scans with the most similar liver lesion annotations. The method starts by automatically segmenting the liver in the scan. It then extracts a histogram-based features vector from the segmented region, learns the features' relative importance, and ranks the database scans according to the relative importance measure. The main advantages of our method are that it fully automates the end-to-end querying process, that it uses simple and efficient techniques that are scalable to large datasets, and that it produces quality retrieval results using an unannotated CT scan. Our experimental results on 9 CT queries on a dataset of 41 volumetric CT scans from the 2014 Image CLEF Liver Annotation Task yield an average retrieval accuracy (Normalized Discounted Cumulative Gain index) of 0.77 and 0.84 without/with annotation, respectively. Fully automatic end-to-end retrieval of similar cases based on image information alone, rather that on disease diagnosis, may help radiologists to better diagnose liver lesions.

Diversity among galaxy clusters

International Nuclear Information System (INIS)

Struble, M.F.; Rood, H.J.

1988-01-01

The classification of galaxy clusters is discussed. Consideration is given to the classification scheme of Abell (1950's), Zwicky (1950's), Morgan, Matthews, and Schmidt (1964), and Morgan-Bautz (1970). Galaxies can be classified based on morphology, chemical composition, spatial distribution, and motion. The correlation between a galaxy's environment and morphology is examined. The classification scheme of Rood-Sastry (1971), which is based on clusters's morphology and galaxy population, is described. The six types of clusters they define include: (1) a cD-cluster dominated by a single large galaxy, (2) a cluster dominated by a binary, (3) a core-halo cluster, (4) a cluster dominated by several bright galaxies, (5) a cluster appearing flattened, and (6) an irregularly shaped cluster. Attention is also given to the evolution of cluster structures, which is related to initial density and cluster motion

Generation of connectivity-preserving surface models of multiple sclerosis lesions.

Science.gov (United States)

Meruvia-Pastor, Oscar; Xiao, Mei; Soh, Jung; Sensen, Christoph W

2011-01-01

Progression of multiple sclerosis (MS) results in brain lesions caused by white matter inflammation. MS lesions have various shapes, sizes and locations, affecting cognitive abilities of patients to different extents. To facilitate the visualization of the brain lesion distribution, we have developed a software tool to build 3D surface models of MS lesions. This tool allows users to create 3D models of lesions quickly and to visualize the lesions and brain tissues using various visual attributes and configurations. The software package is based on breadth-first search based 3D connected component analysis and a 3D flood-fill based region growing algorithm to generate 3D models from binary or non-binary segmented medical image stacks.

Resection of highly language-eloquent brain lesions based purely on rTMS language mapping without awake surgery.

Science.gov (United States)

Ille, Sebastian; Sollmann, Nico; Butenschoen, Vicki M; Meyer, Bernhard; Ringel, Florian; Krieg, Sandro M

2016-12-01

The resection of left-sided perisylvian brain lesions harbours the risk of postoperative language impairment. Therefore the individual patient's language distribution is investigated by intraoperative direct cortical stimulation (DCS) during awake surgery. Yet, not all patients qualify for awake surgery. Non-invasive language mapping by repetitive navigated transcranial magnetic stimulation (rTMS) has frequently shown a high correlation in comparison with the results of DCS language mapping in terms of language-negative brain regions. The present study analyses the extent of resection (EOR) and functional outcome of patients who underwent left-sided perisylvian resection of brain lesions based purely on rTMS language mapping. Four patients with left-sided perisylvian brain lesions (two gliomas WHO III, one glioblastoma, one cavernous angioma) underwent rTMS language mapping prior to surgery. Data from rTMS language mapping and rTMS-based diffusion tensor imaging fibre tracking (DTI-FT) were transferred to the intraoperative neuronavigation system. Preoperatively, 5 days after surgery (POD5), and 3 months after surgery (POM3) clinical follow-up examinations were performed. No patient suffered from a new surgery-related aphasia at POM3. Three patients underwent complete resection immediately, while one patient required a second rTMS-based resection some days later to achieve the final, complete resection. The present study shows for the first time the feasibility of successfully resecting language-eloquent brain lesions based purely on the results of negative language maps provided by rTMS language mapping and rTMS-based DTI-FT. In very select cases, this technique can provide a rescue strategy with an optimal functional outcome and EOR when awake surgery is not feasible.

Impact of compressed breast thickness and dose on lesion detectability in digital mammography: FROC study with simulated lesions in real mammograms

Energy Technology Data Exchange (ETDEWEB)

Salvagnini, Elena, E-mail: elena.salvagnini@gmail.com [Department of Imaging and Pathology, Radiology, KUL, Herestraat 49, Leuven B-3000 (Belgium); SCK-CEN, Boeretang 200, Mol 2400 (Belgium); Bosmans, Hilde; Marshall, Nicholas W. [Department of Imaging and Pathology, Radiology, KUL, Herestraat 49, Leuven B-3000 (Belgium); Department of Radiology, Radiology, UZ Gasthuisberg, Herestraat 49, Leuven B-3000 (Belgium); Van Ongeval, Chantal; Van Steen, Andreas; Cockmartin, Lesley [Department of Radiology, Radiology, UZ Gasthuisberg, Herestraat 49, Leuven B-3000 (Belgium); Michielsen, Koen [Department of Imaging and Pathology, Nuclear Medicine and Molecular Imaging, KUL, Herestraat 49, Leuven B-3000 (Belgium); Struelens, Lara [SCK-CEN, Boeretang 200, Mol 2400 (Belgium)

2016-09-15

Purpose: The aim of this work was twofold: (1) to examine whether, with standard automatic exposure control (AEC) settings that maintain pixel values in the detector constant, lesion detectability in clinical images decreases as a function of breast thickness and (2) to verify whether a new AEC setup can increase lesion detectability at larger breast thicknesses. Methods: Screening patient images, acquired on two identical digital mammography systems, were collected over a period of 2 yr. Mammograms were acquired under standard AEC conditions (part 1) and subsequently with a new AEC setup (part 2), programmed to use the standard AEC settings for compressed breast thicknesses ≤49 mm, while a relative dose increase was applied above this thickness. The images were divided into four thickness groups: T1 ≤ 29 mm, T2 = 30–49 mm, T3 = 50–69 mm, and T4 ≥ 70 mm, with each thickness group containing 130 randomly selected craniocaudal lesion-free images. Two measures of density were obtained for every image: a BI-RADS score and a map of volumetric breast density created with a software application (VolparaDensity, Matakina, NZ). This information was used to select subsets of four images, containing one image from each thickness group, matched to a (global) BI-RADS score and containing a region with the same (local) VOLPARA volumetric density value. One selected lesion (a microcalcification cluster or a mass) was simulated into each of the four images. This process was repeated so that, for a given thickness group, half the images contained a single lesion and half were lesion-free. The lesion templates created and inserted in groups T3 and T4 for the first part of the study were then inserted into the images of thickness groups T3 and T4 acquired with higher dose settings. Finally, all images were visualized using the ViewDEX software and scored by four radiologists performing a free search study. A statistical jackknife-alternative free-response receiver operating

Risk Assessment for Bridges Safety Management during Operation Based on Fuzzy Clustering Algorithm

Directory of Open Access Journals (Sweden)

Xia Hanyu

2016-01-01

Full Text Available In recent years, large span and large sea-crossing bridges are built, bridges accidents caused by improper operational management occur frequently. In order to explore the better methods for risk assessment of the bridges operation departments, the method based on fuzzy clustering algorithm is selected. Then, the implementation steps of fuzzy clustering algorithm are described, the risk evaluation system is built, and Taizhou Bridge is selected as an example, the quantitation of risk factors is described. After that, the clustering algorithm based on fuzzy equivalence is calculated on MATLAB 2010a. In the last, Taizhou Bridge operation management departments are classified and sorted according to the degree of risk, and the safety situation of operation departments is analyzed.

Unsupervised segmentation of medical image based on difference of mutual information

Institute of Scientific and Technical Information of China (English)

L(U) Qingwen; CHEN Wufan

2006-01-01

In the scope of medical image processing, segmentation is important and difficult. There are still two problems which trouble us in this field. One is how to determine the number of clusters in an image and the other is how to segment medical images containing lesions. A new segmentation method called DDC, based on difference of mutual information (dMI) and pixon, is proposed in this paper. Experiments demonstrate that dMI shows one kind of intrinsic relationship between the segmented image and the original one and so it can be used to well determine the number of clusters. Furthermore, multi-modality medical images with lesions can be automatically and successfully segmented by DDC method.

Effect of lesion site on serial position during list learning: a study with the CVLT.

Science.gov (United States)

Albuquerque, Luisa; Loureiro, Clara; Martins, Isabel Pavao

2008-07-01

Successful learning of supraspan word lists such as the California Verbal Learning Test (CVLT) relies more on clustering strategies than rote learning, subserved by the frontal and temporal lobes. The authors studied the effect of word sequence in CVLT learning, in 15 patients with frontal (FLL) and 15 temporal (TLL) lesions, and 33 controls. Experimental measures were: number of clusters, number of first (FI), middle (MI) and last items (LI), in learning trials and in total immediate recall. FLL disclosed significantly lower FI along learning. Clusters were similar among groups. This difficulty is discussed according to the role of frontal lobes in learning and memory.

Breast vibro-acoustography: initial experience in benign lesions

International Nuclear Information System (INIS)

Alizad, Azra; Mehrmohammadi, Mohammad; Ghosh, Karthik; Glazebrook, Katrina N; Carter, Rickey E; Karaberkmez, Leman Gunbery; Whaley, Dana H; Fatemi, Mostafa

2014-01-01

Vibro-acoustography (VA) is a newly developed imaging technology that is based on low-frequency vibrations induced in the object by the radiation force of ultrasound. VA is sensitive to the dynamic characteristics of tissue. Here, we evaluate the performance of VA in identifying benign lesions and compare the results to those of mammography. An integrated mammography-VA system designed for in vivo breast imaging was tested on a group of female volunteers, age ≥ 18 years, with suspected breast lesions based on clinical examination. A set of VA scans was acquired after each corresponding mammography. Most lesions were classified as benign based on their histological results. However, in 4 cases, initial diagnosis based on clinical imaging determined that the lesions were cysts. These cysts were aspirated with needle aspiration and disappeared completely under direct ultrasound visualization. Therefore, no biopsies were performed on these cases and lesions were classified as benign based on clinical findings per clinical standards. To define the VA characteristics of benign breast masses, we adopted the features that are normally attributed to such masses in mammography. In a blinded assessment, three radiologists evaluated the VA images independently. The diagnostic accuracy of VA for detection of benign lesions was assessed by comparing the reviewers’ evaluations with clinical data. Out of a total 29 benign lesions in the group, the reviewers were able to locate all lesions on VA images and mammography, 100% with (95% confidence interval (CI): 88% to 100%). Two reviewers were also able to correctly classify 83% (95% CI: 65% to 92%), and the third reviewer 86% (95% CI: 65% to 95%) of lesions, as benign on VA images and 86% (95% CI: 69% to 95%) on mammography. The results suggest that the mammographic characteristics of benign lesion may also be used to identify such lesions in VA. Furthermore, the results show the ability of VA to detect benign breast

A comparison of heuristic and model-based clustering methods for dietary pattern analysis.

Science.gov (United States)

Greve, Benjamin; Pigeot, Iris; Huybrechts, Inge; Pala, Valeria; Börnhorst, Claudia

2016-02-01

Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.

Bootstrap-Based Improvements for Inference with Clustered Errors

OpenAIRE

Doug Miller; A. Colin Cameron; Jonah B. Gelbach

2006-01-01

Microeconometrics researchers have increasingly realized the essential need to account for any within-group dependence in estimating standard errors of regression parameter estimates. The typical preferred solution is to calculate cluster-robust or sandwich standard errors that permit quite general heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. In applications with few (5-30) clusters, standard asymptotic tests can over-reject consid...

MRI Patterns of Isolated Lesions in the Medulla Oblongata.

Science.gov (United States)

Prakkamakul, Supada; Schaefer, Pamela; Gonzalez, Gilberto; Rapalino, Otto

2017-01-01

Isolated lesions of the medulla oblongata are difficult to diagnose due to their rarity and high biopsy risk. Several individual case reports have been published, but a systematic descriptive study is lacking. Our study has three objectives that 1) provide a differential diagnosis, 2) describe magnetic resonance imaging (MRI) findings, and 3) propose a stepwise MRI-based approach to the isolated lesions of the medulla oblongata in nonstroke patients. We performed an institutional Review Board-approved retrospective analysis of 34 consecutive cases of isolated medullary lesions from nonstroke causes identified from our imaging database between January 2000 and May 2015. Eleven were excluded due to lack of pretreatment or follow-up MRI. MR studies were reviewed by two blinded neuroradiologists. The diagnosis, demographic data, and MR findings were reported using frequencies and proportions. An MRI-based diagnostic algorithm was proposed. Most lesions were neoplasms (47%), followed by vascular malformations (15%), demyelinating/inflammatory lesions (15%), others (12%), unknown (8%), and infection (3%). Five MRI patterns were identified: 1) cystic lesion, 2) exophytic noncystic lesion, 3) intrinsic lesion with T2 hypointensity, 4) enhancing intrinsic lesion, and 5) nonenhancing intrinsic lesion. All showing patterns 1 and 2 were neoplasms or cysts. All showing pattern 3 were vascular malformations. Patterns 4 and 5 comprised of multiple etiologies. Neoplasms are the most common cause of isolated medullary lesions in nonstroke patients. Other differential diagnoses include vascular malformations, demyelinating/inflammatory lesions, and infections. A stepwise MRI-based approach can help differentiate between various etiologies. Copyright © 2016 by the American Society of Neuroimaging.

Biomarker clusters are differentially associated with longitudinal cognitive decline in late midlife

Science.gov (United States)

Racine, Annie M.; Koscik, Rebecca L.; Berman, Sara E.; Nicholas, Christopher R.; Clark, Lindsay R.; Okonkwo, Ozioma C.; Rowley, Howard A.; Asthana, Sanjay; Bendlin, Barbara B.; Blennow, Kaj; Zetterberg, Henrik; Gleason, Carey E.; Carlsson, Cynthia M.

2016-01-01

The ability to detect preclinical Alzheimer’s disease is of great importance, as this stage of the Alzheimer’s continuum is believed to provide a key window for intervention and prevention. As Alzheimer’s disease is characterized by multiple pathological changes, a biomarker panel reflecting co-occurring pathology will likely be most useful for early detection. Towards this end, 175 late middle-aged participants (mean age 55.9 ± 5.7 years at first cognitive assessment, 70% female) were recruited from two longitudinally followed cohorts to undergo magnetic resonance imaging and lumbar puncture. Cluster analysis was used to group individuals based on biomarkers of amyloid pathology (cerebrospinal fluid amyloid-β42/amyloid-β40 assay levels), magnetic resonance imaging-derived measures of neurodegeneration/atrophy (cerebrospinal fluid-to-brain volume ratio, and hippocampal volume), neurofibrillary tangles (cerebrospinal fluid phosphorylated tau181 assay levels), and a brain-based marker of vascular risk (total white matter hyperintensity lesion volume). Four biomarker clusters emerged consistent with preclinical features of (i) Alzheimer’s disease; (ii) mixed Alzheimer’s disease and vascular aetiology; (iii) suspected non-Alzheimer’s disease aetiology; and (iv) healthy ageing. Cognitive decline was then analysed between clusters using longitudinal assessments of episodic memory, semantic memory, executive function, and global cognitive function with linear mixed effects modelling. Cluster 1 exhibited a higher intercept and greater rates of decline on tests of episodic memory. Cluster 2 had a lower intercept on a test of semantic memory and both Cluster 2 and Cluster 3 had steeper rates of decline on a test of global cognition. Additional analyses on Cluster 3, which had the smallest hippocampal volume, suggest that its biomarker profile is more likely due to hippocampal vulnerability and not to detectable specific volume loss exceeding the rate of normal

A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

Directory of Open Access Journals (Sweden)

Yunfeng Dong

2017-01-01

Full Text Available The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM, which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM. In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM.

An Improved Semisupervised Outlier Detection Algorithm Based on Adaptive Feature Weighted Clustering

Directory of Open Access Journals (Sweden)

Tingquan Deng

2016-01-01

Full Text Available There exist already various approaches to outlier detection, in which semisupervised methods achieve encouraging superiority due to the introduction of prior knowledge. In this paper, an adaptive feature weighted clustering-based semisupervised outlier detection strategy is proposed. This method maximizes the membership degree of a labeled normal object to the cluster it belongs to and minimizes the membership degrees of a labeled outlier to all clusters. In consideration of distinct significance of features or components in a dataset in determining an object being an inlier or outlier, each feature is adaptively assigned different weights according to the deviation degrees between this feature of all objects and that of a certain cluster prototype. A series of experiments on a synthetic dataset and several real-world datasets are implemented to verify the effectiveness and efficiency of the proposal.

Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach.

Science.gov (United States)

Pasupa, Kitsuchart; Kudisthalert, Wasu

2018-01-01

Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets-Maximum Unbiased Validation Dataset-which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6.

Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Dao-Wei Bi

2007-07-01

Full Text Available A primary criterion of wireless sensor network is energy efficiency. Focused onthe energy problem of target tracking in wireless sensor networks, this paper proposes acluster-based dynamic energy management mechanism. Target tracking problem isformulated by the multi-sensor detection model as well as energy consumption model. Adistributed adaptive clustering approach is investigated to form a reasonable routingframework which has uniform cluster head distribution. Dijkstra’s algorithm is utilized toobtain optimal intra-cluster routing. Target position is predicted by particle filter. Thepredicted target position is adopted to estimate the idle interval of sensor nodes. Hence,dynamic awakening approach is exploited to prolong sleep time of sensor nodes so that theoperation energy consumption of wireless sensor network can be reduced. The sensornodes around the target wake up on time and act as sensing candidates. With the candidatesensor nodes and predicted target position, the optimal sensor node selection is considered.Binary particle swarm optimization is proposed to minimize the total energy consumptionduring collaborative sensing and data reporting. Experimental results verify that theproposed clustering approach establishes a low-energy communication structure while theenergy efficiency of wireless sensor networks is enhanced by cluster-based dynamic energymanagement.

Hedgehog bases for A{sub n} cluster polylogarithms and an application to six-point amplitudes

Energy Technology Data Exchange (ETDEWEB)

Parker, Daniel E.; Scherlis, Adam; Spradlin, Marcus; Volovich, Anastasia [Department of Physics, Brown University, Providence RI 02912 (United States)

2015-11-20

Multi-loop scattering amplitudes in N=4 Yang-Mills theory possess cluster algebra structure. In order to develop a computational framework which exploits this connection, we show how to construct bases of Goncharov polylogarithm functions, at any weight, whose symbol alphabet consists of cluster coordinates on the A{sub n} cluster algebra. Using such a basis we present a new expression for the 2-loop 6-particle NMHV amplitude which makes some of its cluster structure manifest.

Innovative Development of Building Materials Industry of the Region Based on the Cluster Approach

Directory of Open Access Journals (Sweden)

Mottaeva Asiiat

2016-01-01

Full Text Available The article discusses issues of innovative development of building materials industry of the region based on the cluster approach. Determined the significance of regional cluster development of the industry of construction materials as the effective implementation of the innovative breakthrough of the region as an important part of strategies for strengthening innovation activities may be to support the formation and development of cluster structures. Analyses the current situation with innovation in the building materials industry of the region based on the cluster approach. In the course of the study revealed a direct correlation between involvement in innovative activities on a cluster basis, and the level of development of industry of construction materials. The conducted research allowed identifying the factors that determine the innovation process, systematization and classification which determine the sustainable functioning of the building materials industry in the period of active innovation. The proposed grouping of innovations for the construction industry taking into account industry-specific characteristics that reflect modern trends of scientific and technological progress in construction. Significance of the study lies in the fact that the proposals and practical recommendations can be used in the formation mechanism of innovative development of building materials industry and the overall regional construction complex of Russian regions by creating clusters of construction.

Trend analysis using non-stationary time series clustering based on the finite element method

Science.gov (United States)

Gorji Sefidmazgi, M.; Sayemuzzaman, M.; Homaifar, A.; Jha, M. K.; Liess, S.

2014-05-01

In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods that can analyze multidimensional time series. One important attribute of this method is that it is not dependent on any statistical assumption and does not need local stationarity in the time series. In this paper, it is shown how the FEM-clustering method can be used to locate change points in the trend of temperature time series from in situ observations. This method is applied to the temperature time series of North Carolina (NC) and the results represent region-specific climate variability despite higher frequency harmonics in climatic time series. Next, we investigated the relationship between the climatic indices with the clusters/trends detected based on this clustering method. It appears that the natural variability of climate change in NC during 1950-2009 can be explained mostly by AMO and solar activity.

Cluster-based control of a separating flow over a smoothly contoured ramp

Science.gov (United States)

Kaiser, Eurika; Noack, Bernd R.; Spohn, Andreas; Cattafesta, Louis N.; Morzyński, Marek

2017-12-01

The ability to manipulate and control fluid flows is of great importance in many scientific and engineering applications. The proposed closed-loop control framework addresses a key issue of model-based control: The actuation effect often results from slow dynamics of strongly nonlinear interactions which the flow reveals at timescales much longer than the prediction horizon of any model. Hence, we employ a probabilistic approach based on a cluster-based discretization of the Liouville equation for the evolution of the probability distribution. The proposed methodology frames high-dimensional, nonlinear dynamics into low-dimensional, probabilistic, linear dynamics which considerably simplifies the optimal control problem while preserving nonlinear actuation mechanisms. The data-driven approach builds upon a state space discretization using a clustering algorithm which groups kinematically similar flow states into a low number of clusters. The temporal evolution of the probability distribution on this set of clusters is then described by a control-dependent Markov model. This Markov model can be used as predictor for the ergodic probability distribution for a particular control law. This probability distribution approximates the long-term behavior of the original system on which basis the optimal control law is determined. We examine how the approach can be used to improve the open-loop actuation in a separating flow dominated by Kelvin-Helmholtz shedding. For this purpose, the feature space, in which the model is learned, and the admissible control inputs are tailored to strongly oscillatory flows.

An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network.

Science.gov (United States)

Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

2015-01-01

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.

Clustering cliques for graph-based summarization of the biomedical research literature

DEFF Research Database (Denmark)

Zhang, Han; Fiszman, Marcelo; Shin, Dongwook

2013-01-01

Background: Graph-based notions are increasingly used in biomedical data mining and knowledge discovery tasks. In this paper, we present a clique-clustering method to automatically summarize graphs of semantic predications produced from PubMed citations (titles and abstracts).Results: Sem......Rep is used to extract semantic predications from the citations returned by a PubMed search. Cliques were identified from frequently occurring predications with highly connected arguments filtered by degree centrality. Themes contained in the summary were identified with a hierarchical clustering algorithm...

Cluster-Based Adaptation Using Density Forest for HMM Phone Recognition

DEFF Research Database (Denmark)

Abou-Zleikha, Mohamed; Tan, Zheng-Hua; Christensen, Mads Græsbøll

2014-01-01

The dissimilarity between the training and test data in speech recognition systems is known to have a considerable effect on the recognition accuracy. To solve this problem, we use density forest to cluster the data and use maximum a posteriori (MAP) method to build a cluster-based adapted Gaussian...... mixture models (GMMs) in HMM speech recognition. Specifically, a set of bagged versions of the training data for each state in the HMM is generated, and each of these versions is used to generate one GMM and one tree in the density forest. Thereafter, an acoustic model forest is built by replacing...... the data of each leaf (cluster) in each tree with the corresponding GMM adapted by the leaf data using the MAP method. The results show that the proposed approach achieves 3:8% (absolute) lower phone error rate compared with the standard HMM/GMM and 0:8% (absolute) lower PER compared with bagged HMM/GMM....

The Post-Ureteroscopic Lesion Scale (PULS)

DEFF Research Database (Denmark)

Schoenthaler, Martin; Buchholz, Noor; Farin, Erik

2014-01-01

The Post-Ureteroscopic Lesion Scale (PULS) offers a simple grading system for the description of ureteral lesions after ureteroscopy. In this article, we present the results of a video-based multicenter evaluation of the inter-rater reliability of clinically important PULS grades 0-3....

Multiple-Features-Based Semisupervised Clustering DDoS Detection Method

Directory of Open Access Journals (Sweden)

Yonghao Gu

2017-01-01

Full Text Available DDoS attack stream from different agent host converged at victim host will become very large, which will lead to system halt or network congestion. Therefore, it is necessary to propose an effective method to detect the DDoS attack behavior from the massive data stream. In order to solve the problem that large numbers of labeled data are not provided in supervised learning method, and the relatively low detection accuracy and convergence speed of unsupervised k-means algorithm, this paper presents a semisupervised clustering detection method using multiple features. In this detection method, we firstly select three features according to the characteristics of DDoS attacks to form detection feature vector. Then, Multiple-Features-Based Constrained-K-Means (MF-CKM algorithm is proposed based on semisupervised clustering. Finally, using MIT Laboratory Scenario (DDoS 1.0 data set, we verify that the proposed method can improve the convergence speed and accuracy of the algorithm under the condition of using a small amount of labeled data sets.

On the biophysical interpretation of lethal DNA lesions induced by ionising radiation

Czech Academy of Sciences Publication Activity Database

Kundrát, Pavel; Stewart, R.D.

2006-01-01

Roč. 122, 1-4 (2006), s. 169-172 ISSN 0144-8420 R&D Projects: GA ČR GA202/05/2728 Institutional research plan: CEZ:AV0Z10100502 Keywords : clustered DNA lesions * V79 cells * proton tracks * DNA damage * DNA repair * radiobiological modelling Subject RIV: BF - Elementary Particles and High Energy Physics Impact factor: 0.446, year: 2006