Within-Cluster and Across-Cluster Matching with Observational Multilevel Data

ERIC Educational Resources Information Center

Kim, Jee-Seon; Steiner, Peter M.; Hall, Courtney; Thoemmes, Felix

2013-01-01

When randomized experiments cannot be conducted in practice, propensity score (PS) techniques for matching treated and control units are frequently used for estimating causal treatment effects from observational data. Despite the popularity of PS techniques, they are not yet well studied for matching multilevel data where selection into treatment…

A large-scale cluster randomized trial to determine the effects of community-based dietary sodium reduction--the China Rural Health Initiative Sodium Reduction Study.

PubMed

Li, Nicole; Yan, Lijing L; Niu, Wenyi; Labarthe, Darwin; Feng, Xiangxian; Shi, Jingpu; Zhang, Jianxin; Zhang, Ruijuan; Zhang, Yuhong; Chu, Hongling; Neiman, Andrea; Engelgau, Michael; Elliott, Paul; Wu, Yangfeng; Neal, Bruce

2013-11-01

Cardiovascular diseases are the leading cause of death and disability in China. High blood pressure caused by excess intake of dietary sodium is widespread and an effective sodium reduction program has potential to improve cardiovascular health. This study is a large-scale, cluster-randomized, trial done in five Northern Chinese provinces. Two counties have been selected from each province and 12 townships in each county making a total of 120 clusters. Within each township one village has been selected for participation with 1:1 randomization stratified by county. The sodium reduction intervention comprises community health education and a food supply strategy based upon providing access to salt substitute. Subsidization of the price of salt substitute was done in 30 intervention villages selected at random. Control villages continued usual practices. The primary outcome for the study is dietary sodium intake level estimated from assays of 24-hour urine. The trial recruited and randomized 120 townships in April 2011. The sodium reduction program was commenced in the 60 intervention villages between May and June of that year with outcome surveys scheduled for October to December 2012. Baseline data collection shows that randomisation achieved good balance across groups. The establishment of the China Rural Health Initiative has enabled the launch of this large-scale trial designed to identify a novel, scalable strategy for reduction of dietary sodium and control of blood pressure. If proved effective, the intervention could plausibly be implemented at low cost in large parts of China and other countries worldwide. © 2013.

Clustering of financial time series with application to index and enhanced index tracking portfolio

NASA Astrophysics Data System (ADS)

Dose, Christian; Cincotti, Silvano

2005-09-01

A stochastic-optimization technique based on time series cluster analysis is described for index tracking and enhanced index tracking problems. Our methodology solves the problem in two steps, i.e., by first selecting a subset of stocks and then setting the weight of each stock as a result of an optimization process (asset allocation). Present formulation takes into account constraints on the number of stocks and on the fraction of capital invested in each of them, whilst not including transaction costs. Computational results based on clustering selection are compared to those of random techniques and show the importance of clustering in noise reduction and robust forecasting applications, in particular for enhanced index tracking.

Bootstrap-based methods for estimating standard errors in Cox's regression analyses of clustered event times.

PubMed

Xiao, Yongling; Abrahamowicz, Michal

2010-03-30

We propose two bootstrap-based methods to correct the standard errors (SEs) from Cox's model for within-cluster correlation of right-censored event times. The cluster-bootstrap method resamples, with replacement, only the clusters, whereas the two-step bootstrap method resamples (i) the clusters, and (ii) individuals within each selected cluster, with replacement. In simulations, we evaluate both methods and compare them with the existing robust variance estimator and the shared gamma frailty model, which are available in statistical software packages. We simulate clustered event time data, with latent cluster-level random effects, which are ignored in the conventional Cox's model. For cluster-level covariates, both proposed bootstrap methods yield accurate SEs, and type I error rates, and acceptable coverage rates, regardless of the true random effects distribution, and avoid serious variance under-estimation by conventional Cox-based standard errors. However, the two-step bootstrap method over-estimates the variance for individual-level covariates. We also apply the proposed bootstrap methods to obtain confidence bands around flexible estimates of time-dependent effects in a real-life analysis of cluster event times.

Improved training for target detection using Fukunaga-Koontz transform and distance classifier correlation filter

NASA Astrophysics Data System (ADS)

Elbakary, M. I.; Alam, M. S.; Aslan, M. S.

2008-03-01

In a FLIR image sequence, a target may disappear permanently or may reappear after some frames and crucial information such as direction, position and size related to the target are lost. If the target reappears at a later frame, it may not be tracked again because the 3D orientation, size and location of the target might be changed. To obtain information about the target before disappearing and to detect the target after reappearing, distance classifier correlation filter (DCCF) is trained manualy by selecting a number of chips randomly. This paper introduces a novel idea to eliminates the manual intervention in training phase of DCCF. Instead of selecting the training chips manually and selecting the number of the training chips randomly, we adopted the K-means algorithm to cluster the training frames and based on the number of clusters we select the training chips such that a training chip for each cluster. To detect and track the target after reappearing in the field-ofview ,TBF and DCCF are employed. The contduced experiemnts using real FLIR sequences show results similar to the traditional agorithm but eleminating the manual intervention is the advantage of the proposed algorithm.

Sample size calculations for stepped wedge and cluster randomised trials: a unified approach

PubMed Central

Hemming, Karla; Taljaard, Monica

2016-01-01

Objectives To clarify and illustrate sample size calculations for the cross-sectional stepped wedge cluster randomized trial (SW-CRT) and to present a simple approach for comparing the efficiencies of competing designs within a unified framework. Study Design and Setting We summarize design effects for the SW-CRT, the parallel cluster randomized trial (CRT), and the parallel cluster randomized trial with before and after observations (CRT-BA), assuming cross-sectional samples are selected over time. We present new formulas that enable trialists to determine the required cluster size for a given number of clusters. We illustrate by example how to implement the presented design effects and give practical guidance on the design of stepped wedge studies. Results For a fixed total cluster size, the choice of study design that provides the greatest power depends on the intracluster correlation coefficient (ICC) and the cluster size. When the ICC is small, the CRT tends to be more efficient; when the ICC is large, the SW-CRT tends to be more efficient and can serve as an alternative design when the CRT is an infeasible design. Conclusion Our unified approach allows trialists to easily compare the efficiencies of three competing designs to inform the decision about the most efficient design in a given scenario. PMID:26344808

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

PubMed Central

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. PMID:29652912

Cluster-Glass Phase in Pyrochlore X Y Antiferromagnets with Quenched Disorder

NASA Astrophysics Data System (ADS)

Andrade, Eric C.; Hoyos, José A.; Rachel, Stephan; Vojta, Matthias

2018-03-01

We study the impact of quenched disorder (random exchange couplings or site dilution) on easy-plane pyrochlore antiferromagnets. In the clean system, order by disorder selects a magnetically ordered state from a classically degenerate manifold. In the presence of randomness, however, different orders can be chosen locally depending on details of the disorder configuration. Using a combination of analytical considerations and classical Monte Carlo simulations, we argue that any long-range-ordered magnetic state is destroyed beyond a critical level of randomness where the system breaks into magnetic domains due to random exchange anisotropies, becoming, therefore, a glass of spin clusters, in accordance with the available experimental data. These random anisotropies originate from off-diagonal exchange couplings in the microscopic Hamiltonian, establishing their relevance to other magnets with strong spin-orbit coupling.

Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data

PubMed Central

Tian, Ting; McLachlan, Geoffrey J.; Dieters, Mark J.; Basford, Kaye E.

2015-01-01

It is a common occurrence in plant breeding programs to observe missing values in three-way three-mode multi-environment trial (MET) data. We proposed modifications of models for estimating missing observations for these data arrays, and developed a novel approach in terms of hierarchical clustering. Multiple imputation (MI) was used in four ways, multiple agglomerative hierarchical clustering, normal distribution model, normal regression model, and predictive mean match. The later three models used both Bayesian analysis and non-Bayesian analysis, while the first approach used a clustering procedure with randomly selected attributes and assigned real values from the nearest neighbour to the one with missing observations. Different proportions of data entries in six complete datasets were randomly selected to be missing and the MI methods were compared based on the efficiency and accuracy of estimating those values. The results indicated that the models using Bayesian analysis had slightly higher accuracy of estimation performance than those using non-Bayesian analysis but they were more time-consuming. However, the novel approach of multiple agglomerative hierarchical clustering demonstrated the overall best performances. PMID:26689369

Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data.

PubMed

Tian, Ting; McLachlan, Geoffrey J; Dieters, Mark J; Basford, Kaye E

2015-01-01

It is a common occurrence in plant breeding programs to observe missing values in three-way three-mode multi-environment trial (MET) data. We proposed modifications of models for estimating missing observations for these data arrays, and developed a novel approach in terms of hierarchical clustering. Multiple imputation (MI) was used in four ways, multiple agglomerative hierarchical clustering, normal distribution model, normal regression model, and predictive mean match. The later three models used both Bayesian analysis and non-Bayesian analysis, while the first approach used a clustering procedure with randomly selected attributes and assigned real values from the nearest neighbour to the one with missing observations. Different proportions of data entries in six complete datasets were randomly selected to be missing and the MI methods were compared based on the efficiency and accuracy of estimating those values. The results indicated that the models using Bayesian analysis had slightly higher accuracy of estimation performance than those using non-Bayesian analysis but they were more time-consuming. However, the novel approach of multiple agglomerative hierarchical clustering demonstrated the overall best performances.

Random Walk Quantum Clustering Algorithm Based on Space

NASA Astrophysics Data System (ADS)

Xiao, Shufen; Dong, Yumin; Ma, Hongyang

2018-01-01

In the random quantum walk, which is a quantum simulation of the classical walk, data points interacted when selecting the appropriate walk strategy by taking advantage of quantum-entanglement features; thus, the results obtained when the quantum walk is used are different from those when the classical walk is adopted. A new quantum walk clustering algorithm based on space is proposed by applying the quantum walk to clustering analysis. In this algorithm, data points are viewed as walking participants, and similar data points are clustered using the walk function in the pay-off matrix according to a certain rule. The walk process is simplified by implementing a space-combining rule. The proposed algorithm is validated by a simulation test and is proved superior to existing clustering algorithms, namely, Kmeans, PCA + Kmeans, and LDA-Km. The effects of some of the parameters in the proposed algorithm on its performance are also analyzed and discussed. Specific suggestions are provided.

A LARGE-SCALE CLUSTER RANDOMIZED TRIAL TO DETERMINE THE EFFECTS OF COMMUNITY-BASED DIETARY SODIUM REDUCTION – THE CHINA RURAL HEALTH INITIATIVE SODIUM REDUCTION STUDY

PubMed Central

Li, Nicole; Yan, Lijing L.; Niu, Wenyi; Labarthe, Darwin; Feng, Xiangxian; Shi, Jingpu; Zhang, Jianxin; Zhang, Ruijuan; Zhang, Yuhong; Chu, Hongling; Neiman, Andrea; Engelgau, Michael; Elliott, Paul; Wu, Yangfeng; Neal, Bruce

2013-01-01

Background Cardiovascular diseases are the leading cause of death and disability in China. High blood pressure caused by excess intake of dietary sodium is widespread and an effective sodium reduction program has potential to improve cardiovascular health. Design This study is a large-scale, cluster-randomized, trial done in five Northern Chinese provinces. Two counties have been selected from each province and 12 townships in each county making a total of 120 clusters. Within each township one village has been selected for participation with 1:1 randomization stratified by county. The sodium reduction intervention comprises community health education and a food supply strategy based upon providing access to salt substitute. Subsidization of the price of salt substitute was done in 30 intervention villages selected at random. Control villages continued usual practices. The primary outcome for the study is dietary sodium intake level estimated from assays of 24 hour urine. Trial status The trial recruited and randomized 120 townships in April 2011. The sodium reduction program was commenced in the 60 intervention villages between May and June of that year with outcome surveys scheduled for October to December 2012. Baseline data collection shows that randomisation achieved good balance across groups. Discussion The establishment of the China Rural Health Initiative has enabled the launch of this large-scale trial designed to identify a novel, scalable strategy for reduction of dietary sodium and control of blood pressure. If proved effective, the intervention could plausibly be implemented at low cost in large parts of China and other countries worldwide. PMID:24176436

Cluster Supervision Practices in Primary School of Jimma Zone

ERIC Educational Resources Information Center

Afework, E. A.; Frew, A. T.; Abeya, G. G.

2017-01-01

The main objective of this study was to assess the supervisory practice of cluster resource centre (CRC) supervisors in Jimma Zone primary schools. To achieve this purpose, the descriptive survey design was employed. Data were collected from 238 randomly selected teachers, and 60 school principals with a response rate of 98.6%. Moreover, 12 CRC…

An enhanced deterministic K-Means clustering algorithm for cancer subtype prediction from gene expression data.

PubMed

Nidheesh, N; Abdul Nazeer, K A; Ameer, P M

2017-12-01

Clustering algorithms with steps involving randomness usually give different results on different executions for the same dataset. This non-deterministic nature of algorithms such as the K-Means clustering algorithm limits their applicability in areas such as cancer subtype prediction using gene expression data. It is hard to sensibly compare the results of such algorithms with those of other algorithms. The non-deterministic nature of K-Means is due to its random selection of data points as initial centroids. We propose an improved, density based version of K-Means, which involves a novel and systematic method for selecting initial centroids. The key idea of the algorithm is to select data points which belong to dense regions and which are adequately separated in feature space as the initial centroids. We compared the proposed algorithm to a set of eleven widely used single clustering algorithms and a prominent ensemble clustering algorithm which is being used for cancer data classification, based on the performances on a set of datasets comprising ten cancer gene expression datasets. The proposed algorithm has shown better overall performance than the others. There is a pressing need in the Biomedical domain for simple, easy-to-use and more accurate Machine Learning tools for cancer subtype prediction. The proposed algorithm is simple, easy-to-use and gives stable results. Moreover, it provides comparatively better predictions of cancer subtypes from gene expression data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Computational lymphatic node models in pediatric and adult hybrid phantoms for radiation dosimetry

NASA Astrophysics Data System (ADS)

Lee, Choonsik; Lamart, Stephanie; Moroz, Brian E.

2013-03-01

We developed models of lymphatic nodes for six pediatric and two adult hybrid computational phantoms to calculate the lymphatic node dose estimates from external and internal radiation exposures. We derived the number of lymphatic nodes from the recommendations in International Commission on Radiological Protection (ICRP) Publications 23 and 89 at 16 cluster locations for the lymphatic nodes: extrathoracic, cervical, thoracic (upper and lower), breast (left and right), mesentery (left and right), axillary (left and right), cubital (left and right), inguinal (left and right) and popliteal (left and right), for different ages (newborn, 1-, 5-, 10-, 15-year-old and adult). We modeled each lymphatic node within the voxel format of the hybrid phantoms by assuming that all nodes have identical size derived from published data except narrow cluster sites. The lymph nodes were generated by the following algorithm: (1) selection of the lymph node site among the 16 cluster sites; (2) random sampling of the location of the lymph node within a spherical space centered at the chosen cluster site; (3) creation of the sphere or ovoid of tissue representing the node based on lymphatic node characteristics defined in ICRP Publications 23 and 89. We created lymph nodes until the pre-defined number of lymphatic nodes at the selected cluster site was reached. This algorithm was applied to pediatric (newborn, 1-, 5-and 10-year-old male, and 15-year-old males) and adult male and female ICRP-compliant hybrid phantoms after voxelization. To assess the performance of our models for internal dosimetry, we calculated dose conversion coefficients, called S values, for selected organs and tissues with Iodine-131 distributed in six lymphatic node cluster sites using MCNPX2.6, a well validated Monte Carlo radiation transport code. Our analysis of the calculations indicates that the S values were significantly affected by the location of the lymph node clusters and that the values increased for smaller phantoms due to the shorter inter-organ distances compared to the bigger phantoms. By testing sensitivity of S values to random sampling and voxel resolution, we confirmed that the lymph node model is reasonably stable and consistent for different random samplings and voxel resolutions.

Emotional Intelligence and Life Adjustment for Nigerian Secondary Students

ERIC Educational Resources Information Center

Ogoemeka, Obioma Helen

2013-01-01

In the process of educating adolescents, good emotional development and life adjustment are two significant factors for teachers to know. This study employed random cluster sampling of senior secondary school students in Ondo and Oyo States in south-western Nigeria. The Random sampling was employed to select 1,070 students. The data collected were…

Input variable selection and calibration data selection for storm water quality regression models.

PubMed

Sun, Siao; Bertrand-Krajewski, Jean-Luc

2013-01-01

Storm water quality models are useful tools in storm water management. Interest has been growing in analyzing existing data for developing models for urban storm water quality evaluations. It is important to select appropriate model inputs when many candidate explanatory variables are available. Model calibration and verification are essential steps in any storm water quality modeling. This study investigates input variable selection and calibration data selection in storm water quality regression models. The two selection problems are mutually interacted. A procedure is developed in order to fulfil the two selection tasks in order. The procedure firstly selects model input variables using a cross validation method. An appropriate number of variables are identified as model inputs to ensure that a model is neither overfitted nor underfitted. Based on the model input selection results, calibration data selection is studied. Uncertainty of model performances due to calibration data selection is investigated with a random selection method. An approach using the cluster method is applied in order to enhance model calibration practice based on the principle of selecting representative data for calibration. The comparison between results from the cluster selection method and random selection shows that the former can significantly improve performances of calibrated models. It is found that the information content in calibration data is important in addition to the size of calibration data.

Theory-based behavioral intervention increases self-reported physical activity in South African men: a cluster-randomized controlled trial.

PubMed

Jemmott, John B; Jemmott, Loretta S; Ngwane, Zolani; Zhang, Jingwen; Heeren, G Anita; Icard, Larry D; O'Leary, Ann; Mtose, Xoliswa; Teitelman, Anne; Carty, Craig

2014-07-01

To determine whether a health-promotion intervention increases South African men's adherence to physical-activity guidelines. We utilized a cluster-randomized controlled trial design. Eligible clusters, residential neighborhoods near East London, South Africa, were matched in pairs. Within randomly selected pairs, neighborhoods were randomized to theory-based, culturally congruent health-promotion intervention encouraging physical activity or attention-matched HIV/STI risk-reduction control intervention. Men residing in the neighborhoods and reporting coitus in the previous 3 months were eligible. Primary outcome was self-reported individual-level adherence to physical-activity guidelines averaged over 6-month and 12-month post-intervention assessments. Data were collected in 2007-2010. Data collectors, but not facilitators or participants, were blind to group assignment. Primary outcome intention-to-treat analysis included 22 of 22 clusters and 537 of 572 men in the health-promotion intervention and 22 of 22 clusters and 569 of 609 men in the attention-control intervention. Model-estimated probability of meeting physical-activity guidelines was 51.0% in the health-promotion intervention and 44.7% in attention-matched control (OR=1.34; 95% CI, 1.09-1.63), adjusting for baseline prevalence and clustering from 44 neighborhoods. A theory-based culturally congruent intervention increased South African men's self-reported physical activity, a key contributor to deaths from non-communicable diseases in South Africa. ClinicalTrials.gov Identifier: NCT01490359. Copyright © 2014 Elsevier Inc. All rights reserved.

Deposition of Size-Selected Cu Nanoparticles by Inert Gas Condensation

PubMed Central

2010-01-01

Nanometer size-selected Cu clusters in the size range of 1–5 nm have been produced by a plasma-gas-condensation-type cluster deposition apparatus, which combines a grow-discharge sputtering with an inert gas condensation technique. With this method, by controlling the experimental conditions, it was possible to produce nanoparticles with a strict control in size. The structure and size of Cu nanoparticles were determined by mass spectroscopy and confirmed by atomic force microscopy (AFM) and scanning electron transmission microscopy (STEM) measurements. In order to preserve the structural and morphological properties, the energy of cluster impact was controlled; the energy of acceleration of the nanoparticles was in near values at 0.1 ev/atom for being in soft landing regime. From SEM measurements developed in STEM-HAADF mode, we found that nanoparticles are near sized to those values fixed experimentally also confirmed by AFM observations. The results are relevant, since it demonstrates that proper optimization of operation conditions can lead to desired cluster sizes as well as desired cluster size distributions. It was also demonstrated the efficiency of the method to obtain size-selected Cu clusters films, as a random stacking of nanometer-size crystallites assembly. The deposition of size-selected metal clusters represents a novel method of preparing Cu nanostructures, with high potential in optical and catalytic applications. PMID:20652132

Clustering of galaxies near damped Lyman-alpha systems with (z) = 2.6

NASA Technical Reports Server (NTRS)

Wolfe, A. M

1993-01-01

The galaxy two-point correlation function, xi, at (z) = 2.6 is determined by comparing the number of Ly-alpha-emitting galaxies in narrowband CCD fields selected for the presence of damped L-alpha absorption to their number in randomly selected control fields. Comparisons between the presented determination of (xi), a density-weighted volume average of xi, and model predictions for (xi) at large redshifts show that models in which the clustering pattern is fixed in proper coordinates are highly unlikely, while better agreement is obtained if the clustering pattern is fixed in comoving coordinates. Therefore, clustering of Ly-alpha-emitting galaxies around damped Ly-alpha systems at large redshifts is strong. It is concluded that the faint blue galaxies are drawn from a parent population different from normal galaxies, the presumed offspring of damped Ly-alpha systems.

Performance Analysis of Cluster Formation in Wireless Sensor Networks.

PubMed

Montiel, Edgar Romo; Rivero-Angeles, Mario E; Rubino, Gerardo; Molina-Lozano, Heron; Menchaca-Mendez, Rolando; Menchaca-Mendez, Ricardo

2017-12-13

Clustered-based wireless sensor networks have been extensively used in the literature in order to achieve considerable energy consumption reductions. However, two aspects of such systems have been largely overlooked. Namely, the transmission probability used during the cluster formation phase and the way in which cluster heads are selected. Both of these issues have an important impact on the performance of the system. For the former, it is common to consider that sensor nodes in a clustered-based Wireless Sensor Network (WSN) use a fixed transmission probability to send control data in order to build the clusters. However, due to the highly variable conditions experienced by these networks, a fixed transmission probability may lead to extra energy consumption. In view of this, three different transmission probability strategies are studied: optimal, fixed and adaptive. In this context, we also investigate cluster head selection schemes, specifically, we consider two intelligent schemes based on the fuzzy C-means and k-medoids algorithms and a random selection with no intelligence. We show that the use of intelligent schemes greatly improves the performance of the system, but their use entails higher complexity and selection delay. The main performance metrics considered in this work are energy consumption, successful transmission probability and cluster formation latency. As an additional feature of this work, we study the effect of errors in the wireless channel and the impact on the performance of the system under the different transmission probability schemes.

Performance Analysis of Cluster Formation in Wireless Sensor Networks

PubMed Central

Montiel, Edgar Romo; Rivero-Angeles, Mario E.; Rubino, Gerardo; Molina-Lozano, Heron; Menchaca-Mendez, Rolando; Menchaca-Mendez, Ricardo

2017-01-01

Clustered-based wireless sensor networks have been extensively used in the literature in order to achieve considerable energy consumption reductions. However, two aspects of such systems have been largely overlooked. Namely, the transmission probability used during the cluster formation phase and the way in which cluster heads are selected. Both of these issues have an important impact on the performance of the system. For the former, it is common to consider that sensor nodes in a clustered-based Wireless Sensor Network (WSN) use a fixed transmission probability to send control data in order to build the clusters. However, due to the highly variable conditions experienced by these networks, a fixed transmission probability may lead to extra energy consumption. In view of this, three different transmission probability strategies are studied: optimal, fixed and adaptive. In this context, we also investigate cluster head selection schemes, specifically, we consider two intelligent schemes based on the fuzzy C-means and k-medoids algorithms and a random selection with no intelligence. We show that the use of intelligent schemes greatly improves the performance of the system, but their use entails higher complexity and selection delay. The main performance metrics considered in this work are energy consumption, successful transmission probability and cluster formation latency. As an additional feature of this work, we study the effect of errors in the wireless channel and the impact on the performance of the system under the different transmission probability schemes. PMID:29236065

Inference from clustering with application to gene-expression microarrays.

PubMed

Dougherty, Edward R; Barrera, Junior; Brun, Marcel; Kim, Seungchan; Cesar, Roberto M; Chen, Yidong; Bittner, Michael; Trent, Jeffrey M

2002-01-01

There are many algorithms to cluster sample data points based on nearness or a similarity measure. Often the implication is that points in different clusters come from different underlying classes, whereas those in the same cluster come from the same class. Stochastically, the underlying classes represent different random processes. The inference is that clusters represent a partition of the sample points according to which process they belong. This paper discusses a model-based clustering toolbox that evaluates cluster accuracy. Each random process is modeled as its mean plus independent noise, sample points are generated, the points are clustered, and the clustering error is the number of points clustered incorrectly according to the generating random processes. Various clustering algorithms are evaluated based on process variance and the key issue of the rate at which algorithmic performance improves with increasing numbers of experimental replications. The model means can be selected by hand to test the separability of expected types of biological expression patterns. Alternatively, the model can be seeded by real data to test the expected precision of that output or the extent of improvement in precision that replication could provide. In the latter case, a clustering algorithm is used to form clusters, and the model is seeded with the means and variances of these clusters. Other algorithms are then tested relative to the seeding algorithm. Results are averaged over various seeds. Output includes error tables and graphs, confusion matrices, principal-component plots, and validation measures. Five algorithms are studied in detail: K-means, fuzzy C-means, self-organizing maps, hierarchical Euclidean-distance-based and correlation-based clustering. The toolbox is applied to gene-expression clustering based on cDNA microarrays using real data. Expression profile graphics are generated and error analysis is displayed within the context of these profile graphics. A large amount of generated output is available over the web.

Simulating star clusters with the AMUSE software framework. I. Dependence of cluster lifetimes on model assumptions and cluster dissolution modes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whitehead, Alfred J.; McMillan, Stephen L. W.; Vesperini, Enrico

2013-12-01

We perform a series of simulations of evolving star clusters using the Astrophysical Multipurpose Software Environment (AMUSE), a new community-based multi-physics simulation package, and compare our results to existing work. These simulations model a star cluster beginning with a King model distribution and a selection of power-law initial mass functions and contain a tidal cutoff. They are evolved using collisional stellar dynamics and include mass loss due to stellar evolution. After studying and understanding that the differences between AMUSE results and results from previous studies are understood, we explored the variation in cluster lifetimes due to the random realization noisemore » introduced by transforming a King model to specific initial conditions. This random realization noise can affect the lifetime of a simulated star cluster by up to 30%. Two modes of star cluster dissolution were identified: a mass evolution curve that contains a runaway cluster dissolution with a sudden loss of mass, and a dissolution mode that does not contain this feature. We refer to these dissolution modes as 'dynamical' and 'relaxation' dominated, respectively. For Salpeter-like initial mass functions, we determined the boundary between these two modes in terms of the dynamical and relaxation timescales.« less

A Random Walk Approach to Query Informative Constraints for Clustering.

PubMed

Abin, Ahmad Ali

2017-08-09

This paper presents a random walk approach to the problem of querying informative constraints for clustering. The proposed method is based on the properties of the commute time, that is the expected time taken for a random walk to travel between two nodes and return, on the adjacency graph of data. Commute time has the nice property of that, the more short paths connect two given nodes in a graph, the more similar those nodes are. Since computing the commute time takes the Laplacian eigenspectrum into account, we use this property in a recursive fashion to query informative constraints for clustering. At each recursion, the proposed method constructs the adjacency graph of data and utilizes the spectral properties of the commute time matrix to bipartition the adjacency graph. Thereafter, the proposed method benefits from the commute times distance on graph to query informative constraints between partitions. This process iterates for each partition until the stop condition becomes true. Experiments on real-world data show the efficiency of the proposed method for constraints selection.

Evaluation of Model Specification, Variable Selection, and Adjustment Methods in Relation to Propensity Scores and Prognostic Scores in Multilevel Data

ERIC Educational Resources Information Center

Yu, Bing; Hong, Guanglei

2012-01-01

This study uses simulation examples representing three types of treatment assignment mechanisms in data generation (the random intercept and slopes setting, the random intercept setting, and a third setting with a cluster-level treatment and an individual-level outcome) in order to determine optimal procedures for reducing bias and improving…

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

NASA Astrophysics Data System (ADS)

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.

A critical analysis of high-redshift, massive, galaxy clusters. Part I

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoyle, Ben; Jimenez, Raul; Verde, Licia

2012-02-01

We critically investigate current statistical tests applied to high redshift clusters of galaxies in order to test the standard cosmological model and describe their range of validity. We carefully compare a sample of high-redshift, massive, galaxy clusters with realistic Poisson sample simulations of the theoretical mass function, which include the effect of Eddington bias. We compare the observations and simulations using the following statistical tests: the distributions of ensemble and individual existence probabilities (in the > M, > z sense), the redshift distributions, and the 2d Kolmogorov-Smirnov test. Using seemingly rare clusters from Hoyle et al. (2011), and Jee etmore » al. (2011) and assuming the same survey geometry as in Jee et al. (2011, which is less conservative than Hoyle et al. 2011), we find that the ( > M, > z) existence probabilities of all clusters are fully consistent with ΛCDM. However assuming the same survey geometry, we use the 2d K-S test probability to show that the observed clusters are not consistent with being the least probable clusters from simulations at > 95% confidence, and are also not consistent with being a random selection of clusters, which may be caused by the non-trivial selection function and survey geometry. Tension can be removed if we examine only a X-ray selected sub sample, with simulations performed assuming a modified survey geometry.« less

Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.

PubMed

NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel

2017-08-01

Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.

Extending cluster Lot Quality Assurance Sampling designs for surveillance programs

PubMed Central

Hund, Lauren; Pagano, Marcello

2014-01-01

Lot quality assurance sampling (LQAS) has a long history of applications in industrial quality control. LQAS is frequently used for rapid surveillance in global health settings, with areas classified as poor or acceptable performance based on the binary classification of an indicator. Historically, LQAS surveys have relied on simple random samples from the population; however, implementing two-stage cluster designs for surveillance sampling is often more cost-effective than simple random sampling. By applying survey sampling results to the binary classification procedure, we develop a simple and flexible non-parametric procedure to incorporate clustering effects into the LQAS sample design to appropriately inflate the sample size, accommodating finite numbers of clusters in the population when relevant. We use this framework to then discuss principled selection of survey design parameters in longitudinal surveillance programs. We apply this framework to design surveys to detect rises in malnutrition prevalence in nutrition surveillance programs in Kenya and South Sudan, accounting for clustering within villages. By combining historical information with data from previous surveys, we design surveys to detect spikes in the childhood malnutrition rate. PMID:24633656

Extending cluster lot quality assurance sampling designs for surveillance programs.

PubMed

Hund, Lauren; Pagano, Marcello

2014-07-20

Lot quality assurance sampling (LQAS) has a long history of applications in industrial quality control. LQAS is frequently used for rapid surveillance in global health settings, with areas classified as poor or acceptable performance on the basis of the binary classification of an indicator. Historically, LQAS surveys have relied on simple random samples from the population; however, implementing two-stage cluster designs for surveillance sampling is often more cost-effective than simple random sampling. By applying survey sampling results to the binary classification procedure, we develop a simple and flexible nonparametric procedure to incorporate clustering effects into the LQAS sample design to appropriately inflate the sample size, accommodating finite numbers of clusters in the population when relevant. We use this framework to then discuss principled selection of survey design parameters in longitudinal surveillance programs. We apply this framework to design surveys to detect rises in malnutrition prevalence in nutrition surveillance programs in Kenya and South Sudan, accounting for clustering within villages. By combining historical information with data from previous surveys, we design surveys to detect spikes in the childhood malnutrition rate. Copyright © 2014 John Wiley & Sons, Ltd.

How Much Videos Win over Audios in Listening Instruction for EFL Learners

ERIC Educational Resources Information Center

Yasin, Burhanuddin; Mustafa, Faisal; Permatasari, Rizki

2017-01-01

This study aims at comparing the benefits of using videos instead of audios for improving students' listening skills. This experimental study used a pre-test and post-test control group design. The sample, selected by cluster random sampling resulted in the selection of 32 second year high school students for each group. The instruments used were…

An Energy-Efficient Game-Theory-Based Spectrum Decision Scheme for Cognitive Radio Sensor Networks

PubMed Central

Salim, Shelly; Moh, Sangman

2016-01-01

A cognitive radio sensor network (CRSN) is a wireless sensor network in which sensor nodes are equipped with cognitive radio. In this paper, we propose an energy-efficient game-theory-based spectrum decision (EGSD) scheme for CRSNs to prolong the network lifetime. Note that energy efficiency is the most important design consideration in CRSNs because it determines the network lifetime. The central part of the EGSD scheme consists of two spectrum selection algorithms: random selection and game-theory-based selection. The EGSD scheme also includes a clustering algorithm, spectrum characterization with a Markov chain, and cluster member coordination. Our performance study shows that EGSD outperforms the existing popular framework in terms of network lifetime and coordination overhead. PMID:27376290

An Energy-Efficient Game-Theory-Based Spectrum Decision Scheme for Cognitive Radio Sensor Networks.

PubMed

Salim, Shelly; Moh, Sangman

2016-06-30

A cognitive radio sensor network (CRSN) is a wireless sensor network in which sensor nodes are equipped with cognitive radio. In this paper, we propose an energy-efficient game-theory-based spectrum decision (EGSD) scheme for CRSNs to prolong the network lifetime. Note that energy efficiency is the most important design consideration in CRSNs because it determines the network lifetime. The central part of the EGSD scheme consists of two spectrum selection algorithms: random selection and game-theory-based selection. The EGSD scheme also includes a clustering algorithm, spectrum characterization with a Markov chain, and cluster member coordination. Our performance study shows that EGSD outperforms the existing popular framework in terms of network lifetime and coordination overhead.

Large scale structural optimization of trimetallic Cu-Au-Pt clusters up to 147 atoms

NASA Astrophysics Data System (ADS)

Wu, Genhua; Sun, Yan; Wu, Xia; Chen, Run; Wang, Yan

2017-10-01

The stable structures of Cu-Au-Pt clusters up to 147 atoms are optimized by using an improved adaptive immune optimization algorithm (AIOA-IC method), in which several motifs, such as decahedron, icosahedron, face centered cubic, sixfold pancake, and Leary tetrahedron, are randomly selected as the inner cores of the starting structures. The structures of Cu8AunPt30-n (n = 1-29), Cu8AunPt47-n (n = 1-46), and partial 75-, 79-, 100-, and 147-atom clusters are analyzed. Cu12Au93Pt42 cluster has onion-like Mackay icosahedral motif. The segregation phenomena of Cu, Au and Pt in clusters are explained by the atomic radius, surface energy, and cohesive energy.

Underrecognition of Dengue during 2013 Epidemic in Luanda, Angola

PubMed Central

Moreira, Rosa; Soares, Maria José; Miguel da Costa, Lúis; Mann, Jennifer; DeLorey, Mark; Hunsperger, Elizabeth; Muñoz-Jordán, Jorge L.; Colón, Candimar; Margolis, Harold S.; de Caravalho, Adelaide; Tomashek, Kay M.

2015-01-01

During the 2013 dengue epidemic in Luanda, Angola, 811 dengue rapid diagnostic test–positive cases were reported to the Ministry of Health. To better understand the magnitude of the epidemic and identify risk factors for dengue virus (DENV) infection, we conducted cluster surveys around households of case-patients and randomly selected households 6 weeks after the peak of the epidemic. Of 173 case cluster participants, 16 (9%) exhibited evidence of recent DENV infection. Of 247 random cluster participants, 25 (10%) had evidence of recent DENV infection. Of 13 recently infected participants who had a recent febrile illness, 7 (54%) had sought medical care, and 1 (14%) was hospitalized with symptoms consistent with severe dengue; however, none received a diagnosis of dengue. Behavior associated with protection from DENV infection included recent use of mosquito repellent or a bed net. These findings suggest that the 2013 dengue epidemic was larger than indicated by passive surveillance data. PMID:26196224

Implementation of client versus care-provider strategies to improve external cephalic version rates: a cluster randomized controlled trial.

PubMed

Vlemmix, Floortje; Rosman, Ageeth N; Rijnders, Marlies E; Beuckens, Antje; Opmeer, Brent C; Mol, Ben W J; Kok, Marjolein; Fleuren, Margot A H

2015-05-01

To determine the effectiveness of a client or care-provider strategy to improve the implementation of external cephalic version. Cluster randomized controlled trial. Twenty-five clusters; hospitals and their referring midwifery practices randomly selected in the Netherlands. Singleton breech presentation from 32 weeks of gestation onwards. We randomized clusters to a client strategy (written information leaflets and decision aid), a care-provider strategy (1-day counseling course focused on knowledge and counseling skills), a combined client and care-provider strategy and care-as-usual strategy. We performed an intention-to-treat analysis. Rate of external cephalic version in various strategies. Secondary outcomes were the percentage of women counseled and opting for a version attempt. The overall implementation rate of external cephalic version was 72% (1169 of 1613 eligible clients) with a range between clusters of 8-95%. Neither the client strategy (OR 0.8, 95% CI 0.4-1.5) nor the care-provider strategy (OR 1.2, 95% CI 0.6-2.3) showed significant improvements. Results were comparable when we limited the analysis to those women who were actually offered intervention (OR 0.6, 95% CI 0.3-1.4 and OR 2.0, 95% CI 0.7-4.5). Neither a client nor a care-provider strategy improved the external cephalic version implementation rate for breech presentation, neither with regard to the number of version attempts offered nor the number of women accepting the procedure. © 2015 Nordic Federation of Societies of Obstetrics and Gynecology.

Analysis of the project synthesis goal cluster orientation and inquiry emphasis of elementary science textbooks

NASA Astrophysics Data System (ADS)

Staver, John R.; Bay, Mary

The purpose of this descriptive study was to examine selected units of commonly used elementary science texts, using the Project Synthesis goal clusters as a framework for part of the examination. An inquiry classification scheme was used for the remaining segment. Four questions were answered: (1) To what extent do elementary science textbooks focus on each Project Synthesis goal cluster? (2) In which part of the text is such information found? (3) To what extent are the activities and experiments merely verifications of information already introduced in the text? (4) If inquiry is present in an activity, then what is the level of such inquiry?Eleven science textbook series, which comprise approximately 90 percent of the national market, were selected for analysis. Two units, one primary (K-3) and one intermediate (4-6), were selected for analysis by first identifying units common to most series, then randomly selecting one primary and one intermediate unit for analysis.Each randomly selected unit was carefully read, using the sentence as the unit of analysis. Each declarative and interrogative sentence in the body of the text was classified as: (1) academic; (2) personal; (3) career; or (4) societal in its focus. Each illustration, except those used in evaluation items, was similarly classified. Each activity/experiment and each miscellaneous sentence in end-of-chapter segments labelled review, summary, evaluation, etc., were similarly classified. Finally, each activity/experiment, as a whole, was categorized according to a four-category inquiry scheme (confirmation, structured inquiry, guided inquiry, open inquiry).In general, results of the analysis are: (1) most text prose focuses on academic science; (2) most remaining text prose focuses on the personal goal cluster; (3) the career and societal goal clusters receive only minor attention; (4) text illustrations exhibit a pattern similar to text prose; (5) text activities/experiments are academic in orientation, almost to the exclusion of other goal clusters; (6) end-of-chapter sentences are largely academic; (7) inquiry is absent or present only in limited forms in text activities/experiments; and (8) texts allocate only a minor portion of space to activities/experiments. Detailed findings are given as numeral, percentage, and decimal values. Discussion focuses on the implications of the results and a comparison of NSTA recommendations with the results of this analysis.

Power Calculations for Moderators in Multi-Site Cluster Randomized Trials

ERIC Educational Resources Information Center

Spybrook, Jessaca; Kelcey, Ben; Dong, Nianbo

2016-01-01

Cluster randomized trials (CRTs), or studies in which intact groups of individuals are randomly assigned to a condition, are becoming more common in evaluation studies of educational programs. A specific type of CRT in which clusters are randomly assigned to treatment within blocks or sites, known as multisite cluster randomized trials (MSCRTs),…

Android Malware Classification Using K-Means Clustering Algorithm

NASA Astrophysics Data System (ADS)

Hamid, Isredza Rahmi A.; Syafiqah Khalid, Nur; Azma Abdullah, Nurul; Rahman, Nurul Hidayah Ab; Chai Wen, Chuah

2017-08-01

Malware was designed to gain access or damage a computer system without user notice. Besides, attacker exploits malware to commit crime or fraud. This paper proposed Android malware classification approach based on K-Means clustering algorithm. We evaluate the proposed model in terms of accuracy using machine learning algorithms. Two datasets were selected to demonstrate the practicing of K-Means clustering algorithms that are Virus Total and Malgenome dataset. We classify the Android malware into three clusters which are ransomware, scareware and goodware. Nine features were considered for each types of dataset such as Lock Detected, Text Detected, Text Score, Encryption Detected, Threat, Porn, Law, Copyright and Moneypak. We used IBM SPSS Statistic software for data classification and WEKA tools to evaluate the built cluster. The proposed K-Means clustering algorithm shows promising result with high accuracy when tested using Random Forest algorithm.

Assessing different measures of population-level vaccine protection using a case-control study.

PubMed

Ali, Mohammad; You, Young Ae; Kanungo, Suman; Manna, Byomkesh; Deen, Jacqueline L; Lopez, Anna Lena; Wierzba, Thomas F; Bhattacharya, Sujit K; Sur, Dipika; Clemens, John D

2015-11-27

Case-control studies have not been examined for their utility in assessing population-level vaccine protection in individually randomized trials. We used the data of a randomized, placebo-controlled trial of a cholera vaccine to compare the results of case-control analyses with those of cohort analyses. Cases of cholera were selected from the trial population followed for three years following dosing. For each case, we selected 4 age-matched controls who had not developed cholera. For each case and control, GIS was used to calculate vaccine coverage of individuals in a surrounding "virtual" cluster. Specific selection strategies were used to evaluate the vaccine protective effects. 66,900 out of 108,389 individuals received two doses of the assigned regimen. For direct protection among subjects in low vaccine coverage clusters, we observed 78% (95% CI: 47-91%) protection in a cohort analysis and 84% (95% CI: 60-94%) in case-control analysis after adjusting for confounding factors. Using our GIS-based approach, estimated indirect protection was 52% (95% CI: 10-74%) in cohort and 76% (95% CI: 47-89%) in case control analysis. Estimates of total and overall effectiveness were similar for cohort and case-control analyses. The findings show that case-control analyses of individually randomized vaccine trials may be used to evaluate direct as well as population-level vaccine protection. Copyright © 2015. Published by Elsevier Ltd.

Intra-class correlation estimates for assessment of vitamin A intake in children.

PubMed

Agarwal, Girdhar G; Awasthi, Shally; Walter, Stephen D

2005-03-01

In many community-based surveys, multi-level sampling is inherent in the design. In the design of these studies, especially to calculate the appropriate sample size, investigators need good estimates of intra-class correlation coefficient (ICC), along with the cluster size, to adjust for variation inflation due to clustering at each level. The present study used data on the assessment of clinical vitamin A deficiency and intake of vitamin A-rich food in children in a district in India. For the survey, 16 households were sampled from 200 villages nested within eight randomly-selected blocks of the district. ICCs and components of variances were estimated from a three-level hierarchical random effects analysis of variance model. Estimates of ICCs and variance components were obtained at village and block levels. Between-cluster variation was evident at each level of clustering. In these estimates, ICCs were inversely related to cluster size, but the design effect could be substantial for large clusters. At the block level, most ICC estimates were below 0.07. At the village level, many ICC estimates ranged from 0.014 to 0.45. These estimates may provide useful information for the design of epidemiological studies in which the sampled (or allocated) units range in size from households to large administrative zones.

American Healthy Homes Survey: A National Study of Residential Phthalates Measured from Floor Wipes

EPA Science Inventory

The United States Environmental Protection Agency (U.S. EPA), in collaboration with the U.S. Department of Housing and Urban Development (HUD), conducted a survey measuring phthalates in randomly selected residential homes throughout the U.S. Multistage sampling with clustering w...

Multiple filters affect tree species assembly in mid-latitude forest communities.

PubMed

Kubota, Y; Kusumoto, B; Shiono, T; Ulrich, W

2018-05-01

Species assembly patterns of local communities are shaped by the balance between multiple abiotic/biotic filters and dispersal that both select individuals from species pools at the regional scale. Knowledge regarding functional assembly can provide insight into the relative importance of the deterministic and stochastic processes that shape species assembly. We evaluated the hierarchical roles of the α niche and β niches by analyzing the influence of environmental filtering relative to functional traits on geographical patterns of tree species assembly in mid-latitude forests. Using forest plot datasets, we examined the α niche traits (leaf and wood traits) and β niche properties (cold/drought tolerance) of tree species, and tested non-randomness (clustering/over-dispersion) of trait assembly based on null models that assumed two types of species pools related to biogeographical regions. For most plots, species assembly patterns fell within the range of random expectation. However, particularly for cold/drought tolerance-related β niche properties, deviation from randomness was frequently found; non-random clustering was predominant in higher latitudes with harsh climates. Our findings demonstrate that both randomness and non-randomness in trait assembly emerged as a result of the α and β niches, although we suggest the potential role of dispersal processes and/or species equalization through trait similarities in generating the prevalence of randomness. Clustering of β niche traits along latitudinal climatic gradients provides clear evidence of species sorting by filtering particular traits. Our results reveal that multiple filters through functional niches and stochastic processes jointly shape geographical patterns of species assembly across mid-latitude forests.

CLUMP-3D: Testing ΛCDM with Galaxy Cluster Shapes

NASA Astrophysics Data System (ADS)

Sereno, Mauro; Umetsu, Keiichi; Ettori, Stefano; Sayers, Jack; Chiu, I.-Non; Meneghetti, Massimo; Vega-Ferrero, Jesús; Zitrin, Adi

2018-06-01

The ΛCDM model of structure formation makes strong predictions on the concentration and shape of dark matter (DM) halos, which are determined by mass accretion processes. Comparison between predicted shapes and observations provides a geometric test of the ΛCDM model. Accurate and precise measurements needs a full three-dimensional (3D) analysis of the cluster mass distribution. We accomplish this with a multi-probe 3D analysis of the X-ray regular Cluster Lensing and Supernova survey with Hubble (CLASH) clusters combining strong and weak lensing, X-ray photometry and spectroscopy, and the Sunyaev–Zel’dovich effect (SZe). The cluster shapes and concentrations are consistent with ΛCDM predictions. The CLASH clusters are randomly oriented, as expected given the sample selection criteria. Shapes agree with numerical results for DM-only halos, which hints at baryonic physics being less effective in making halos rounder.

Mathematical Creativity and Mathematical Aptitude: A Cross-Lagged Panel Analysis

ERIC Educational Resources Information Center

Tyagi, Tarun Kumar

2016-01-01

Cross-lagged panel correlation (CLPC) analysis has been used to identify causal relationships between mathematical creativity and mathematical aptitude. For this study, 480 8th standard students were selected through a random cluster technique from 9 intermediate and high schools of Varanasi, India. Mathematical creativity and mathematical…

Phenotypic and Genotypic Characterization of Non-Starter Lactic Acid Bacteria in Mature Cheddar Cheese

PubMed Central

Fitzsimons, N. A.; Cogan, T. M.; Condon, S.; Beresford, T.

1999-01-01

Non-starter lactic acid bacteria were isolated from 14 premium-quality and 3 sensorially defective mature Irish Cheddar cheeses, obtained from six manufacturers. From countable plates of Lactobacillus-selective agar, 20 single isolated colonies were randomly picked per cheese. All 331 viable isolates were biochemically characterized as mesophilic (i.e., group II) Lactobacillus spp. Phenotypically, the isolates comprised 96.4% L. paracasei, 2.1% L. plantarum, 0.3% L. curvatus, 0.3% L. brevis, and 0.9% unidentified species. Randomly amplified polymorphic DNA (RAPD) analysis was used to rapidly identify the dominant strain groups in nine cheeses from three of the factories, and through clustering by the unweighted pair group method with arithmetic averages, an average of seven strains were found per cheese. In general, strains isolated from cheese produced at the same factory clustered together. The majority of isolates associated with premium-quality cheese grouped together and apart from clusters of strains from defective-quality cheese. No correlation was found between the isomer of lactate produced and RAPD profiles, although isolates which did not ferment ribose clustered together. The phenotypic and genotypic methods employed were validated with a selection of 31 type and reference strains of mesophilic Lactobacillus spp. commonly found in Cheddar cheese. RAPD analysis was found to be a useful and rapid method for identifying isolates to the species level. The low homology exhibited between RAPD banding profiles for cheese isolates and collection strains demonstrated the heterogeneity of the L. paracasei complex. PMID:10427029

A Highly Efficient Design Strategy for Regression with Outcome Pooling

PubMed Central

Mitchell, Emily M.; Lyles, Robert H.; Manatunga, Amita K.; Perkins, Neil J.; Schisterman, Enrique F.

2014-01-01

The potential for research involving biospecimens can be hindered by the prohibitive cost of performing laboratory assays on individual samples. To mitigate this cost, strategies such as randomly selecting a portion of specimens for analysis or randomly pooling specimens prior to performing laboratory assays may be employed. These techniques, while effective in reducing cost, are often accompanied by a considerable loss of statistical efficiency. We propose a novel pooling strategy based on the k-means clustering algorithm to reduce laboratory costs while maintaining a high level of statistical efficiency when predictor variables are measured on all subjects, but the outcome of interest is assessed in pools. We perform simulations motivated by the BioCycle study to compare this k-means pooling strategy with current pooling and selection techniques under simple and multiple linear regression models. While all of the methods considered produce unbiased estimates and confidence intervals with appropriate coverage, pooling under k-means clustering provides the most precise estimates, closely approximating results from the full data and losing minimal precision as the total number of pools decreases. The benefits of k-means clustering evident in the simulation study are then applied to an analysis of the BioCycle dataset. In conclusion, when the number of lab tests is limited by budget, pooling specimens based on k-means clustering prior to performing lab assays can be an effective way to save money with minimal information loss in a regression setting. PMID:25220822

A highly efficient design strategy for regression with outcome pooling.

PubMed

Mitchell, Emily M; Lyles, Robert H; Manatunga, Amita K; Perkins, Neil J; Schisterman, Enrique F

2014-12-10

The potential for research involving biospecimens can be hindered by the prohibitive cost of performing laboratory assays on individual samples. To mitigate this cost, strategies such as randomly selecting a portion of specimens for analysis or randomly pooling specimens prior to performing laboratory assays may be employed. These techniques, while effective in reducing cost, are often accompanied by a considerable loss of statistical efficiency. We propose a novel pooling strategy based on the k-means clustering algorithm to reduce laboratory costs while maintaining a high level of statistical efficiency when predictor variables are measured on all subjects, but the outcome of interest is assessed in pools. We perform simulations motivated by the BioCycle study to compare this k-means pooling strategy with current pooling and selection techniques under simple and multiple linear regression models. While all of the methods considered produce unbiased estimates and confidence intervals with appropriate coverage, pooling under k-means clustering provides the most precise estimates, closely approximating results from the full data and losing minimal precision as the total number of pools decreases. The benefits of k-means clustering evident in the simulation study are then applied to an analysis of the BioCycle dataset. In conclusion, when the number of lab tests is limited by budget, pooling specimens based on k-means clustering prior to performing lab assays can be an effective way to save money with minimal information loss in a regression setting. Copyright © 2014 John Wiley & Sons, Ltd.

Epidemiological characteristics of reported sporadic and outbreak cases of E. coli O157 in people from Alberta, Canada (2000-2002): methodological challenges of comparing clustered to unclustered data.

PubMed

Pearl, D L; Louie, M; Chui, L; Doré, K; Grimsrud, K M; Martin, S W; Michel, P; Svenson, L W; McEwen, S A

2008-04-01

Using multivariable models, we compared whether there were significant differences between reported outbreak and sporadic cases in terms of their sex, age, and mode and site of disease transmission. We also determined the potential role of administrative, temporal, and spatial factors within these models. We compared a variety of approaches to account for clustering of cases in outbreaks including weighted logistic regression, random effects models, general estimating equations, robust variance estimates, and the random selection of one case from each outbreak. Age and mode of transmission were the only epidemiologically and statistically significant covariates in our final models using the above approaches. Weighing observations in a logistic regression model by the inverse of their outbreak size appeared to be a relatively robust and valid means for modelling these data. Some analytical techniques, designed to account for clustering, had difficulty converging or producing realistic measures of association.

Point process statistics in atom probe tomography.

PubMed

Philippe, T; Duguay, S; Grancher, G; Blavette, D

2013-09-01

We present a review of spatial point processes as statistical models that we have designed for the analysis and treatment of atom probe tomography (APT) data. As a major advantage, these methods do not require sampling. The mean distance to nearest neighbour is an attractive approach to exhibit a non-random atomic distribution. A χ(2) test based on distance distributions to nearest neighbour has been developed to detect deviation from randomness. Best-fit methods based on first nearest neighbour distance (1 NN method) and pair correlation function are presented and compared to assess the chemical composition of tiny clusters. Delaunay tessellation for cluster selection has been also illustrated. These statistical tools have been applied to APT experiments on microelectronics materials. Copyright © 2012 Elsevier B.V. All rights reserved.

Cluster randomised trials in the medical literature: two bibliometric surveys

PubMed Central

Bland, J Martin

2004-01-01

Background Several reviews of published cluster randomised trials have reported that about half did not take clustering into account in the analysis, which was thus incorrect and potentially misleading. In this paper I ask whether cluster randomised trials are increasing in both number and quality of reporting. Methods Computer search for papers on cluster randomised trials since 1980, hand search of trial reports published in selected volumes of the British Medical Journal over 20 years. Results There has been a large increase in the numbers of methodological papers and of trial reports using the term 'cluster random' in recent years, with about equal numbers of each type of paper. The British Medical Journal contained more such reports than any other journal. In this journal there was a corresponding increase over time in the number of trials where subjects were randomised in clusters. In 2003 all reports showed awareness of the need to allow for clustering in the analysis. In 1993 and before clustering was ignored in most such trials. Conclusion Cluster trials are becoming more frequent and reporting is of higher quality. Perhaps statistician pressure works. PMID:15310402

Prevention of EP Migratory Contamination in a Cluster Randomized Trial to Increase tPA Use in Stroke (The INSTINCT Trial)

PubMed Central

Weston, Victoria C.; Meurer, William J.; Frederiksen, Shirley M.; Fox, Allison K.; Scott, Phillip A.

2016-01-01

Objectives Cluster randomized trials (CRTs) are increasingly utilized to evaluate quality improvement interventions aimed at healthcare providers. In trials testing emergency department interventions, migration of emergency physicians (EPs) between hospitals is an important concern, as contamination may affect both internal and external validity. We hypothesized that geographically isolating emergency departments would prevent migratory contamination in a CRT designed to increase ED delivery of tPA in stroke (The INSTINCT Trial). Methods INSTINCT was a prospective, cluster randomized, controlled trial. 24 Michigan community hospitals were randomly selected in matched pairs for study. Contamination was defined at the cluster level, with substantial contamination defined a priori as >10% of EPs affected. Non-adherence, total crossover (contamination + non-adherence), migration distance and characteristics were determined. Results 307 emergency physicians were identified at all sites. Overall, 7 (2.3%) changed study sites. 1 moved between control sites, leaving 6 (2.0%) total crossovers. Of these, 2 (0.7%) moved from intervention to control (contamination) and 4 (1.3%) moved from control to intervention (non-adherence). Contamination was observed in 2 of 12 control sites, with 17% and 9% contamination of the total site EP workforce at follow-up, respectively. Average migration distance was 42 miles for all EPs moving in the study and 35 miles for EPs moving from intervention to control sites. Conclusion The mobile nature of emergency physicians should be considered in the design of quality improvement CRTs. Increased reporting of contamination in CRTs is encouraged to clarify thresholds and facilitate CRT design. PMID:25440230

Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattle.

PubMed

Campos, G S; Reimann, F A; Cardoso, L L; Ferreira, C E R; Junqueira, V S; Schmidt, P I; Braccini Neto, J; Yokoo, M J I; Sollero, B P; Boligon, A A; Cardoso, F F

2018-05-07

The objective of the present study was to evaluate the accuracy and bias of direct and blended genomic predictions using different methods and cross-validation techniques for growth traits (weight and weight gains) and visual scores (conformation, precocity, muscling and size) obtained at weaning and at yearling in Hereford and Braford breeds. Phenotypic data contained 126,290 animals belonging to the Delta G Connection genetic improvement program, and a set of 3,545 animals genotyped with the 50K chip and 131 sires with the 777K. After quality control, 41,045 markers remained for all animals. An animal model was used to estimate (co)variances components and to predict breeding values, which were later used to calculate the deregressed estimated breeding values (DEBV). Animals with genotype and phenotype for the traits studied were divided into four or five groups by random and k-means clustering cross-validation strategies. The values of accuracy of the direct genomic values (DGV) were moderate to high magnitude for at weaning and at yearling traits, ranging from 0.19 to 0.45 for the k-means and 0.23 to 0.78 for random clustering among all traits. The greatest gain in relation to the pedigree BLUP (PBLUP) was 9.5% with the BayesB method with both the k-means and the random clustering. Blended genomic value accuracies ranged from 0.19 to 0.56 for k-means and from 0.21 to 0.82 for random clustering. The analyzes using the historical pedigree and phenotypes contributed additional information to calculate the GEBV and in general, the largest gains were for the single-step (ssGBLUP) method in bivariate analyses with a mean increase of 43.00% among all traits measured at weaning and of 46.27% for those evaluated at yearling. The accuracy values for the marker effects estimation methods were lower for k-means clustering, indicating that the training set relationship to the selection candidates is a major factor affecting accuracy of genomic predictions. The gains in accuracy obtained with genomic blending methods, mainly ssGBLUP in bivariate analyses, indicate that genomic predictions should be used as a tool to improve genetic gains in relation to the traditional PBLUP selection.

Application of k-means clustering algorithm in grouping the DNA sequences of hepatitis B virus (HBV)

NASA Astrophysics Data System (ADS)

Bustamam, A.; Tasman, H.; Yuniarti, N.; Frisca, Mursidah, I.

2017-07-01

Based on WHO data, an estimated of 15 millions people worldwide who are infected with hepatitis B (HBsAg+), which is caused by HBV virus, are also infected by hepatitis D, which is caused by HDV virus. Hepatitis D infection can occur simultaneously with hepatitis B (co infection) or after a person is exposed to chronic hepatitis B (super infection). Since HDV cannot live without HBV, HDV infection is closely related to HBV infection, hence it is very realistic that every effort of prevention against hepatitis B can indirectly prevent hepatitis D. This paper presents clustering of HBV DNA sequences by using k-means clustering algorithm and R programming. Clustering processes are started with collecting HBV DNA sequences from GenBank, then performing extraction HBV DNA sequences using n-mers frequency and furthermore the extraction results are collected as a matrix and normalized using the min-max normalization with interval [0, 1] which will later be used as an input data. The number of clusters is two and the initial centroid selected of the cluster is chosen randomly. In each iteration, the distance of every object to each centroid are calculated using the Euclidean distance and the minimum distance is selected to determine the membership in a cluster until two convergent clusters are created. As the result, the HBV viruses in the first cluster is more virulent than the HBV viruses in the second cluster, so the HBV viruses in the first cluster can potentially evolve with HDV viruses that cause hepatitis D.

CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests.

PubMed

Ma, Li; Fan, Suohai

2017-03-14

The random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization. We propose the CURE-SMOTE algorithm for the imbalanced data classification problem. Experiments on imbalanced UCI data reveal that the combination of Clustering Using Representatives (CURE) enhances the original synthetic minority oversampling technique (SMOTE) algorithms effectively compared with the classification results on the original data using random sampling, Borderline-SMOTE1, safe-level SMOTE, C-SMOTE, and k-means-SMOTE. Additionally, the hybrid RF (random forests) algorithm has been proposed for feature selection and parameter optimization, which uses the minimum out of bag (OOB) data error as its objective function. Simulation results on binary and higher-dimensional data indicate that the proposed hybrid RF algorithms, hybrid genetic-random forests algorithm, hybrid particle swarm-random forests algorithm and hybrid fish swarm-random forests algorithm can achieve the minimum OOB error and show the best generalization ability. The training set produced from the proposed CURE-SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm. Moreover, the hybrid algorithm's F-value, G-mean, AUC and OOB scores demonstrate that they surpass the performance of the original RF algorithm. Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization.

Decentralized cooperative TOA/AOA target tracking for hierarchical wireless sensor networks.

PubMed

Chen, Ying-Chih; Wen, Chih-Yu

2012-11-08

This paper proposes a distributed method for cooperative target tracking in hierarchical wireless sensor networks. The concept of leader-based information processing is conducted to achieve object positioning, considering a cluster-based network topology. Random timers and local information are applied to adaptively select a sub-cluster for the localization task. The proposed energy-efficient tracking algorithm allows each sub-cluster member to locally estimate the target position with a Bayesian filtering framework and a neural networking model, and further performs estimation fusion in the leader node with the covariance intersection algorithm. This paper evaluates the merits and trade-offs of the protocol design towards developing more efficient and practical algorithms for object position estimation.

The Atacama Cosmology Telescope: Relation Between Galaxy Cluster Optical Richness and Sunyaev-Zel'dovich Effect

NASA Technical Reports Server (NTRS)

Sehgal, Neelima; Addison, Graeme; Battaglia, Nick; Battistelli, Elia S.; Bond, J. Richard; Das, Sudeep; Devlin, Mark J.; Dunkley, Joanna; Duenner, Rolando; Gralla, Megan;

Pattern Selection and Super-Patterns in Opinion Dynamics

NASA Astrophysics Data System (ADS)

Ben-Naim, Eli; Scheel, Arnd

We study pattern formation in the bounded confidence model of opinion dynamics. In this random process, opinion is quantified by a single variable. Two agents may interact and reach a fair compromise, but only if their difference of opinion falls below a fixed threshold. Starting from a uniform distribution of opinions with compact support, a traveling wave forms and it propagates from the domain boundary into the unstable uniform state. Consequently, the system reaches a steady state with isolated clusters that are separated by distance larger than the interaction range. These clusters form a quasi-periodic pattern where the sizes of the clusters and the separations between them are nearly constant. We obtain analytically the average separation between clusters L. Interestingly, there are also very small quasi-periodic modulations in the size of the clusters. The spatial periods of these modulations are a series of integers that follow from the continued-fraction representation of the irrational average separation L.

The Effectiveness of Educational Interventions to Enhance the Adoption of Fee-Based Arsenic Testing in Bangladesh: A Cluster Randomized Controlled Trial

PubMed Central

George, Christine Marie; Inauen, Jennifer; Rahman, Sheikh Masudur; Zheng, Yan

2013-01-01

Arsenic (As) testing could help 22 million people, using drinking water sources that exceed the Bangladesh As standard, to identify safe sources. A cluster randomized controlled trial was conducted to evaluate the effectiveness of household education and local media in the increasing demand for fee-based As testing. Randomly selected households (N = 452) were divided into three interventions implemented by community workers: 1) fee-based As testing with household education (HE); 2) fee-based As testing with household education and a local media campaign (HELM); and 3) fee-based As testing alone (Control). The fee for the As test was US$ 0.28, higher than the cost of the test (US$ 0.16). Of households with untested wells, 93% in both intervention groups HE and HELM purchased an As test, whereas only 53% in the control group. In conclusion, fee-based As testing with household education is effective in the increasing demand for As testing in rural Bangladesh. PMID:23716409

The effectiveness of educational interventions to enhance the adoption of fee-based arsenic testing in Bangladesh: a cluster randomized controlled trial.

PubMed

George, Christine Marie; Inauen, Jennifer; Rahman, Sheikh Masudur; Zheng, Yan

2013-07-01

Arsenic (As) testing could help 22 million people, using drinking water sources that exceed the Bangladesh As standard, to identify safe sources. A cluster randomized controlled trial was conducted to evaluate the effectiveness of household education and local media in the increasing demand for fee-based As testing. Randomly selected households (N = 452) were divided into three interventions implemented by community workers: 1) fee-based As testing with household education (HE); 2) fee-based As testing with household education and a local media campaign (HELM); and 3) fee-based As testing alone (Control). The fee for the As test was US$ 0.28, higher than the cost of the test (US$ 0.16). Of households with untested wells, 93% in both intervention groups HE and HELM purchased an As test, whereas only 53% in the control group. In conclusion, fee-based As testing with household education is effective in the increasing demand for As testing in rural Bangladesh.

The Impact of a School-Based Hygiene, Water Quality and Sanitation Intervention on Soil-Transmitted Helminth Reinfection: A Cluster-Randomized Trial

PubMed Central

Freeman, Matthew C.; Clasen, Thomas; Brooker, Simon J.; Akoko, Daniel O.; Rheingans, Richard

2013-01-01

We conducted a cluster-randomized trial to assess the impact of a school-based water treatment, hygiene, and sanitation program on reducing infection with soil-transmitted helminths (STHs) after school-based deworming. We assessed infection with STHs at baseline and then at two follow-up rounds 8 and 10 months after deworming. Forty government primary schools in Nyanza Province, Kenya were randomly selected and assigned to intervention or control arms. The intervention reduced reinfection prevalence (odds ratio [OR] 0.56, 95% confidence interval [CI] 0.31–1.00) and egg count (rate ratio [RR] 0.34, CI 0.15–0.75) of Ascaris lumbricoides. We found no evidence of significant intervention effects on the overall prevalence and intensity of Trichuris trichiura, hookworm, or Schistosoma mansoni reinfection. Provision of school-based sanitation, water quality, and hygiene improvements may reduce reinfection of STHs after school-based deworming, but the magnitude of the effects may be sex- and helminth species-specific. PMID:24019429

Proposed variations of the stepped-wedge design can be used to accommodate multiple interventions.

PubMed

Lyons, Vivian H; Li, Lingyu; Hughes, James P; Rowhani-Rahbar, Ali

2017-06-01

Stepped-wedge design (SWD) cluster-randomized trials have traditionally been used for evaluating a single intervention. We aimed to explore design variants suitable for evaluating multiple interventions in an SWD trial. We identified four specific variants of the traditional SWD that would allow two interventions to be conducted within a single cluster-randomized trial: concurrent, replacement, supplementation, and factorial SWDs. These variants were chosen to flexibly accommodate study characteristics that limit a one-size-fits-all approach for multiple interventions. In the concurrent SWD, each cluster receives only one intervention, unlike the other variants. The replacement SWD supports two interventions that will not or cannot be used at the same time. The supplementation SWD is appropriate when the second intervention requires the presence of the first intervention, and the factorial SWD supports the evaluation of intervention interactions. The precision for estimating intervention effects varies across the four variants. Selection of the appropriate design variant should be driven by the research question while considering the trade-off between the number of steps, number of clusters, restrictions for concurrent implementation of the interventions, lingering effects of each intervention, and precision of the intervention effect estimates. Copyright © 2017 Elsevier Inc. All rights reserved.

SU-G-TeP3-14: Three-Dimensional Cluster Model in Inhomogeneous Dose Distribution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei, J; Penagaricano, J; Narayanasamy, G

2016-06-15

Purpose: We aim to investigate 3D cluster formation in inhomogeneous dose distribution to search for new models predicting radiation tissue damage and further leading to new optimization paradigm for radiotherapy planning. Methods: The aggregation of higher dose in the organ at risk (OAR) than a preset threshold was chosen as the cluster whose connectivity dictates the cluster structure. Upon the selection of the dose threshold, the fractional density defined as the fraction of voxels in the organ eligible to be part of the cluster was determined according to the dose volume histogram (DVH). A Monte Carlo method was implemented tomore » establish a case pertinent to the corresponding DVH. Ones and zeros were randomly assigned to each OAR voxel with the sampling probability equal to the fractional density. Ten thousand samples were randomly generated to ensure a sufficient number of cluster sets. A recursive cluster searching algorithm was developed to analyze the cluster with various connectivity choices like 1-, 2-, and 3-connectivity. The mean size of the largest cluster (MSLC) from the Monte Carlo samples was taken to be a function of the fractional density. Various OARs from clinical plans were included in the study. Results: Intensive Monte Carlo study demonstrates the inverse relationship between the MSLC and the cluster connectivity as anticipated and the cluster size does not change with fractional density linearly regardless of the connectivity types. An initially-slow-increase to exponential growth transition of the MSLC from low to high density was observed. The cluster sizes were found to vary within a large range and are relatively independent of the OARs. Conclusion: The Monte Carlo study revealed that the cluster size could serve as a suitable index of the tissue damage (percolation cluster) and the clinical outcome of the same DVH might be potentially different.« less

Differential effect of exposure-based therapy and cognitive therapy on post-traumatic stress disorder symptom clusters: A randomized controlled trial.

PubMed

Horesh, Danny; Qian, Meng; Freedman, Sara; Shalev, Arieh

2017-06-01

A question remains regarding differential effects of exposure-based versus non-exposure-based therapies on specific post-traumatic stress disorder (PTSD) symptom clusters. Traumatized emergency room patients were randomized to receive prolonged exposure (PE) or cognitive therapy (CT) without exposure. PE/CT had no differential effect on individual symptom clusters, and change in total PTSD score remained significant even after controlling for the reductions in all three symptom clusters. In addition, baseline levels of PTSD avoidance/intrusion/hyperarousal did not moderate the effects of PE and CT on total PTSD symptom scores. Taken together, these findings challenge the notion that PE and CT are specifically, and differentially, useful in treating one particular PTSD symptom cluster. Despite their different theoretical backgrounds and techniques, the notion that PE and CT (without exposure) target different PTSD symptoms was not confirmed in this study. Thus, both interventions may in fact be equally effective for treating intrusion, avoidance and hyperarousal symptoms. Baseline levels of avoidance, intrusion and hyperarousal may not be good a priori indicators for PTSD treatment selection. The effect of PE and CT on PTSD as a whole does not seem to depend on a reduction in any specific symptom cluster. These findings indicate that exposure and non-exposure interventions may lead to similar results in terms of reductions in specific PTSD symptoms. It is quite possible that individual PTSD clusters may respond to therapy in an inter-related fashion, with one cluster affecting the other. © 2016 The British Psychological Society.

Impact of community-based interventions on maternal and neonatal health indicators: Results from a community randomized trial in rural Balochistan, Pakistan.

PubMed

Midhet, Farid; Becker, Stan

2010-11-05

Pakistan has high maternal mortality, particularly in the rural areas. The delay in decision making to seek medical care during obstetric emergencies remains a significant factor in maternal mortality. We present results from an experimental study in rural Pakistan. Village clusters were randomly assigned to intervention and control arms (16 clusters each). In the intervention clusters, women were provided information on safe motherhood through pictorial booklets and audiocassettes; traditional birth attendants were trained in clean delivery and recognition of obstetric and newborn complications; and emergency transportation systems were set up. In eight of the 16 intervention clusters, husbands also received specially designed education materials on safe motherhood and family planning. Pre- and post-intervention surveys on selected maternal and neonatal health indicators were conducted in all 32 clusters. A district-wide survey was conducted two years after project completion to measure any residual impact of the interventions. Pregnant women in intervention clusters received prenatal care and prophylactic iron therapy more frequently than pregnant women in control clusters. Providing safe motherhood education to husbands resulted in further improvement of some indicators. There was a small but significant increase in percent of hospital deliveries but no impact on the use of skilled birth attendants. Perinatal mortality reduced significantly in clusters where only wives received information and education in safe motherhood. The survey to assess residual impact showed similar results. We conclude that providing safe motherhood education increased the probability of pregnant women having prenatal care and utilization of health services for obstetric complications.

Line-of-sight structure toward strong lensing galaxy clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bayliss, Matthew B.; Johnson, Traci; Sharon, Keren

2014-03-01

We present an analysis of the line-of-sight structure toward a sample of 10 strong lensing cluster cores. Structure is traced by groups that are identified spectroscopically in the redshift range, 0.1 ≤ z ≤ 0.9, and we measure the projected angular and comoving separations between each group and the primary strong lensing clusters in each corresponding line of sight. From these data we measure the distribution of projected angular separations between the primary strong lensing clusters and uncorrelated large-scale structure as traced by groups. We then compare the observed distribution of angular separations for our strong lensing selected lines ofmore » sight against the distribution of groups that is predicted for clusters lying along random lines of sight. There is clear evidence for an excess of structure along the line of sight at small angular separations (θ ≤ 6') along the strong lensing selected lines of sight, indicating that uncorrelated structure is a significant systematic that contributes to producing galaxy clusters with large cross sections for strong lensing. The prevalence of line-of-sight structure is one of several biases in strong lensing clusters that can potentially be folded into cosmological measurements using galaxy cluster samples. These results also have implications for current and future studies—such as the Hubble Space Telescope Frontier Fields—that make use of massive galaxy cluster lenses as precision cosmological telescopes; it is essential that the contribution of line-of-sight structure be carefully accounted for in the strong lens modeling of the cluster lenses.« less

Variation in the fumonisin biosynthetic gene cluster in fumonisin-producing and nonproducing black aspergilli.

PubMed

Susca, Antonia; Proctor, Robert H; Butchko, Robert A E; Haidukowski, Miriam; Stea, Gaetano; Logrieco, Antonio; Moretti, Antonio

2014-12-01

The ability to produce fumonisin mycotoxins varies among members of the black aspergilli. Previously, analyses of selected genes in the fumonisin biosynthetic gene (fum) cluster in black aspergilli from California grapes indicated that fumonisin-nonproducing isolates of Aspergillus welwitschiae lack six fum genes, but nonproducing isolates of Aspergillus niger do not. In the current study, analyses of black aspergilli from grapes from the Mediterranean Basin indicate that the genomic context of the fum cluster is the same in isolates of A. niger and A. welwitschiae regardless of fumonisin-production ability and that full-length clusters occur in producing isolates of both species and nonproducing isolates of A. niger. In contrast, the cluster has undergone an eight-gene deletion in fumonisin-nonproducing isolates of A. welwitschiae. Phylogenetic analyses suggest each species consists of a mixed population of fumonisin-producing and nonproducing individuals, and that existence of both production phenotypes may provide a selective advantage to these species. Differences in gene content of fum cluster homologues and phylogenetic relationships of fum genes suggest that the mutation(s) responsible for the nonproduction phenotype differs, and therefore arose independently, in the two species. Partial fum cluster homologues were also identified in genome sequences of four other black Aspergillus species. Gene content of these partial clusters and phylogenetic relationships of fum sequences indicate that non-random partial deletion of the cluster has occurred multiple times among the species. This in turn suggests that an intact cluster and fumonisin production were once more widespread among black aspergilli. Copyright © 2014 Elsevier Inc. All rights reserved.

Informing resource-poor populations and the delivery of entitled health and social services in rural India: a cluster randomized controlled trial.

PubMed

Pandey, Priyanka; Sehgal, Ashwini R; Riboud, Michelle; Levine, David; Goyal, Madhav

2007-10-24

A lack of awareness about entitled health and social services may contribute to poor delivery of such services in developing countries, especially among individuals of low socioeconomic status. To determine the impact of informing resource-poor rural populations about entitled services. Community-based, cluster randomized controlled trial conducted from May 2004 to May 2005 in 105 randomly selected village clusters in Uttar Pradesh state in India. Households (548 intervention and 497 control) were selected by a systematic sampling design, including both low-caste and mid- to high-caste households. Four to 6 public meetings were held in each intervention village cluster to disseminate information on entitled health services, entitled education services, and village governance requirements. No intervention took place in control village clusters. Visits by nurse midwife; prenatal examinations, tetanus vaccinations, and prenatal supplements received by pregnant women; vaccinations received by infants; excess school fees charged; occurrence of village council meetings; and development work in villages. At baseline, there were no significant differences in self-reported delivery of health and social services. After 1 year, intervention villagers reported better delivery of several services compared with control villagers: in a multivariate analysis, 30% more prenatal examinations (95% confidence interval [CI], 17%-43%; P < .001), 27% more tetanus vaccinations (95% CI, 12%-41%; P < .001), 24% more prenatal supplements (95% CI, 8%-39%; P = .003), 25% more infant vaccinations (95% CI, 8%-42%; P = .004), and decreased excess school fees of 8 rupees (95% CI, 4-13 rupees; P < .001). In a difference-in-differences analysis, 21% more village council meetings were reported (95% CI, 5%-36%; P = .01). There were no improvements in visits by a nurse midwife or in development work in the villages. Both low-caste and mid- to high-caste intervention households reported significant improvements in service delivery. Informing resource-poor rural populations in India about entitled services enhanced the delivery of health and social services among both low- and mid- to high-caste households. Interventions that emphasize educating resource-poor populations about entitled services may improve the delivery of such services. clinicaltrials.gov Identifier: NCT00421291.

Can community midwives prevent antenatal depression? An external pilot study to test the feasibility of a cluster randomized controlled universal prevention trial.

PubMed

Brugha, T S; Smith, J; Austin, J; Bankart, J; Patterson, M; Lovett, C; Morgan, Z; Morrell, C J; Slade, P

2016-01-01

Repeated epidemiological surveys show no decline in depression although uptake of treatments has grown. Universal depression prevention interventions are effective in schools but untested rigorously in adulthood. Selective prevention programmes have poor uptake. Universal interventions may be more acceptable during routine healthcare contacts for example antenatally. One study within routine postnatal healthcare suggested risk of postnatal depression could be reduced in non-depressed women from 11% to 8% by giving health visitors psychological intervention training. Feasibility and effectiveness in other settings, most notably antenatally, is unknown. We conducted an external pilot study using a cluster trial design consisting of recruitment and enhanced psychological training of randomly selected clusters of community midwives (CMWs), recruitment of pregnant women of all levels of risk of depression, collection of baseline and outcome data prior to childbirth, allowing time for women 'at increased risk' to complete CMW-provided psychological support sessions. Seventy-nine percent of eligible women approached agreed to take part. Two hundred and ninety-eight women in eight clusters participated and 186 termed 'at low risk' for depression, based on an Edinburgh Perinatal Depression Scale (EPDS) score of <12 at 12 weeks gestation, provided baseline and outcome data at 34 weeks gestation. All trial protocol procedures were shown to be feasible. Antenatal effect sizes in women 'at low risk' were similar to those previously demonstrated postnatally. Qualitative work confirmed the acceptability of the approach to CMWs and intervention group women. A fully powered trial testing universal prevention of depression in pregnancy is feasible, acceptable and worth undertaking.

Discovery of an H I-rich Gas Reservoir in the Outskirts of SZ-effect-selected Clusters

NASA Astrophysics Data System (ADS)

Muzahid, Sowgat; Charlton, Jane; Nagai, Daisuke; Schaye, Joop; Srianand, Raghunathan

2017-09-01

We report on the detection of three strong H I absorbers originating in the outskirts (I.e., impact parameter, {ρ }{cl} ≈ (1.6-4.7)r 500) of three massive ({M}500˜ 3× {10}14 M ⊙) clusters of galaxies at redshift {z}{cl}≈ 0.46, in the Hubble Space Telescope Cosmic Origins Spectrograph (HST/COS) spectra of three background UV-bright quasars. These clusters were discovered by the 2500 deg2 South Pole Telescope Sunyaev-Zel’dovich (SZ) effect survey. All three COS spectra show a partial Lyman limit absorber with N(H I) > 1016.5 cm-2 near the photometric redshifts (| {{Δ }}z/(1+z)| ≈ 0.03) of the clusters. The compound probability of the random occurrence of all three absorbers is <0.02%, indicating that the absorbers are most likely related to the targeted clusters. We find that the outskirts of these SZ-selected clusters are remarkably rich in cool gas compared to existing observations of other clusters in the literature. The effective Doppler parameters of the Lyman series lines, obtained using a single-cloud curve-of-growth (COG) analysis, suggest a nonthermal/turbulent velocity of a few×10 km s-1 in the absorbing gas. We emphasize the need for uniform galaxy surveys around these fields and for more UV observations of quasar-cluster pairs in general in order to improve the statistics and gain further insights into the unexplored territory of the largest collapsed cosmic structures.

Baseline adjustments for binary data in repeated cross-sectional cluster randomized trials.

PubMed

Nixon, R M; Thompson, S G

2003-09-15

Analysis of covariance models, which adjust for a baseline covariate, are often used to compare treatment groups in a controlled trial in which individuals are randomized. Such analysis adjusts for any baseline imbalance and usually increases the precision of the treatment effect estimate. We assess the value of such adjustments in the context of a cluster randomized trial with repeated cross-sectional design and a binary outcome. In such a design, a new sample of individuals is taken from the clusters at each measurement occasion, so that baseline adjustment has to be at the cluster level. Logistic regression models are used to analyse the data, with cluster level random effects to allow for different outcome probabilities in each cluster. We compare the estimated treatment effect and its precision in models that incorporate a covariate measuring the cluster level probabilities at baseline and those that do not. In two data sets, taken from a cluster randomized trial in the treatment of menorrhagia, the value of baseline adjustment is only evident when the number of subjects per cluster is large. We assess the generalizability of these findings by undertaking a simulation study, and find that increased precision of the treatment effect requires both large cluster sizes and substantial heterogeneity between clusters at baseline, but baseline imbalance arising by chance in a randomized study can always be effectively adjusted for. Copyright 2003 John Wiley & Sons, Ltd.

Simultaneously selecting appropriate partners for gaming and strategy adaptation to enhance network reciprocity in the prisoner's dilemma

NASA Astrophysics Data System (ADS)

Tanimoto, Jun

2014-01-01

Network reciprocity is one mechanism for adding social viscosity, which leads to cooperative equilibrium in 2 × 2 prisoner's dilemma games. Previous studies have shown that cooperation can be enhanced by using a skewed, rather than a random, selection of partners for either strategy adaptation or the gaming process. Here we show that combining both processes for selecting a gaming partner and an adaptation partner further enhances cooperation, provided that an appropriate selection rule and parameters are adopted. We also show that this combined model significantly enhances cooperation by reducing the degree of activity in the underlying network; we measure the degree of activity with a quantity called effective degree. More precisely, during the initial evolutionary stage in which the global cooperation fraction declines because initially allocated cooperators becoming defectors, the model shows that weak cooperative clusters perish and only a few strong cooperative clusters survive. This finding is the most important key to attaining significant network reciprocity.

Assessing variation in life-history tactics within a population using mixture regression models: a practical guide for evolutionary ecologists.

PubMed

Hamel, Sandra; Yoccoz, Nigel G; Gaillard, Jean-Michel

2017-05-01

Mixed models are now well-established methods in ecology and evolution because they allow accounting for and quantifying within- and between-individual variation. However, the required normal distribution of the random effects can often be violated by the presence of clusters among subjects, which leads to multi-modal distributions. In such cases, using what is known as mixture regression models might offer a more appropriate approach. These models are widely used in psychology, sociology, and medicine to describe the diversity of trajectories occurring within a population over time (e.g. psychological development, growth). In ecology and evolution, however, these models are seldom used even though understanding changes in individual trajectories is an active area of research in life-history studies. Our aim is to demonstrate the value of using mixture models to describe variation in individual life-history tactics within a population, and hence to promote the use of these models by ecologists and evolutionary ecologists. We first ran a set of simulations to determine whether and when a mixture model allows teasing apart latent clustering, and to contrast the precision and accuracy of estimates obtained from mixture models versus mixed models under a wide range of ecological contexts. We then used empirical data from long-term studies of large mammals to illustrate the potential of using mixture models for assessing within-population variation in life-history tactics. Mixture models performed well in most cases, except for variables following a Bernoulli distribution and when sample size was small. The four selection criteria we evaluated [Akaike information criterion (AIC), Bayesian information criterion (BIC), and two bootstrap methods] performed similarly well, selecting the right number of clusters in most ecological situations. We then showed that the normality of random effects implicitly assumed by evolutionary ecologists when using mixed models was often violated in life-history data. Mixed models were quite robust to this violation in the sense that fixed effects were unbiased at the population level. However, fixed effects at the cluster level and random effects were better estimated using mixture models. Our empirical analyses demonstrated that using mixture models facilitates the identification of the diversity of growth and reproductive tactics occurring within a population. Therefore, using this modelling framework allows testing for the presence of clusters and, when clusters occur, provides reliable estimates of fixed and random effects for each cluster of the population. In the presence or expectation of clusters, using mixture models offers a suitable extension of mixed models, particularly when evolutionary ecologists aim at identifying how ecological and evolutionary processes change within a population. Mixture regression models therefore provide a valuable addition to the statistical toolbox of evolutionary ecologists. As these models are complex and have their own limitations, we provide recommendations to guide future users. © 2016 Cambridge Philosophical Society.

Training a whole-book LSTM-based recognizer with an optimal training set

NASA Astrophysics Data System (ADS)

Soheili, Mohammad Reza; Yousefi, Mohammad Reza; Kabir, Ehsanollah; Stricker, Didier

2018-04-01

Despite the recent progress in OCR technologies, whole-book recognition, is still a challenging task, in particular in case of old and historical books, that the unknown font faces or low quality of paper and print contributes to the challenge. Therefore, pre-trained recognizers and generic methods do not usually perform up to required standards, and usually the performance degrades for larger scale recognition tasks, such as of a book. Such reportedly low error-rate methods turn out to require a great deal of manual correction. Generally, such methodologies do not make effective use of concepts such redundancy in whole-book recognition. In this work, we propose to train Long Short Term Memory (LSTM) networks on a minimal training set obtained from the book to be recognized. We show that clustering all the sub-words in the book, and using the sub-word cluster centers as the training set for the LSTM network, we can train models that outperform any identical network that is trained with randomly selected pages of the book. In our experiments, we also show that although the sub-word cluster centers are equivalent to about 8 pages of text for a 101- page book, a LSTM network trained on such a set performs competitively compared to an identical network that is trained on a set of 60 randomly selected pages of the book.

Optical amplification of photothermal therapy with gold nanoparticles and nanoclusters

NASA Astrophysics Data System (ADS)

Khlebtsov, Boris; Zharov, Vladimir; Melnikov, Andrei; Tuchin, Valery; Khlebtsov, Nikolai

2006-10-01

Recently, several groups (Anderson, Halas, Zharov, and their co-workers, 2003; El-Sayed and co-workers, 2006) demonstrated, through pioneering results, the great potential of photothermal (PT) therapy for the selective treatment of cancer cells, bacteria, viruses, and DNA targeted with gold nanospheres, nanoshells, nanorods, and nanosphere clusters. However, the current understanding of the relationship between the nanoparticle/cluster parameters (size, shape, particle/cluster structure, etc) and the efficiency of PT therapy is limited. Here, we report theoretical simulations aimed at finding the optimal single-particle and cluster structures to achieve its maximal absorption, which is crucial for PT therapeutic effects. To characterize the optical amplification in laser-induced thermal effects, we introduce relevant parameters such as the ratio of the absorption cross section to the gold mass of a single-particle structure and absorption amplification, defined as the ratio of cluster absorption to the total absorption of non-interacting particles. We consider the absorption efficiency of single nanoparticles (gold spheres, rods, and silica/gold nanoshells), linear chains, 2D lattice arrays, 3D random volume clusters, and the random aggregated N-particle ensembles on the outer surface of a larger dielectric sphere, which mimic aggregation of nanosphere bioconjugates on or within cancer cells. The cluster particles are bare or biopolymer-coated gold nanospheres. The light absorption of cluster structures is studied by using the generalized multiparticle Mie solution and the T-matrix method. The gold nanoshells with (silica core diameter)/(gold shell thickness) parameters of (50-100)/(3-8) nm and nanorods with minor/major sizes of (15-20)/(50-70) nm are shown to be more efficient PT labels and sensitizers than the equivolume solid single gold spheres. In the case of nanosphere clusters, the interparticle separations and the short linear-chain fragments are the main structural parameters determining the absorption efficiency and its spectral shifting to the red. Although we have not found a noticeable dependence of absorption amplification on the cluster sphere size, 20-40 nm particles are found to be most effective, in accordance with our experimental observations. The long-wavelength absorption efficiency of random clusters increases with the cluster particle number N at small N and reveals a saturation behaviour at N>20.

Advanced analysis of forest fire clustering

NASA Astrophysics Data System (ADS)

Kanevski, Mikhail; Pereira, Mario; Golay, Jean

2017-04-01

Analysis of point pattern clustering is an important topic in spatial statistics and for many applications: biodiversity, epidemiology, natural hazards, geomarketing, etc. There are several fundamental approaches used to quantify spatial data clustering using topological, statistical and fractal measures. In the present research, the recently introduced multi-point Morisita index (mMI) is applied to study the spatial clustering of forest fires in Portugal. The data set consists of more than 30000 fire events covering the time period from 1975 to 2013. The distribution of forest fires is very complex and highly variable in space. mMI is a multi-point extension of the classical two-point Morisita index. In essence, mMI is estimated by covering the region under study by a grid and by computing how many times more likely it is that m points selected at random will be from the same grid cell than it would be in the case of a complete random Poisson process. By changing the number of grid cells (size of the grid cells), mMI characterizes the scaling properties of spatial clustering. From mMI, the data intrinsic dimension (fractal dimension) of the point distribution can be estimated as well. In this study, the mMI of forest fires is compared with the mMI of random patterns (RPs) generated within the validity domain defined as the forest area of Portugal. It turns out that the forest fires are highly clustered inside the validity domain in comparison with the RPs. Moreover, they demonstrate different scaling properties at different spatial scales. The results obtained from the mMI analysis are also compared with those of fractal measures of clustering - box counting and sand box counting approaches. REFERENCES Golay J., Kanevski M., Vega Orozco C., Leuenberger M., 2014: The multipoint Morisita index for the analysis of spatial patterns. Physica A, 406, 191-202. Golay J., Kanevski M. 2015: A new estimator of intrinsic dimension based on the multipoint Morisita index. Pattern Recognition, 48, 4070-4081.

Enhancing local health department disaster response capacity with rapid community needs assessments: validation of a computerized program for binary attribute cluster sampling.

PubMed

Groenewold, Matthew R

2006-01-01

Local health departments are among the first agencies to respond to disasters or other mass emergencies. However, they often lack the ability to handle large-scale events. Plans including locally developed and deployed tools may enhance local response. Simplified cluster sampling methods can be useful in assessing community needs after a sudden-onset, short duration event. Using an adaptation of the methodology used by the World Health Organization Expanded Programme on Immunization (EPI), a Microsoft Access-based application for two-stage cluster sampling of residential addresses in Louisville/Jefferson County Metro, Kentucky was developed. The sampling frame was derived from geographically referenced data on residential addresses and political districts available through the Louisville/Jefferson County Information Consortium (LOJIC). The program randomly selected 30 clusters, defined as election precincts, from within the area of interest, and then, randomly selected 10 residential addresses from each cluster. The program, called the Rapid Assessment Tools Package (RATP), was tested in terms of accuracy and precision using data on a dichotomous characteristic of residential addresses available from the local tax assessor database. A series of 30 samples were produced and analyzed with respect to their precision and accuracy in estimating the prevalence of the study attribute. Point estimates with 95% confidence intervals were calculated by determining the proportion of the study attribute values in each of the samples and compared with the population proportion. To estimate the design effect, corresponding simple random samples of 300 addresses were taken after each of the 30 cluster samples. The sample proportion fell within +/-10 absolute percentage points of the true proportion in 80% of the samples. In 93.3% of the samples, the point estimate fell within +/-12.5%, and 96.7% fell within +/-15%. All of the point estimates fell within +/-20% of the true proportion. Estimates of the design effect ranged from 0.926 to 1.436 (mean = 1.157, median = 1.170) for the 30 samples. Although prospective evaluation of its performance in field trials or a real emergency is required to confirm its utility, this study suggests that the RATP, a locally designed and deployed tool, may provide population-based estimates of community needs or the extent of event-related consequences that are precise enough to serve as the basis for the initial post-event decisions regarding relief efforts.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sehgal, Neelima; Hlozek, Renee; Addison, Graeme

We present the measured Sunyaev-Zel'dovich (SZ) flux from 474 optically selected MaxBCG clusters that fall within the Atacama Cosmology Telescope (ACT) Equatorial survey region. The ACT Equatorial region used in this analysis covers 510 deg{sup 2} and overlaps Stripe 82 of the Sloan Digital Sky Survey. We also present the measured SZ flux stacked on 52 X-ray-selected MCXC clusters that fall within the ACT Equatorial region and an ACT Southern survey region covering 455 deg{sup 2}. We find that the measured SZ flux from the X-ray-selected clusters is consistent with expectations. However, we find that the measured SZ flux frommore » the optically selected clusters is both significantly lower than expectations and lower than the recovered SZ flux measured by the Planck satellite. Since we find a lower recovered SZ signal than Planck, we investigate the possibility that there is a significant offset between the optically selected brightest cluster galaxies (BCGs) and the SZ centers, to which ACT is more sensitive due to its finer resolution. Such offsets can arise due to either an intrinsic physical separation between the BCG and the center of the gas concentration or from misidentification of the cluster BCG. We find that the entire discrepancy for both ACT and Planck can be explained by assuming that the BCGs are offset from the SZ maxima with a uniform random distribution between 0 and 1.5 Mpc. Such large offsets between gas peaks and BCGs for optically selected cluster samples seem unlikely given that we find the physical separation between BCGs and X-ray peaks for an X-ray-selected subsample of MaxBCG clusters to have a much narrower distribution that peaks within 0.2 Mpc. It is possible that other effects are lowering the ACT and Planck signals by the same amount, with offsets between BCGs and SZ peaks explaining the remaining difference between ACT and Planck measurements. Several effects that can lower the SZ signal equally for both ACT and Planck, but not explain the difference in measured signals, include a larger percentage of false detections in the MaxBCG sample, a lower normalization of the mass-richness relation, radio or infrared galaxy contamination of the SZ flux, and a low intrinsic SZ signal. In the latter two cases, the effects would need to be preferentially more significant in the optically selected MaxBCG sample than in the MCXC X-ray sample.« less

Prevention of emergency physician migratory contamination in a cluster randomized trial to increase tissue plasminogen activator use in stroke (the INSTINCT trial).

PubMed

Weston, Victoria C; Meurer, William J; Frederiksen, Shirley M; Fox, Allison K; Scott, Phillip A

2014-12-01

Cluster randomized trials (CRTs) are increasingly used to evaluate quality improvement interventions aimed at health care providers. In trials testing emergency department (ED) interventions, migration of emergency physicians (EPs) between hospitals is an important concern, as contamination may affect both internal and external validity. We hypothesized that geographically isolating EDs would prevent migratory contamination in a CRT designed to increase ED delivery of tissue plasminogen activator (tPA) in stroke (the INSTINCT trial). INSTINCT was a prospective, cluster randomized, controlled trial. Twenty-four Michigan community hospitals were randomly selected in matched pairs for study. Contamination was defined at the cluster level, with substantial contamination defined a priori as greater than 10% of EPs affected. Nonadherence, total crossover (contamination+nonadherence), migration distance, and characteristics were determined. Three hundred seven EPs were identified at all sites. Overall, 7 (2.3%) changed study sites. One moved between control sites, leaving 6 (2.0%) total crossovers. Of these, 2 (0.7%) moved from intervention to control (contamination); and 4 (1.3%) moved from control to intervention (nonadherence). Contamination was observed in 2 of 12 control sites, with 17% and 9% contamination of the total site EP workforce at follow-up, respectively. Average migration distance was 42 miles for all EPs moving in the study and 35 miles for EPs moving from intervention to control sites. The mobile nature of EPs should be considered in the design of quality improvement CRTs. Increased reporting of contamination in CRTs is encouraged to clarify thresholds and facilitate CRT design. Copyright © 2014 Elsevier Inc. All rights reserved.

Effectiveness of a virtual intervention for primary healthcare professionals aimed at improving attitudes towards the empowerment of patients with chronic diseases: study protocol for a cluster randomized controlled trial (e-MPODERA project).

PubMed

González-González, Ana Isabel; Orrego, Carola; Perestelo-Perez, Lilisbeth; Bermejo-Caja, Carlos Jesús; Mora, Nuria; Koatz, Débora; Ballester, Marta; Del Pino, Tasmania; Pérez-Ramos, Jeannet; Toledo-Chavarri, Ana; Robles, Noemí; Pérez-Rivas, Francisco Javier; Ramírez-Puerta, Ana Belén; Canellas-Criado, Yolanda; Del Rey-Granado, Yolanda; Muñoz-Balsa, Marcos José; Becerril-Rojas, Beatriz; Rodríguez-Morales, David; Sánchez-Perruca, Luis; Vázquez, José Ramón; Aguirre, Armando

2017-10-30

Communities of practice are based on the idea that learning involves a group of people exchanging experiences and knowledge. The e-MPODERA project aims to assess the effectiveness of a virtual community of practice aimed at improving primary healthcare professional attitudes to the empowerment of patients with chronic diseases. This paper describes the protocol for a cluster randomized controlled trial. We will randomly assign 18 primary-care practices per participating region of Spain (Catalonia, Madrid and Canary Islands) to a virtual community of practice or to usual training. The primary-care practice will be the randomization unit and the primary healthcare professional will be the unit of analysis. We will need a sample of 270 primary healthcare professionals (general practitioners and nurses) and 1382 patients. We will perform randomization after professionals and patients are selected. We will ask the intervention group to participate for 12 months in a virtual community of practice based on a web 2.0 platform. We will measure the primary outcome using the Patient-Provider Orientation Scale questionnaire administered at baseline and after 12 months. Secondary outcomes will be the sociodemographic characteristics of health professionals, sociodemographic and clinical characteristics of patients, the Patient Activation Measure questionnaire for patient activation and outcomes regarding use of the virtual community of practice. We will calculate a linear mixed-effects regression to estimate the effect of participating in the virtual community of practice. This cluster randomized controlled trial will show whether a virtual intervention for primary healthcare professionals improves attitudes to the empowerment of patients with chronic diseases. ClicalTrials.gov, NCT02757781 . Registered on 25 April 2016. Protocol Version. PI15.01 22 January 2016.

Promoting state health department evidence-based cancer and chronic disease prevention: a multi-phase dissemination study with a cluster randomized trial component

PubMed Central

2013-01-01

Background Cancer and other chronic diseases reduce quality and length of life and productivity, and represent a significant financial burden to society. Evidence-based public health approaches to prevent cancer and other chronic diseases have been identified in recent decades and have the potential for high impact. Yet, barriers to implement prevention approaches persist as a result of multiple factors including lack of organizational support, limited resources, competing emerging priorities and crises, and limited skill among the public health workforce. The purpose of this study is to learn how best to promote the adoption of evidence based public health practice related to chronic disease prevention. Methods/design This paper describes the methods for a multi-phase dissemination study with a cluster randomized trial component that will evaluate the dissemination of public health knowledge about evidence-based prevention of cancer and other chronic diseases. Phase one involves development of measures of practitioner views on and organizational supports for evidence-based public health and data collection using a national online survey involving state health department chronic disease practitioners. In phase two, a cluster randomized trial design will be conducted to test receptivity and usefulness of dissemination strategies directed toward state health department chronic disease practitioners to enhance capacity and organizational support for evidence-based chronic disease prevention. Twelve state health department chronic disease units will be randomly selected and assigned to intervention or control. State health department staff and the university-based study team will jointly identify, refine, and select dissemination strategies within intervention units. Intervention (dissemination) strategies may include multi-day in-person training workshops, electronic information exchange modalities, and remote technical assistance. Evaluation methods include pre-post surveys, structured qualitative phone interviews, and abstraction of state-level chronic disease prevention program plans and progress reports. Trial registration clinicaltrials.gov: NCT01978054. PMID:24330729

A Study of the Relationship between Demographic Factors and Elementary School Teacher Burnout: The Iranian Case

ERIC Educational Resources Information Center

Mazidi, Mohammad; Khoshbakht, Friba; Mahboobe, Alborzi

2017-01-01

The aim of the present study was to investigate the relationship between certain demographic factors and elementary school teachers' burnout. The sample consisted of 144 elementary school teachers (98 male and 76 women) selected through cluster random sampling. Data were collected by: (1) Personal Information Form developed by the researchers, and…

Intra-Group Motivational Analysis of Students with Learning Disabilities: A Goal Orientation Approach

ERIC Educational Resources Information Center

Sideris, Georgios D.; Tsorbatzoudis, Charalambos

2003-01-01

The purpose of the present study was to profile, using a K-means cluster analysis, the cognitive, motivational, affective, and goal orientation characteristics of elementary school students with and without learning disabilities (LD). Participants were 58 fifth and 6 sixth graders (29 typical and 29 LD) selected using stratified random procedures.…

Parental and School Bonding in Iranian Adolescent Perpetrators and Victims of Bullying

ERIC Educational Resources Information Center

Mohebbi, Mina; Mirnasab, Mirmahmoud; Wiener, Judith

2016-01-01

This study compared parental and school bonding in adolescents in Iran who are perpetrators of bullying, victims of bullying and not-involved in bullying. Secondary school students (N = 240) were selected by cluster random sampling and screening, and categorized as perpetrators of bullying (N = 80), victims of bullying (N = 80) and non-involved (N…

Implementation of Structured Inquiry Based Model Learning toward Students' Understanding of Geometry

ERIC Educational Resources Information Center

Salim, Kalbin; Tiawa, Dayang Hjh

2015-01-01

The purpose of this study is implementation of a structured inquiry learning model in instruction of geometry. The model used is a model with a quasi-experimental study amounted to two classes of samples selected from the population of the ten classes with cluster random sampling technique. Data collection tool consists of a test item…

Identity Styles and Academic Achievement: Mediating Role of Academic Self-Efficacy

ERIC Educational Resources Information Center

Hejazi, Elaheh; Shahraray, Mehrnaz; Farsinejad, Masomeh; Asgary, Ali

2009-01-01

The purpose of this study was to assess the mediating effect of self-efficacy on the relationship between identity styles and academic achievement. Four-hundred high school students (200 male, 200 female) who were selected through cluster random sampling, completed the Revised Identity Styles Inventory (ISI, 6G) and Morgan-Jink Student Efficacy…

Percolation of the site random-cluster model by Monte Carlo method

NASA Astrophysics Data System (ADS)

Wang, Songsong; Zhang, Wanzhou; Ding, Chengxiang

2015-08-01

We propose a site random-cluster model by introducing an additional cluster weight in the partition function of the traditional site percolation. To simulate the model on a square lattice, we combine the color-assignation and the Swendsen-Wang methods to design a highly efficient cluster algorithm with a small critical slowing-down phenomenon. To verify whether or not it is consistent with the bond random-cluster model, we measure several quantities, such as the wrapping probability Re, the percolating cluster density P∞, and the magnetic susceptibility per site χp, as well as two exponents, such as the thermal exponent yt and the fractal dimension yh of the percolating cluster. We find that for different exponents of cluster weight q =1.5 , 2, 2.5 , 3, 3.5 , and 4, the numerical estimation of the exponents yt and yh are consistent with the theoretical values. The universalities of the site random-cluster model and the bond random-cluster model are completely identical. For larger values of q , we find obvious signatures of the first-order percolation transition by the histograms and the hysteresis loops of percolating cluster density and the energy per site. Our results are helpful for the understanding of the percolation of traditional statistical models.

Effect Sizes in Cluster-Randomized Designs

ERIC Educational Resources Information Center

Hedges, Larry V.

2007-01-01

Multisite research designs involving cluster randomization are becoming increasingly important in educational and behavioral research. Researchers would like to compute effect size indexes based on the standardized mean difference to compare the results of cluster-randomized studies (and corresponding quasi-experiments) with other studies and to…

Assessment of community-level effects of intermittent preventive treatment for malaria in schoolchildren in Jinja, Uganda (START-IPT trial): a cluster-randomised trial.

PubMed

Staedke, Sarah G; Maiteki-Sebuguzi, Catherine; Rehman, Andrea M; Kigozi, Simon P; Gonahasa, Samuel; Okiring, Jaffer; Lindsay, Steve W; Kamya, Moses R; Chandler, Clare I R; Dorsey, Grant; Drakeley, Chris

2018-06-01

Intermittent preventive treatment (IPT) is a well established malaria control intervention. Evidence that delivering IPT to schoolchildren could provide community-level benefits is limited. We did a cluster-randomised controlled trial to assess the effect of IPT of primary schoolchildren with dihydroartemisinin-piperaquine (DP) on indicators of malaria transmission in the community, in Jinja, Uganda. We included 84 clusters, each comprising one primary school and the 100 closest available households. The clusters were randomly assigned 1:1 to receive IPT with DP or standard care (control) by restricted randomisation to ensure balance by geography and school type. Children in intervention schools received IPT monthly for up to six rounds (June to December, 2014). We did cross-sectional community surveys in randomly selected households at baseline and in January to April, 2015, during which we measured participants' temperatures and obtained finger-prick blood smears for measurement of parasite prevalence by microscopy. We also did entomological surveys 1 night per month in households from 20 randomly selected IPT and 20 control clusters. The primary trial outcome was parasite prevalence in the final community survey. The primary entomological survey outcome was the annual entomological inoculation rate (aEIR) from July, 2014, to April, 2015. This trial is registered at ClinicalTrials.gov, number NCT02009215. Among 23 280 students registered in the 42 intervention schools, 10 079 (43%) aged 5-20 years were enrolled and received at least one dose of DP. 9286 (92%) of 10 079 received at least one full course of DP (three doses). Community-level parasite prevalence was lower in the intervention clusters than in the control clusters (19% vs 23%, adjusted risk ratio 0·85, 95% CI 0·73-1·00, p=0·05). The aEIR was lower in the intervention group than in the control group, but not significantly so (10·1 vs 15·2 infective bites per person, adjusted incidence rate ratio 0·80, 95% CI 0·36-1·80, p=0·59). IPT of schoolchildren with DP might have a positive effect on community-level malaria indicators and be operationally feasible. Studies with greater IPT coverage are needed. UK Medical Research Council, UK Department for International Development, and Wellcome Trust. Copyright © 2018 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

A focus group study to understand biases and confounders in a cluster randomized controlled trial on low back pain in primary care in Norway.

PubMed

Werner, Erik L; Løchting, Ida; Storheim, Kjersti; Grotle, Margreth

2018-05-22

Cluster randomized controlled trials are often used in research in primary care but creates challenges regarding biases and confounders. We recently presented a study on low back pain from primary care in Norway with equal effects in the intervention and the control group. In order to understand the specific mechanisms that may produce biases in a cluster randomized trial we conducted a focus group study among the participating health care providers. The aim of this study was to understand how the participating providers themselves influenced on the study and thereby possibly on the results of the cluster randomized controlled trial. The providers were invited to share their experiences from their participation in the COPE study, from recruitment of patients to accomplishment of either the intervention or control consultations. Six clinicians from the intervention group and four from the control group took part in the focus group interviews. The group discussions focused on feasibility of the study in primary care and particularly on identifying potential biases and confounders in the study. The audio-recorded interviews were transcribed verbatim and analyzed according to a systematic text condensation. The themes for the analysis emerged from the group discussions. A personal interest for back pain, logistic factors at the clinics and an assessment of the patients' capacity to accomplish the study prior to their recruitment was reported. The providers were allowed to provide additional therapy to the intervention and it turned out that some of these could be regarded as opposed to the messages of the intervention. The providers seemed to select different items from the educational package according to personal beliefs and their perception of the patients' acceptance. The study disclosed several potential biases to the COPE study which may have impacted on the study results. Awareness of these is highly important when planning and conducting a cluster randomized controlled trial. Procedures in the recruitment of both providers and patients seem to be key factors and the providers should be aware of their role in a scientific study in order to standardize the provision of the intervention.

Reaching the unreached through trained and skilled birth attendants in Ethiopia: a cluster randomized controlled trial study protocol.

PubMed

Zerfu, Taddese Alemu; Taddese, Henok; Nigatu, Tariku; Tenkolu, Girma; Vogel, Joshua P; Khan-Neelofur, Dina; Biadgilign, Sibhatu; Deribew, Amare

2017-01-26

Despite improvements since 1990 to 2014, maternal mortality ratio (MMR) remains high in Ethiopia. One of the key drivers of maternal mortality in Ethiopia is the very low coverage of Skilled Birth attendance (SBA) in rural Ethiopia. This cluster randomized trial piloted an innovative approach of deploying trained community reproductive nurses (CORN) to hard to reach/unreachable rural Ethiopia to improve the coverage of SBA. We used a three-arm cluster randomized trial to test the effect of deploying CORN in rural communities in South Ethiopia to improve SBA and other maternal health indicators. A total of 282 villages/clusters (94 from each arm) were randomly selected in the three districts of the zone for the study. The intervention was implemented in four consecutive phases that aimed at of provision of essential maternal, neonatal and child health (MNCH) services mainly focusing on SBA. The CORN were trained and deployed in health centres (arm 1) and in the community/health posts (arm2). A third arm (arm 3) consisting control villages without the intervention. A baseline and end line assessment was conducted to compare the difference in the proportion of SBA and other MNCH service uptake across the three arms Data was entered into computer, edited, cleaned, and analyzed using Epi-data statistical software. The presentation followed the Consolidated Standards of Reporting Trials (CONSORT) statement guidelines for cluster-randomized trials. This trial is designed to test the impact of an innovative and newly designed means of distribution for the national health extension program strategy with additional service package with no change to the target population. The focus is on effect of CORN in revitalizing the Health Extension Program (HEP) through improving SBA service uptake and other maternal health service uptake indicators. The study findings may guide national policy to strengthen and shape the already existing HEP that has certain limitations to improve maternal health indicators. The competency based training methodology could provide feedback for health science colleges to improve the national nursing or midwifery training curriculum. clinicaltrails.gov NCT02501252 dated on July 14, 2015.

The effect of clustering on perceived quantity in humans (Homo sapiens) and in chicks (Gallus gallus).

PubMed

Bertamini, Marco; Guest, Martin; Vallortigara, Giorgio; Rugani, Rosa; Regolin, Lucia

2018-04-30

Animals can perceive the numerosity of sets of visual elements. Qualitative and quantitative similarities in different species suggest the existence of a shared system (approximate number system). Biases associated with sensory properties are informative about the underlying mechanisms. In humans, regular spacing increases perceived numerosity (regular-random numerosity illusion). This has led to a model that predicts numerosity based on occupancy (a measure that decreases when elements are close together). We used a procedure in which observers selected one of two stimuli and were given feedback with respect to whether the choice was correct. One configuration had 20 elements and the other 40, randomly placed inside a circular region. Participants had to discover the rule based on feedback. Because density and clustering covaried with numerosity, different dimensions could be used. After reaching a criterion, test trials presented two types of configurations with 30 elements. One type had a larger interelement distance than the other (high or low clustering). If observers had adopted a numerosity strategy, they would choose low clustering (if reinforced with 40) and high clustering (if reinforced with 20). A clustering or density strategy predicts the opposite. Human adults used a numerosity strategy. Chicks were tested using a similar procedure. There were two behavioral measures: first approach response and final circumnavigation (walking behind the screen). The prediction based on numerosity was confirmed by the first approach data. For chicks, one clear pattern from both responses was a preference for the configurations with higher clustering. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Finite-sample corrected generalized estimating equation of population average treatment effects in stepped wedge cluster randomized trials.

PubMed

Scott, JoAnna M; deCamp, Allan; Juraska, Michal; Fay, Michael P; Gilbert, Peter B

2017-04-01

Stepped wedge designs are increasingly commonplace and advantageous for cluster randomized trials when it is both unethical to assign placebo, and it is logistically difficult to allocate an intervention simultaneously to many clusters. We study marginal mean models fit with generalized estimating equations for assessing treatment effectiveness in stepped wedge cluster randomized trials. This approach has advantages over the more commonly used mixed models that (1) the population-average parameters have an important interpretation for public health applications and (2) they avoid untestable assumptions on latent variable distributions and avoid parametric assumptions about error distributions, therefore, providing more robust evidence on treatment effects. However, cluster randomized trials typically have a small number of clusters, rendering the standard generalized estimating equation sandwich variance estimator biased and highly variable and hence yielding incorrect inferences. We study the usual asymptotic generalized estimating equation inferences (i.e., using sandwich variance estimators and asymptotic normality) and four small-sample corrections to generalized estimating equation for stepped wedge cluster randomized trials and for parallel cluster randomized trials as a comparison. We show by simulation that the small-sample corrections provide improvement, with one correction appearing to provide at least nominal coverage even with only 10 clusters per group. These results demonstrate the viability of the marginal mean approach for both stepped wedge and parallel cluster randomized trials. We also study the comparative performance of the corrected methods for stepped wedge and parallel designs, and describe how the methods can accommodate interval censoring of individual failure times and incorporate semiparametric efficient estimators.

Focused Screening of ECM-Selective Adhesion Peptides on Cellulose-Bound Peptide Microarrays.

PubMed

Kanie, Kei; Kondo, Yuto; Owaki, Junki; Ikeda, Yurika; Narita, Yuji; Kato, Ryuji; Honda, Hiroyuki

2016-11-19

The coating of surfaces with bio-functional proteins is a promising strategy for the creation of highly biocompatible medical implants. Bio-functional proteins from the extracellular matrix (ECM) provide effective surface functions for controlling cellular behavior. We have previously screened bio-functional tripeptides for feasibility of mass production with the aim of identifying those that are medically useful, such as cell-selective peptides. In this work, we focused on the screening of tripeptides that selectively accumulate collagen type IV (Col IV), an ECM protein that accelerates the re-endothelialization of medical implants. A SPOT peptide microarray was selected for screening owing to its unique cellulose membrane platform, which can mimic fibrous scaffolds used in regenerative medicine. However, since the library size on the SPOT microarray was limited, physicochemical clustering was used to provide broader variation than that of random peptide selection. Using the custom focused microarray of 500 selected peptides, we assayed the relative binding rates of tripeptides to Col IV, collagen type I (Col I), and albumin. We discovered a cluster of Col IV-selective adhesion peptides that exhibit bio-safety with endothelial cells. The results from this study can be used to improve the screening of regeneration-enhancing peptides.

Focused Screening of ECM-Selective Adhesion Peptides on Cellulose-Bound Peptide Microarrays

PubMed Central

Kanie, Kei; Kondo, Yuto; Owaki, Junki; Ikeda, Yurika; Narita, Yuji; Kato, Ryuji; Honda, Hiroyuki

2016-01-01

The coating of surfaces with bio-functional proteins is a promising strategy for the creation of highly biocompatible medical implants. Bio-functional proteins from the extracellular matrix (ECM) provide effective surface functions for controlling cellular behavior. We have previously screened bio-functional tripeptides for feasibility of mass production with the aim of identifying those that are medically useful, such as cell-selective peptides. In this work, we focused on the screening of tripeptides that selectively accumulate collagen type IV (Col IV), an ECM protein that accelerates the re-endothelialization of medical implants. A SPOT peptide microarray was selected for screening owing to its unique cellulose membrane platform, which can mimic fibrous scaffolds used in regenerative medicine. However, since the library size on the SPOT microarray was limited, physicochemical clustering was used to provide broader variation than that of random peptide selection. Using the custom focused microarray of 500 selected peptides, we assayed the relative binding rates of tripeptides to Col IV, collagen type I (Col I), and albumin. We discovered a cluster of Col IV-selective adhesion peptides that exhibit bio-safety with endothelial cells. The results from this study can be used to improve the screening of regeneration-enhancing peptides. PMID:28952593

A two-step initial mass function:. Consequences of clustered star formation for binary properties

NASA Astrophysics Data System (ADS)

Durisen, R. H.; Sterzik, M. F.; Pickett, B. K.

2001-06-01

If stars originate in transient bound clusters of moderate size, these clusters will decay due to dynamic interactions in which a hard binary forms and ejects most or all the other stars. When the cluster members are chosen at random from a reasonable initial mass function (IMF), the resulting binary characteristics do not match current observations. We find a significant improvement in the trends of binary properties from this scenario when an additional constraint is taken into account, namely that there is a distribution of total cluster masses set by the masses of the cloud cores from which the clusters form. Two distinct steps then determine final stellar masses - the choice of a cluster mass and the formation of the individual stars. We refer to this as a ``two-step'' IMF. Simple statistical arguments are used in this paper to show that a two-step IMF, combined with typical results from dynamic few-body system decay, tends to give better agreement between computed binary characteristics and observations than a one-step mass selection process.

A nonparametric method to generate synthetic populations to adjust for complex sampling design features.

PubMed

Dong, Qi; Elliott, Michael R; Raghunathan, Trivellore E

2014-06-01

Outside of the survey sampling literature, samples are often assumed to be generated by a simple random sampling process that produces independent and identically distributed (IID) samples. Many statistical methods are developed largely in this IID world. Application of these methods to data from complex sample surveys without making allowance for the survey design features can lead to erroneous inferences. Hence, much time and effort have been devoted to develop the statistical methods to analyze complex survey data and account for the sample design. This issue is particularly important when generating synthetic populations using finite population Bayesian inference, as is often done in missing data or disclosure risk settings, or when combining data from multiple surveys. By extending previous work in finite population Bayesian bootstrap literature, we propose a method to generate synthetic populations from a posterior predictive distribution in a fashion inverts the complex sampling design features and generates simple random samples from a superpopulation point of view, making adjustment on the complex data so that they can be analyzed as simple random samples. We consider a simulation study with a stratified, clustered unequal-probability of selection sample design, and use the proposed nonparametric method to generate synthetic populations for the 2006 National Health Interview Survey (NHIS), and the Medical Expenditure Panel Survey (MEPS), which are stratified, clustered unequal-probability of selection sample designs.

A nonparametric method to generate synthetic populations to adjust for complex sampling design features

PubMed Central

Dong, Qi; Elliott, Michael R.; Raghunathan, Trivellore E.

2017-01-01

Outside of the survey sampling literature, samples are often assumed to be generated by a simple random sampling process that produces independent and identically distributed (IID) samples. Many statistical methods are developed largely in this IID world. Application of these methods to data from complex sample surveys without making allowance for the survey design features can lead to erroneous inferences. Hence, much time and effort have been devoted to develop the statistical methods to analyze complex survey data and account for the sample design. This issue is particularly important when generating synthetic populations using finite population Bayesian inference, as is often done in missing data or disclosure risk settings, or when combining data from multiple surveys. By extending previous work in finite population Bayesian bootstrap literature, we propose a method to generate synthetic populations from a posterior predictive distribution in a fashion inverts the complex sampling design features and generates simple random samples from a superpopulation point of view, making adjustment on the complex data so that they can be analyzed as simple random samples. We consider a simulation study with a stratified, clustered unequal-probability of selection sample design, and use the proposed nonparametric method to generate synthetic populations for the 2006 National Health Interview Survey (NHIS), and the Medical Expenditure Panel Survey (MEPS), which are stratified, clustered unequal-probability of selection sample designs. PMID:29200608

Cluster-randomized Studies in Educational Research: Principles and Methodological Aspects.

PubMed

Dreyhaupt, Jens; Mayer, Benjamin; Keis, Oliver; Öchsner, Wolfgang; Muche, Rainer

2017-01-01

An increasing number of studies are being performed in educational research to evaluate new teaching methods and approaches. These studies could be performed more efficiently and deliver more convincing results if they more strictly applied and complied with recognized standards of scientific studies. Such an approach could substantially increase the quality in particular of prospective, two-arm (intervention) studies that aim to compare two different teaching methods. A key standard in such studies is randomization, which can minimize systematic bias in study findings; such bias may result if the two study arms are not structurally equivalent. If possible, educational research studies should also achieve this standard, although this is not yet generally the case. Some difficulties and concerns exist, particularly regarding organizational and methodological aspects. An important point to consider in educational research studies is that usually individuals cannot be randomized, because of the teaching situation, and instead whole groups have to be randomized (so-called "cluster randomization"). Compared with studies with individual randomization, studies with cluster randomization normally require (significantly) larger sample sizes and more complex methods for calculating sample size. Furthermore, cluster-randomized studies require more complex methods for statistical analysis. The consequence of the above is that a competent expert with respective special knowledge needs to be involved in all phases of cluster-randomized studies. Studies to evaluate new teaching methods need to make greater use of randomization in order to achieve scientifically convincing results. Therefore, in this article we describe the general principles of cluster randomization and how to implement these principles, and we also outline practical aspects of using cluster randomization in prospective, two-arm comparative educational research studies.

Molecular codes for neuronal individuality and cell assembly in the brain

PubMed Central

Yagi, Takeshi

2012-01-01

The brain contains an enormous, but finite, number of neurons. The ability of this limited number of neurons to produce nearly limitless neural information over a lifetime is typically explained by combinatorial explosion; that is, by the exponential amplification of each neuron's contribution through its incorporation into “cell assemblies” and neural networks. In development, each neuron expresses diverse cellular recognition molecules that permit the formation of the appropriate neural cell assemblies to elicit various brain functions. The mechanism for generating neuronal assemblies and networks must involve molecular codes that give neurons individuality and allow them to recognize one another and join appropriate networks. The extensive molecular diversity of cell-surface proteins on neurons is likely to contribute to their individual identities. The clustered protocadherins (Pcdh) is a large subfamily within the diverse cadherin superfamily. The clustered Pcdh genes are encoded in tandem by three gene clusters, and are present in all known vertebrate genomes. The set of clustered Pcdh genes is expressed in a random and combinatorial manner in each neuron. In addition, cis-tetramers composed of heteromultimeric clustered Pcdh isoforms represent selective binding units for cell-cell interactions. Here I present the mathematical probabilities for neuronal individuality based on the random and combinatorial expression of clustered Pcdh isoforms and their formation of cis-tetramers in each neuron. Notably, clustered Pcdh gene products are known to play crucial roles in correct axonal projections, synaptic formation, and neuronal survival. Their molecular and biological features induce a hypothesis that the diverse clustered Pcdh molecules provide the molecular code by which neuronal individuality and cell assembly permit the combinatorial explosion of networks that supports enormous processing capability and plasticity of the brain. PMID:22518100

Ensemble Feature Learning of Genomic Data Using Support Vector Machine

PubMed Central

Anaissi, Ali; Goyal, Madhu; Catchpoole, Daniel R.; Braytee, Ali; Kennedy, Paul J.

2016-01-01

The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random decision trees with bagging to improve overall feature selection and classification accuracy. Surprisingly, the adoption of these methods in support vector machines has only recently received attention but mostly on classification not gene selection. This paper introduces an ensemble SVM-Recursive Feature Elimination (ESVM-RFE) for gene selection that follows the concepts of ensemble and bagging used in random forest but adopts the backward elimination strategy which is the rationale of RFE algorithm. The rationale behind this is, building ensemble SVM models using randomly drawn bootstrap samples from the training set, will produce different feature rankings which will be subsequently aggregated as one feature ranking. As a result, the decision for elimination of features is based upon the ranking of multiple SVM models instead of choosing one particular model. Moreover, this approach will address the problem of imbalanced datasets by constructing a nearly balanced bootstrap sample. Our experiments show that ESVM-RFE for gene selection substantially increased the classification performance on five microarray datasets compared to state-of-the-art methods. Experiments on the childhood leukaemia dataset show that an average 9% better accuracy is achieved by ESVM-RFE over SVM-RFE, and 5% over random forest based approach. The selected genes by the ESVM-RFE algorithm were further explored with Singular Value Decomposition (SVD) which reveals significant clusters with the selected data. PMID:27304923

Multilevel Analysis Methods for Partially Nested Cluster Randomized Trials

ERIC Educational Resources Information Center

Sanders, Elizabeth A.

2011-01-01

This paper explores multilevel modeling approaches for 2-group randomized experiments in which a treatment condition involving clusters of individuals is compared to a control condition involving only ungrouped individuals, otherwise known as partially nested cluster randomized designs (PNCRTs). Strategies for comparing groups from a PNCRT in the…

On the limiting characteristics of quantum random number generators at various clusterings of photocounts

NASA Astrophysics Data System (ADS)

Molotkov, S. N.

2017-03-01

Various methods for the clustering of photocounts constituting a sequence of random numbers are considered. It is shown that the clustering of photocounts resulting in the Fermi-Dirac distribution makes it possible to achieve the theoretical limit of the random number generation rate.

Cluster randomization and political philosophy.

PubMed

Chwang, Eric

2012-11-01

In this paper, I will argue that, while the ethical issues raised by cluster randomization can be challenging, they are not new. My thesis divides neatly into two parts. In the first, easier part I argue that many of the ethical challenges posed by cluster randomized human subjects research are clearly present in other types of human subjects research, and so are not novel. In the second, more difficult part I discuss the thorniest ethical challenge for cluster randomized research--cases where consent is genuinely impractical to obtain. I argue that once again these cases require no new analytic insight; instead, we should look to political philosophy for guidance. In other words, the most serious ethical problem that arises in cluster randomized research also arises in political philosophy. © 2011 Blackwell Publishing Ltd.

Genetic relatedness between oral and intestinal isolates of Porphyromonas endodontalis by analysis of random amplified polymorphic DNA.

PubMed

Gonçalves, R B; Väisänen, M L; Van Steenbergen, T J; Sundqvist, G; Mouton, C

1999-01-01

Genomic fingerprints from the DNA of 27 strains of Porphyromonas endodontalis from diverse clinical and geographic origins were generated as random amplified polymorphic DNA (RAPD) using the technique of PCR amplification with a single primer of arbitrary sequence. Cluster analysis of the combined RAPD data obtained with three selected 9- or 10-mer-long primers identified 25 distinct RAPD types which clustered as three main groups identifying three genogroups. Genogroups I and II included exclusively P. endodontalis isolates of oral origin, while 7/9 human intestinal strains of genogroup III which linked at a similarity level of 52% constituted the most homogeneous group in our study. Genotypic diversity within P. endodontalis, as shown by RAPD analysis, suggests that the taxon is composed of two oral genogroups and one intestinal genogroup. This hypothesis remains to be confirmed.

Fast Constrained Spectral Clustering and Cluster Ensemble with Random Projection

PubMed Central

Liu, Wenfen

2017-01-01

Constrained spectral clustering (CSC) method can greatly improve the clustering accuracy with the incorporation of constraint information into spectral clustering and thus has been paid academic attention widely. In this paper, we propose a fast CSC algorithm via encoding landmark-based graph construction into a new CSC model and applying random sampling to decrease the data size after spectral embedding. Compared with the original model, the new algorithm has the similar results with the increase of its model size asymptotically; compared with the most efficient CSC algorithm known, the new algorithm runs faster and has a wider range of suitable data sets. Meanwhile, a scalable semisupervised cluster ensemble algorithm is also proposed via the combination of our fast CSC algorithm and dimensionality reduction with random projection in the process of spectral ensemble clustering. We demonstrate by presenting theoretical analysis and empirical results that the new cluster ensemble algorithm has advantages in terms of efficiency and effectiveness. Furthermore, the approximate preservation of random projection in clustering accuracy proved in the stage of consensus clustering is also suitable for the weighted k-means clustering and thus gives the theoretical guarantee to this special kind of k-means clustering where each point has its corresponding weight. PMID:29312447

Proposed variations of the stepped-wedge design can be used to accommodate multiple interventions

PubMed Central

Lyons, Vivian H; Li, Lingyu; Hughes, James P; Rowhani-Rahbar, Ali

2018-01-01

Objective Stepped wedge design (SWD) cluster randomized trials have traditionally been used for evaluating a single intervention. We aimed to explore design variants suitable for evaluating multiple interventions in a SWD trial. Study Design and Setting We identified four specific variants of the traditional SWD that would allow two interventions to be conducted within a single cluster randomized trial: Concurrent, Replacement, Supplementation and Factorial SWDs. These variants were chosen to flexibly accommodate study characteristics that limit a one-size-fits-all approach for multiple interventions. Results In the Concurrent SWD, each cluster receives only one intervention, unlike the other variants. The Replacement SWD supports two interventions that will not or cannot be employed at the same time. The Supplementation SWD is appropriate when the second intervention requires the presence of the first intervention, and the Factorial SWD supports the evaluation of intervention interactions. The precision for estimating intervention effects varies across the four variants. Conclusion Selection of the appropriate design variant should be driven by the research question while considering the trade-off between the number of steps, number of clusters, restrictions for concurrent implementation of the interventions, lingering effects of each intervention, and precision of the intervention effect estimates. PMID:28412466

Cluster-randomized Studies in Educational Research: Principles and Methodological Aspects

PubMed Central

Dreyhaupt, Jens; Mayer, Benjamin; Keis, Oliver; Öchsner, Wolfgang; Muche, Rainer

2017-01-01

An increasing number of studies are being performed in educational research to evaluate new teaching methods and approaches. These studies could be performed more efficiently and deliver more convincing results if they more strictly applied and complied with recognized standards of scientific studies. Such an approach could substantially increase the quality in particular of prospective, two-arm (intervention) studies that aim to compare two different teaching methods. A key standard in such studies is randomization, which can minimize systematic bias in study findings; such bias may result if the two study arms are not structurally equivalent. If possible, educational research studies should also achieve this standard, although this is not yet generally the case. Some difficulties and concerns exist, particularly regarding organizational and methodological aspects. An important point to consider in educational research studies is that usually individuals cannot be randomized, because of the teaching situation, and instead whole groups have to be randomized (so-called “cluster randomization”). Compared with studies with individual randomization, studies with cluster randomization normally require (significantly) larger sample sizes and more complex methods for calculating sample size. Furthermore, cluster-randomized studies require more complex methods for statistical analysis. The consequence of the above is that a competent expert with respective special knowledge needs to be involved in all phases of cluster-randomized studies. Studies to evaluate new teaching methods need to make greater use of randomization in order to achieve scientifically convincing results. Therefore, in this article we describe the general principles of cluster randomization and how to implement these principles, and we also outline practical aspects of using cluster randomization in prospective, two-arm comparative educational research studies. PMID:28584874

The Role of School Culture and Basic Psychological Needs on Iranian Adolescents' Academic Alienation: A Multi-Level Examination

ERIC Educational Resources Information Center

Mahmoudi, Hojjat; Brown, Monica R.; Amani Saribagloo, Javad; Dadashzadeh, Shiva

2018-01-01

This aim of this current research was a multi-level analysis of the relationship between school culture, basic psychological needs, and adolescents' academic alienation. One thousand twenty-nine (N = 1,029) high school students from Qom City were randomly selected through a multi-phase cluster sampling method and answered questions regarding…

Mathematics Skill of Fifteen Years Old Students in Yogyakarta in Solving Problems Like PISA

ERIC Educational Resources Information Center

Wulandari, Nidya Ferry; Jailani

2018-01-01

The aims of this research were to describe mathematics skill of 8th fifteen-year old students in Yogyakarta in solving problem of PISA. The sampling was combination of stratified and cluster random sampling. The sample consisting of 400 students was selected from fifteen schools. The data collection was by tests. The research finding revealed that…

How Professionalized Is College Teaching? Norms and the Ideal of Service. ASHE Annual Meeting Paper.

ERIC Educational Resources Information Center

Braxton, John M.; Bayer, Alan E.

This study examined the behavioral expectations and norms for college and university faculty particularly whether they varied with respect to the level of commitment to teaching at different institutions and in different disciplines. A cluster sampling design was used to select a random sample of the population of faculty in biology, history,…

Individual and Familial Correlates of Career Salience among Upwardly Mobile College Women. Final Report.

ERIC Educational Resources Information Center

Guttmacher, Mary Johnson

A case study was conducted using a sample of 271 women selected from a state college by a stratified random cluster technique that approximates proportional representation of women in all four classes and all college majors. The data source was an extensive questionnaire designed to measure the attitudes and behavior of interest. The major…

Applying the Dynamic Social Systems Model to HIV Prevention in a Rural African Context: The Maasai and the "Esoto" Dance

ERIC Educational Resources Information Center

Siegler, Aaron J.; Mbwambo, Jessie K.; DiClemente, Ralph J.

2013-01-01

This study applied the Dynamic Social Systems Model (DSSM) to the issue of HIV risk among the Maasai tribe of Tanzania, using data from a cross-sectional, cluster survey among 370 randomly selected participants from Ngorongoro and Siha Districts. A culturally appropriate survey instrument was developed to explore traditions reportedly coadunate…

Establishing the Baseline Height and Weight Status of New Hampshire Head Start Children, 2007-2008

ERIC Educational Resources Information Center

Blaney, David D.; Flynn, Regina T.; Martin, Nancy R.; Anderson, Ludmila

2010-01-01

We report on a standardized survey of height and weight status of children attending the New Hampshire Head Start Program during the 2007-2008 school year. Baseline prevalence estimates of overweight and obesity are needed for obesity prevention activities and intervention. We selected a random one-stage cluster sample and screened 629 children…

The Method of Randomization for Cluster-Randomized Trials: Challenges of Including Patients with Multiple Chronic Conditions

PubMed Central

Esserman, Denise; Allore, Heather G.; Travison, Thomas G.

2016-01-01

Cluster-randomized clinical trials (CRT) are trials in which the unit of randomization is not a participant but a group (e.g. healthcare systems or community centers). They are suitable when the intervention applies naturally to the cluster (e.g. healthcare policy); when lack of independence among participants may occur (e.g. nursing home hygiene); or when it is most ethical to apply an intervention to all within a group (e.g. school-level immunization). Because participants in the same cluster receive the same intervention, CRT may approximate clinical practice, and may produce generalizable findings. However, when not properly designed or interpreted, CRT may induce biased results. CRT designs have features that add complexity to statistical estimation and inference. Chief among these is the cluster-level correlation in response measurements induced by the randomization. A critical consideration is the experimental unit of inference; often it is desirable to consider intervention effects at the level of the individual rather than the cluster. Finally, given that the number of clusters available may be limited, simple forms of randomization may not achieve balance between intervention and control arms at either the cluster- or participant-level. In non-clustered clinical trials, balance of key factors may be easier to achieve because the sample can be homogenous by exclusion of participants with multiple chronic conditions (MCC). CRTs, which are often pragmatic, may eschew such restrictions. Failure to account for imbalance may induce bias and reducing validity. This article focuses on the complexities of randomization in the design of CRTs, such as the inclusion of patients with MCC, and imbalances in covariate factors across clusters. PMID:27478520

Bayesian network meta-analysis for cluster randomized trials with binary outcomes.

PubMed

Uhlmann, Lorenz; Jensen, Katrin; Kieser, Meinhard

2017-06-01

Network meta-analysis is becoming a common approach to combine direct and indirect comparisons of several treatment arms. In recent research, there have been various developments and extensions of the standard methodology. Simultaneously, cluster randomized trials are experiencing an increased popularity, especially in the field of health services research, where, for example, medical practices are the units of randomization but the outcome is measured at the patient level. Combination of the results of cluster randomized trials is challenging. In this tutorial, we examine and compare different approaches for the incorporation of cluster randomized trials in a (network) meta-analysis. Furthermore, we provide practical insight on the implementation of the models. In simulation studies, it is shown that some of the examined approaches lead to unsatisfying results. However, there are alternatives which are suitable to combine cluster randomized trials in a network meta-analysis as they are unbiased and reach accurate coverage rates. In conclusion, the methodology can be extended in such a way that an adequate inclusion of the results obtained in cluster randomized trials becomes feasible. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Pattern selection and super-patterns in the bounded confidence model

DOE PAGES

Ben-Naim, E.; Scheel, A.

2015-10-26

We study pattern formation in the bounded confidence model of opinion dynamics. In this random process, opinion is quantified by a single variable. Two agents may interact and reach a fair compromise, but only if their difference of opinion falls below a fixed threshold. Starting from a uniform distribution of opinions with compact support, a traveling wave forms and it propagates from the domain boundary into the unstable uniform state. Consequently, the system reaches a steady state with isolated clusters that are separated by distance larger than the interaction range. These clusters form a quasi-periodic pattern where the sizes ofmore » the clusters and the separations between them are nearly constant. We obtain analytically the average separation between clusters L. Interestingly, there are also very small quasi-periodic modulations in the size of the clusters. Furthermore, the spatial periods of these modulations are a series of integers that follow from the continued-fraction representation of the irrational average separation L.« less

Pattern selection and super-patterns in the bounded confidence model

NASA Astrophysics Data System (ADS)

Ben-Naim, E.; Scheel, A.

2015-10-01

We study pattern formation in the bounded confidence model of opinion dynamics. In this random process, opinion is quantified by a single variable. Two agents may interact and reach a fair compromise, but only if their difference of opinion falls below a fixed threshold. Starting from a uniform distribution of opinions with compact support, a traveling wave forms and it propagates from the domain boundary into the unstable uniform state. Consequently, the system reaches a steady state with isolated clusters that are separated by distance larger than the interaction range. These clusters form a quasi-periodic pattern where the sizes of the clusters and the separations between them are nearly constant. We obtain analytically the average separation between clusters L. Interestingly, there are also very small quasi-periodic modulations in the size of the clusters. The spatial periods of these modulations are a series of integers that follow from the continued-fraction representation of the irrational average separation L.

Percolation and epidemics in random clustered networks

NASA Astrophysics Data System (ADS)

Miller, Joel C.

2009-08-01

The social networks that infectious diseases spread along are typically clustered. Because of the close relation between percolation and epidemic spread, the behavior of percolation in such networks gives insight into infectious disease dynamics. A number of authors have studied percolation or epidemics in clustered networks, but the networks often contain preferential contacts in high degree nodes. We introduce a class of random clustered networks and a class of random unclustered networks with the same preferential mixing. Percolation in the clustered networks reduces the component sizes and increases the epidemic threshold compared to the unclustered networks.

Health-risk behaviour in Croatia.

PubMed

Bécue-Bertaut, Mónica; Kern, Josipa; Hernández-Maldonado, Maria-Luisa; Juresa, Vesna; Vuletic, Silvije

2008-02-01

To identify the health-risk behaviour of various homogeneous clusters of individuals. The study was conducted in 13 of the 20 Croatian counties and in Zagreb, the Croatian capital. In the first stage, general practices were selected in each county. The second-stage sample was created by drawing a random subsample of 10% of the patients registered at each selected general practice. The sample was divided into seven homogenous clusters using statistical methodology, combining multiple factor analysis with a hybrid clustering method. Seven homogeneous clusters were identified, three composed of males and four composed of females, based on statistically significant differences between selected characteristics (P<0.001). Although, in general, self-assessed health declined with age, significant variations were observed within specific age intervals. Higher levels of self-assessed health were associated with higher levels of education and/or socio-economic status. Many individuals, especially females, who self-reported poor health were heavy consumers of sleeping pills. Males and females reported different health-risk behaviours related to lifestyle, diet and use of the healthcare system. Heavy alcohol and tobacco use, unhealthy diet, risky physical activity and non-use of the healthcare system influenced self-assessed health in males. Females were slightly less satisfied with their health than males of the same age and educational level. Even highly educated females who took preventive healthcare tests and ate a healthy diet reported a less satisfactory self-assessed level of health than expected. Sociodemographic characteristics, life style, self-assessed health and use of the healthcare system were used in the identification of seven homogeneous population clusters. A comprehensive analysis of these clusters suggests health-related prevention and intervention efforts geared towards specific populations.

Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation

NASA Astrophysics Data System (ADS)

Li, Xiayue; Curtis, Farren S.; Rose, Timothy; Schober, Christoph; Vazquez-Mayagoitia, Alvaro; Reuter, Karsten; Oberhofer, Harald; Marom, Noa

2018-06-01

We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast energy evaluations, Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory is then used for the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor developed specifically for molecular crystals, which is shown to be robust in identifying packing motif similarity. In addition to random structure generation, Genarris offers three workflows based on different sequences of successive clustering and selection steps: the "Rigorous" workflow is an exhaustive exploration of the potential energy landscape, the "Energy" workflow produces a set of low energy structures, and the "Diverse" workflow produces a maximally diverse set of structures. The latter is recommended for generating initial populations for genetic algorithms. Here, the implementation of Genarris is reported and its application is demonstrated for three test cases.

Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic.

PubMed

Leyrat, Clémence; Caille, Agnès; Foucher, Yohann; Giraudeau, Bruno

2016-01-22

Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40% of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs.

Cluster randomized trial of text message reminders to retail staff in tanzanian drug shops dispensing artemether-lumefantrine: effect on dispenser knowledge and patient adherence.

PubMed

Bruxvoort, Katia; Festo, Charles; Kalolella, Admirabilis; Cairns, Matthew; Lyaruu, Peter; Kenani, Mitya; Kachur, S Patrick; Goodman, Catherine; Schellenberg, David

2014-10-01

Artemisinin combination therapies are available in private outlets, but patient adherence might be compromised by poor advice from dispensers. In this cluster randomized trial in drug shops in Tanzania, 42 of 82 selected shops were randomized to receive text message reminders about what advice to provide when dispensing artemether-lumefantrine (AL). Eligible patients purchasing AL at shops in both arms were followed up at home and questioned about each dose taken. Dispensers were interviewed regarding knowledge of AL dispensing practices and receipt of the malaria-related text messages. We interviewed 904 patients and 110 dispensers from 77 shops. Although there was some improvement in dispenser knowledge, there was no difference between arms in adherence measured as completion of all doses (intervention 68.3%, control 69.8%, p [adjusted] = 0.6), or as completion of each dose at the correct time (intervention 33.1%, control 32.6%, p [adjusted] = 0.9). Further studies on the potential of text messages to improve adherence are needed. © The American Society of Tropical Medicine and Hygiene.

Using Cluster Bootstrapping to Analyze Nested Data with a Few Clusters

ERIC Educational Resources Information Center

Huang, Francis L.

2018-01-01

Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials…

Intraclass Correlations for Three-Level Multi-Site Cluster-Randomized Trials of Science Achievement

ERIC Educational Resources Information Center

Westine, Carl D.

2015-01-01

A cluster-randomized trial (CRT) relies on random assignment of intact clusters to treatment conditions, such as classrooms or schools (Raudenbush & Bryk, 2002). One specific type of CRT, a multi-site CRT (MSCRT), is commonly employed in educational research and evaluation studies (Spybrook & Raudenbush, 2009; Spybrook, 2014; Bloom,…

Sydney Playground Project: A Cluster-Randomized Trial to Increase Physical Activity, Play, and Social Skills

ERIC Educational Resources Information Center

Bundy, Anita; Engelen, Lina; Wyver, Shirley; Tranter, Paul; Ragen, Jo; Bauman, Adrian; Baur, Louise; Schiller, Wendy; Simpson, Judy M.; Niehues, Anita N.; Perry, Gabrielle; Jessup, Glenda; Naughton, Geraldine

2017-01-01

Background: We assessed the effectiveness of a simple intervention for increasing children's physical activity, play, perceived competence/social acceptance, and social skills. Methods: A cluster-randomized controlled trial was conducted, in which schools were the clusters. Twelve Sydney (Australia) primary schools were randomly allocated to…

Socio-economic Status, Needs, and Utilization of Dental Services among Rural Adults in a Primary Health Center Area in Southern India

PubMed Central

Bommireddy, Vikram Simha; Pachava, Srinivas; Ravoori, Srinivas; Sanikommu, Suresh; Talluri, Devaki; Vinnakota, Narayana Rao

2014-01-01

Background: The oral disease burden in India is showing a steady increase in the recent years. Utilization of dental care being the major factor affecting the oral health status of the population is used as an important tool in oral health policy decision-making and is measured in terms of the number of dental visits per annum. Materials and Methods: A cross-sectional house to house questionnaire survey was conducted in three rural clusters which were randomly selected from a total of eight clusters served by a primary health center. Simple random sampling was used to select 100 houses from each cluster. Screening was done to examine the existing oral diseases. A total of 385 completed questionnaires were collected from 300 houses. Results: Of 385 study subjects, 183 have experienced previous dental problems. Major dental problem experienced by the study subjects was toothache (68.85%) and the treatment underwent was extraction (50.27%). Most preferred treatment centers by the study subjects were private dental hospital (68.25%) and reason identified was accessibility which constituted (45.24%) of all the reasons given. Negative attitude toward dental care is one of the important barriers; 50.8% of the non-utilizers felt dental treatment is not much important. Conclusion: Person’s attitude, lack of awareness, and affordability remain the barriers for utilization of dental services. Effective methods have to be exercised to breach such barriers. PMID:25628485

Prediction models for clustered data: comparison of a random intercept and standard regression model

PubMed Central

2013-01-01

Background When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Methods Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. Results The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. Conclusion The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters. PMID:23414436

Prediction models for clustered data: comparison of a random intercept and standard regression model.

PubMed

Bouwmeester, Walter; Twisk, Jos W R; Kappen, Teus H; van Klei, Wilton A; Moons, Karel G M; Vergouwe, Yvonne

2013-02-15

When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters.

Assessment of economic status in trauma registries: A new algorithm for generating population-specific clustering-based models of economic status for time-constrained low-resource settings.

PubMed

Eyler, Lauren; Hubbard, Alan; Juillard, Catherine

2016-10-01

Low and middle-income countries (LMICs) and the world's poor bear a disproportionate share of the global burden of injury. Data regarding disparities in injury are vital to inform injury prevention and trauma systems strengthening interventions targeted towards vulnerable populations, but are limited in LMICs. We aim to facilitate injury disparities research by generating a standardized methodology for assessing economic status in resource-limited country trauma registries where complex metrics such as income, expenditures, and wealth index are infeasible to assess. To address this need, we developed a cluster analysis-based algorithm for generating simple population-specific metrics of economic status using nationally representative Demographic and Health Surveys (DHS) household assets data. For a limited number of variables, g, our algorithm performs weighted k-medoids clustering of the population using all combinations of g asset variables and selects the combination of variables and number of clusters that maximize average silhouette width (ASW). In simulated datasets containing both randomly distributed variables and "true" population clusters defined by correlated categorical variables, the algorithm selected the correct variable combination and appropriate cluster numbers unless variable correlation was very weak. When used with 2011 Cameroonian DHS data, our algorithm identified twenty economic clusters with ASW 0.80, indicating well-defined population clusters. This economic model for assessing health disparities will be used in the new Cameroonian six-hospital centralized trauma registry. By describing our standardized methodology and algorithm for generating economic clustering models, we aim to facilitate measurement of health disparities in other trauma registries in resource-limited countries. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Community-based intermittent mass testing and treatment for malaria in an area of high transmission intensity, western Kenya: study design and methodology for a cluster randomized controlled trial.

PubMed

Samuels, Aaron M; Awino, Nobert; Odongo, Wycliffe; Abong'o, Benard; Gimnig, John; Otieno, Kephas; Shi, Ya Ping; Were, Vincent; Allen, Denise Roth; Were, Florence; Sang, Tony; Obor, David; Williamson, John; Hamel, Mary J; Patrick Kachur, S; Slutsker, Laurence; Lindblade, Kim A; Kariuki, Simon; Desai, Meghna

2017-06-07

Most human Plasmodium infections in western Kenya are asymptomatic and are believed to contribute importantly to malaria transmission. Elimination of asymptomatic infections requires active treatment approaches, such as mass testing and treatment (MTaT) or mass drug administration (MDA), as infected persons do not seek care for their infection. Evaluations of community-based approaches that are designed to reduce malaria transmission require careful attention to study design to ensure that important effects can be measured accurately. This manuscript describes the study design and methodology of a cluster-randomized controlled trial to evaluate a MTaT approach for malaria transmission reduction in an area of high malaria transmission. Ten health facilities in western Kenya were purposively selected for inclusion. The communities within 3 km of each health facility were divided into three clusters of approximately equal population size. Two clusters around each health facility were randomly assigned to the control arm, and one to the intervention arm. Three times per year for 2 years, after the long and short rains, and again before the long rains, teams of community health volunteers visited every household within the intervention arm, tested all consenting individuals with malaria rapid diagnostic tests, and treated all positive individuals with an effective anti-malarial. The effect of mass testing and treatment on malaria transmission was measured through population-based longitudinal cohorts, outpatient visits for clinical malaria, periodic population-based cross-sectional surveys, and entomological indices.

Evaluation of Tai Chi Yunshou exercises on community-based stroke patients with balance dysfunction: a study protocol of a cluster randomized controlled trial.

PubMed

Tao, Jing; Rao, Ting; Lin, Lili; Liu, Wei; Wu, Zhenkai; Zheng, Guohua; Su, Yusheng; Huang, Jia; Lin, Zhengkun; Wu, Jinsong; Fang, Yunhua; Chen, Lidian

2015-02-25

Balance dysfunction after stroke limits patients' general function and participation in daily life. Previous researches have suggested that Tai Chi exercise could offer a positive improvement in older individuals' balance function and reduce the risk of falls. But convincing evidence for the effectiveness of enhancing balance function after stroke with Tai Chi exercise is still inadequate. Considering the difficulties for stroke patients to complete the whole exercise, the current trial evaluates the benefit of Tai Chi Yunshou exercise for patients with balance dysfunction after stroke through a cluster randomization, parallel-controlled design. A single-blind, cluster-randomized, parallel-controlled trial will be conducted. A total of 10 community health centers (5 per arm) will be selected and randomly allocated into Tai Chi Yunshou exercise group or balance rehabilitation training group. Each community health centers will be asked to enroll 25 eligible patients into the trial. 60 minutes per each session, 1 session per day, 5 times per week and the total training round is 12 weeks. Primary and secondary outcomes will be measured at baseline and 4-weeks, 8-weeks, 12-weeks, 6-week follow-up, 12-week follow-up after randomization. Safety and economic evaluation will also be assessed. This protocol aims to evaluate the effectiveness of Tai Chi Yunshou exercise for the balance function of patients after stroke. If the outcome is positive, this project will provide an appropriate and economic balance rehabilitation technology for community-based stroke patients. Chinese Clinical Trial Registry: ChiCTR-TRC-13003641. Registration date: 22 August, 2013 http://www.chictr.org/usercenter/project/listbycreater.aspx .

A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

PubMed Central

Wang, Zhihao; Yi, Jing

2016-01-01

For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291

Prevalence of blindness and diabetic retinopathy in northern Jordan.

PubMed

Rabiu, Mansur M; Al Bdour, Muawyah D; Abu Ameerh, Mohammed A; Jadoon, Muhammed Z

2015-01-01

To estimate the prevalence of blindness, visual impairment, diabetes, and diabetic retinopathy in north Jordan (Irbid) using the rapid assessment of avoidable blindness and diabetic retinopathy methodology. A multistage cluster random sampling technique was used to select participants for this survey. A total of 108 clusters were selected using probability proportional to size method while subjects within the clusters were selected using compact segment method. Survey teams moved from house to house in selected segments examining residents 50 years and older until 35 participants were recruited. All eligible people underwent a standardized examination protocol, which included ophthalmic examination and random blood sugar test using digital glucometers (Accu-Chek) in their homes. Diabetic retinopathy among diabetic patients was assessed through dilated fundus examination. A total of 3638 out of the 3780 eligible participants were examined. Age- and sex-adjusted prevalence of blindness, severe visual impairment, and visual impairment with available correction were 1.33% (95% confidence interval [CI] 0.87-1.73), 1.82% (95% CI 1.35-2.25), and 9.49% (95% CI 8.26-10.74), respectively, all higher in women. Untreated cataract and diabetic retinopathy were the major causes of blindness, accounting for 46.7% and 33.2% of total blindness cases, respectively. Glaucoma was the third major cause, accounting for 8.9% of cases. The prevalence of diabetes mellitus was 28.6% (95% CI 26.9-30.3) among the study population and higher in women. The prevalence of any retinopathy among diabetic patients was 48.4%. Cataract and diabetic retinopathy are the 2 major causes of blindness and visual impairment in northern Jordan. For both conditions, women are primarily affected, suggesting possible limitations to access to services. A diabetic retinopathy screening program needs to proactively create sex-sensitive awareness and provide easily accessible screening services with prompt treatment.

Interpreting semantic clustering effects in free recall.

PubMed

Manning, Jeremy R; Kahana, Michael J

2012-07-01

The order in which participants choose to recall words from a studied list of randomly selected words provides insights into how memories of the words are represented, organised, and retrieved. One pervasive finding is that when a pair of semantically related words (e.g., "cat" and "dog") is embedded in the studied list, the related words are often recalled successively. This tendency to successively recall semantically related words is termed semantic clustering (Bousfield, 1953; Bousfield & Sedgewick, 1944; Cofer, Bruce, & Reicher, 1966). Measuring semantic clustering effects requires making assumptions about which words participants consider to be similar in meaning. However, it is often difficult to gain insights into individual participants' internal semantic models, and for this reason researchers typically rely on standardised semantic similarity metrics. Here we use simulations to gain insights into the expected magnitudes of semantic clustering effects given systematic differences between participants' internal similarity models and the similarity metric used to quantify the degree of semantic clustering. Our results provide a number of useful insights into the interpretation of semantic clustering effects in free recall.

Ferromagnetic clusters induced by a nonmagnetic random disorder in diluted magnetic semiconductors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bui, Dinh-Hoi; Physics Department, Hue University’s College of Education, 34 Le Loi, Hue; Phan, Van-Nham, E-mail: phanvannham@dtu.edu.vn

In this work, we analyze the nonmagnetic random disorder leading to a formation of ferromagnetic clusters in diluted magnetic semiconductors. The nonmagnetic random disorder arises from randomness in the host lattice. Including the disorder to the Kondo lattice model with random distribution of magnetic dopants, the ferromagnetic–paramagnetic transition in the system is investigated in the framework of dynamical mean-field theory. At a certain low temperature one finds a fraction of ferromagnetic sites transiting to the paramagnetic state. Enlarging the nonmagnetic random disorder strength, the paramagnetic regimes expand resulting in the formation of the ferromagnetic clusters.

Under What Circumstances Does External Knowledge about the Correlation Structure Improve Power in Cluster Randomized Designs?

ERIC Educational Resources Information Center

Rhoads, Christopher

2014-01-01

Recent publications have drawn attention to the idea of utilizing prior information about the correlation structure to improve statistical power in cluster randomized experiments. Because power in cluster randomized designs is a function of many different parameters, it has been difficult for applied researchers to discern a simple rule explaining…

How large are the consequences of covariate imbalance in cluster randomized trials: a simulation study with a continuous outcome and a binary covariate at the cluster level.

PubMed

Moerbeek, Mirjam; van Schie, Sander

2016-07-11

The number of clusters in a cluster randomized trial is often low. It is therefore likely random assignment of clusters to treatment conditions results in covariate imbalance. There are no studies that quantify the consequences of covariate imbalance in cluster randomized trials on parameter and standard error bias and on power to detect treatment effects. The consequences of covariance imbalance in unadjusted and adjusted linear mixed models are investigated by means of a simulation study. The factors in this study are the degree of imbalance, the covariate effect size, the cluster size and the intraclass correlation coefficient. The covariate is binary and measured at the cluster level; the outcome is continuous and measured at the individual level. The results show covariate imbalance results in negligible parameter bias and small standard error bias in adjusted linear mixed models. Ignoring the possibility of covariate imbalance while calculating the sample size at the cluster level may result in a loss in power of at most 25 % in the adjusted linear mixed model. The results are more severe for the unadjusted linear mixed model: parameter biases up to 100 % and standard error biases up to 200 % may be observed. Power levels based on the unadjusted linear mixed model are often too low. The consequences are most severe for large clusters and/or small intraclass correlation coefficients since then the required number of clusters to achieve a desired power level is smallest. The possibility of covariate imbalance should be taken into account while calculating the sample size of a cluster randomized trial. Otherwise more sophisticated methods to randomize clusters to treatments should be used, such as stratification or balance algorithms. All relevant covariates should be carefully identified, be actually measured and included in the statistical model to avoid severe levels of parameter and standard error bias and insufficient power levels.

Pure-phase selective excitation in fast-relaxing systems.

PubMed

Zangger, K; Oberer, M; Sterk, H

2001-09-01

Selective pulses have been used frequently for small molecules. However, their application to proteins and other macromolecules has been limited. The long duration of shaped-selective pulses and the short T(2) relaxation times in proteins often prohibited the use of highly selective pulses especially on larger biomolecules. A very selective excitation can be obtained within a short time by using the selective excitation sequence presented in this paper. Instead of using a shaped low-intensity radiofrequency pulse, a cluster of hard 90 degrees pulses, delays of free precession, and pulsed field gradients can be used to selectively excite a narrow chemical shift range within a relatively short time. Thereby, off-resonance magnetization, which is allowed to evolve freely during the free precession intervals, is destroyed by the gradient pulses. Off-resonance excitation artifacts can be removed by random variation of the interpulse delays. This leads to an excitation profile with selectivity as well as phase and relaxation behavior superior to that of commonly used shaped-selective pulses. Since the evolution of scalar coupling is inherently suppressed during the double-selective excitation of two different scalar-coupled nuclei, the presented pulse cluster is especially suited for simultaneous highly selective excitation of N-H and C-H fragments. Experimental examples are demonstrated on hen egg white lysozyme (14 kD) and the bacterial antidote ParD (19 kD). Copyright 2001 Academic Press.

Using estimates of natural variation to detect ecologically important change in forest spatial patterns: a case study, Cascade Range, eastern Washington.

Treesearch

Paul F. Hessburg; Bradley G. Smith; R. Brion Salter

1999-01-01

Using hierarchical clustering techniques, we grouped subwatersheds on the eastern slope of the Cascade Range in Washington State into ecological subregions by similarity of area in potential vegetation and climate attributes. We then built spatially continuous historical and current vegetation maps for 48 randomly selected subwatersheds from interpretations of 1938-49...

A Framework for Designing Cluster Randomized Trials with Binary Outcomes

ERIC Educational Resources Information Center

Spybrook, Jessaca; Martinez, Andres

2011-01-01

The purpose of this paper is to provide a frame work for approaching a power analysis for a CRT (cluster randomized trial) with a binary outcome. The authors suggest a framework in the context of a simple CRT and then extend it to a blocked design, or a multi-site cluster randomized trial (MSCRT). The framework is based on proportions, an…

Understanding Statistical Power in Cluster Randomized Trials: Challenges Posed by Differences in Notation and Terminology

ERIC Educational Resources Information Center

Spybrook, Jessaca; Hedges, Larry; Borenstein, Michael

2014-01-01

Research designs in which clusters are the unit of randomization are quite common in the social sciences. Given the multilevel nature of these studies, the power analyses for these studies are more complex than in a simple individually randomized trial. Tools are now available to help researchers conduct power analyses for cluster randomized…

A Comparison of Single Sample and Bootstrap Methods to Assess Mediation in Cluster Randomized Trials

ERIC Educational Resources Information Center

Pituch, Keenan A.; Stapleton, Laura M.; Kang, Joo Youn

2006-01-01

A Monte Carlo study examined the statistical performance of single sample and bootstrap methods that can be used to test and form confidence interval estimates of indirect effects in two cluster randomized experimental designs. The designs were similar in that they featured random assignment of clusters to one of two treatment conditions and…

The Spatial Distribution of the Young Stellar Clusters in the Star-forming Galaxy NGC 628

NASA Astrophysics Data System (ADS)

Grasha, K.; Calzetti, D.; Adamo, A.; Kim, H.; Elmegreen, B. G.; Gouliermis, D. A.; Aloisi, A.; Bright, S. N.; Christian, C.; Cignoni, M.; Dale, D. A.; Dobbs, C.; Elmegreen, D. M.; Fumagalli, M.; Gallagher, J. S., III; Grebel, E. K.; Johnson, K. E.; Lee, J. C.; Messa, M.; Smith, L. J.; Ryon, J. E.; Thilker, D.; Ubeda, L.; Wofford, A.

2015-12-01

We present a study of the spatial distribution of the stellar cluster populations in the star-forming galaxy NGC 628. Using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey), we have identified 1392 potential young (≲ 100 Myr) stellar clusters within the galaxy using a combination of visual inspection and automatic selection. We investigate the clustering of these young stellar clusters and quantify the strength and change of clustering strength with scale using the two-point correlation function. We also investigate how image boundary conditions and dust lanes affect the observed clustering. The distribution of the clusters is well fit by a broken power law with negative exponent α. We recover a weighted mean index of α ∼ -0.8 for all spatial scales below the break at 3.″3 (158 pc at a distance of 9.9 Mpc) and an index of α ∼ -0.18 above 158 pc for the accumulation of all cluster types. The strength of the clustering increases with decreasing age and clusters older than 40 Myr lose their clustered structure very rapidly and tend to be randomly distributed in this galaxy, whereas the mass of the star cluster has little effect on the clustering strength. This is consistent with results from other studies that the morphological hierarchy in stellar clustering resembles the same hierarchy as the turbulent interstellar medium.

Merger types forming the Virgo cluster in recent gigayears

NASA Astrophysics Data System (ADS)

Olchanski, M.; Sorce, J. G.

2018-06-01

Context. As our closest cluster-neighbor, the Virgo cluster of galaxies is intensely studied by observers to unravel the mysteries of galaxy evolution within clusters. At this stage, cosmological numerical simulations of the cluster are useful to efficiently test theories and calibrate models. However, it is not trivial to select the perfect simulacrum of the Virgo cluster to fairly compare in detail its observed and simulated galaxy populations that are affected by the type and history of the cluster. Aims: Determining precisely the properties of Virgo for a later selection of simulated clusters becomes essential. It is still not clear how to access some of these properties, such as the past history of the Virgo cluster from current observations. Therefore, directly producing effective simulacra of the Virgo cluster is inevitable. Methods: Efficient simulacra of the Virgo cluster can be obtained via simulations that resemble the local Universe down to the cluster scale. In such simulations, Virgo-like halos form in the proper local environment and permit assessing the most probable formation history of the cluster. Studies based on these simulations have already revealed that the Virgo cluster has had a quiet merging history over the last seven gigayears and that the cluster accretes matter along a preferential direction. Results: This paper reveals that in addition such Virgo halos have had on average only one merger larger than about a tenth of their mass at redshift zero within the last four gigayears. This second branch (by opposition to main branch) formed in a given sub-region and merged recently (within the last gigayear). These properties are not shared with a set of random halos within the same mass range. Conclusions: This study extends the validity of the scheme used to produce the Virgo simulacra down to the largest sub-halos of the Virgo cluster. It opens up great prospects for detailed comparisons with observations, including substructures and markers of past history, to be conducted with a large sample of high resolution "Virgos" and including baryons, in the near future.

ODE, RDE and SDE models of cell cycle dynamics and clustering in yeast.

PubMed

Boczko, Erik M; Gedeon, Tomas; Stowers, Chris C; Young, Todd R

2010-07-01

Biologists have long observed periodic-like oxygen consumption oscillations in yeast populations under certain conditions, and several unsatisfactory explanations for this phenomenon have been proposed. These ‘autonomous oscillations’ have often appeared with periods that are nearly integer divisors of the calculated doubling time of the culture. We hypothesize that these oscillations could be caused by a form of cell cycle synchronization that we call clustering. We develop some novel ordinary differential equation models of the cell cycle. For these models, and for random and stochastic perturbations, we give both rigorous proofs and simulations showing that both positive and negative growth rate feedback within the cell cycle are possible agents that can cause clustering of populations within the cell cycle. It occurs for a variety of models and for a broad selection of parameter values. These results suggest that the clustering phenomenon is robust and is likely to be observed in nature. Since there are necessarily an integer number of clusters, clustering would lead to periodic-like behaviour with periods that are nearly integer divisors of the period of the cell cycle. Related experiments have shown conclusively that cell cycle clustering occurs in some oscillating yeast cultures.

The Aged Residential Care Healthcare Utilization Study (ARCHUS): a multidisciplinary, cluster randomized controlled trial designed to reduce acute avoidable hospitalizations from long-term care facilities.

PubMed

Connolly, Martin J; Boyd, Michal; Broad, Joanna B; Kerse, Ngaire; Lumley, Thomas; Whitehead, Noeline; Foster, Susan

2015-01-01

To assess effect of a complex, multidisciplinary intervention aimed at reducing avoidable acute hospitalization of residents of residential aged care (RAC) facilities. Cluster randomized controlled trial. RAC facilities with higher than expected hospitalizations in Auckland, New Zealand, were recruited and randomized to intervention or control. A total of 1998 residents of 18 intervention facilities and 18 control facilities. A facility-based complex intervention of 9 months' duration. The intervention comprised gerontology nurse specialist (GNS)-led staff education, facility bench-marking, GNS resident review, and multidisciplinary (geriatrician, primary-care physician, pharmacist, GNS, and facility nurse) discussion of residents selected using standard criteria. Primary end point was avoidable hospitalizations. Secondary end points were all acute admissions, mortality, and acute bed-days. Follow-up was for a total of 14 months. The intervention did not affect main study end points: number of acute avoidable hospital admissions (RR 1.07; 95% CI 0.85-1.36; P = .59) or mortality (RR 1.11; 95% CI 0.76-1.61; P = .62). This multidisciplinary intervention, packaging selected case review, and staff education had no overall impact on acute hospital admissions or mortality. This may have considerable implications for resourcing in the acute and RAC sectors in the face of population aging. Australian and New Zealand Clinical Trials Registry (ACTRN12611000187943). Copyright © 2015 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.

Secure Cluster Head Sensor Elections Using Signal Strength Estimation and Ordered Transmissions

PubMed Central

Wang, Gicheol; Cho, Gihwan

2009-01-01

In clustered sensor networks, electing CHs (Cluster Heads) in a secure manner is very important because they collect data from sensors and send the aggregated data to the sink. If a compromised node is elected as a CH, it can illegally acquire data from all the members and even send forged data to the sink. Nevertheless, most of the existing CH election schemes have not treated the problem of the secure CH election. Recently, random value based protocols have been proposed to resolve the secure CH election problem. However, these schemes cannot prevent an attacker from suppressing its contribution for the change of CH election result and from selectively forwarding its contribution for the disagreement of CH election result. In this paper, we propose a modified random value scheme to prevent these disturbances. Our scheme dynamically adjusts the forwarding order of contributions and discards a received contribution when its signal strength is lower than the specified level to prevent these malicious actions. The simulation results have shown that our scheme effectively prevents attackers from changing and splitting an agreement of CH election result. Also, they have shown that our scheme is relatively energy-efficient than other schemes. PMID:22408550

Climate-driven extinctions shape the phylogenetic structure of temperate tree floras.

PubMed

Eiserhardt, Wolf L; Borchsenius, Finn; Plum, Christoffer M; Ordonez, Alejandro; Svenning, Jens-Christian

2015-03-01

When taxa go extinct, unique evolutionary history is lost. If extinction is selective, and the intrinsic vulnerabilities of taxa show phylogenetic signal, more evolutionary history may be lost than expected under random extinction. Under what conditions this occurs is insufficiently known. We show that late Cenozoic climate change induced phylogenetically selective regional extinction of northern temperate trees because of phylogenetic signal in cold tolerance, leading to significantly and substantially larger than random losses of phylogenetic diversity (PD). The surviving floras in regions that experienced stronger extinction are phylogenetically more clustered, indicating that non-random losses of PD are of increasing concern with increasing extinction severity. Using simulations, we show that a simple threshold model of survival given a physiological trait with phylogenetic signal reproduces our findings. Our results send a strong warning that we may expect future assemblages to be phylogenetically and possibly functionally depauperate if anthropogenic climate change affects taxa similarly. © 2015 John Wiley & Sons Ltd/CNRS.

New Estimates of Design Parameters for Clustered Randomization Studies: Findings from North Carolina and Florida. Working Paper 43

ERIC Educational Resources Information Center

Xu, Zeyu; Nichols, Austin

2010-01-01

The gold standard in making causal inference on program effects is a randomized trial. Most randomization designs in education randomize classrooms or schools rather than individual students. Such "clustered randomization" designs have one principal drawback: They tend to have limited statistical power or precision. This study aims to…

Quantifying opening-mode fracture spatial organization in horizontal wellbore image logs, core and outcrop: Application to Upper Cretaceous Frontier Formation tight gas sandstones, USA

NASA Astrophysics Data System (ADS)

Li, J. Z.; Laubach, S. E.; Gale, J. F. W.; Marrett, R. A.

2018-03-01

The Upper Cretaceous Frontier Formation is a naturally fractured gas-producing sandstone in Wyoming. Regionally, random and statistically more clustered than random patterns exist in the same upper to lower shoreface depositional facies. East-west- and north-south-striking regional fractures sampled using image logs and cores from three horizontal wells exhibit clustered patterns, whereas data collected from east-west-striking fractures in outcrop have patterns that are indistinguishable from random. Image log data analyzed with the correlation count method shows clusters ∼35 m wide and spaced ∼50 to 90 m apart as well as clusters up to 12 m wide with periodic inter-cluster spacings. A hierarchy of cluster sizes exists; organization within clusters is likely fractal. These rocks have markedly different structural and burial histories, so regional differences in degree of clustering are unsurprising. Clustered patterns correspond to fractures having core quartz deposition contemporaneous with fracture opening, circumstances that some models suggest might affect spacing patterns by interfering with fracture growth. Our results show that quantifying and identifying patterns as statistically more or less clustered than random delineates differences in fracture patterns that are not otherwise apparent but that may influence gas and water production, and therefore may be economically important.

When is informed consent required in cluster randomized trials in health research?

PubMed Central

2011-01-01

This article is part of a series of papers examining ethical issues in cluster randomized trials (CRTs) in health research. In the introductory paper in this series, we set out six areas of inquiry that must be addressed if the cluster trial is to be set on a firm ethical foundation. This paper addresses the second of the questions posed, namely, from whom, when, and how must informed consent be obtained in CRTs in health research? The ethical principle of respect for persons implies that researchers are generally obligated to obtain the informed consent of research subjects. Aspects of CRT design, including cluster randomization, cluster level interventions, and cluster size, present challenges to obtaining informed consent. Here we address five questions related to consent and CRTs: How can a study proceed if informed consent is not possible? Is consent to randomization always required? What information must be disclosed to potential subjects if their cluster has already been randomized? Is passive consent a valid substitute for informed consent? Do health professionals have a moral obligation to participate as subjects in CRTs designed to improve professional practice? We set out a framework based on the moral foundations of informed consent and international regulatory provisions to address each of these questions. First, when informed consent is not possible, a study may proceed if a research ethics committee is satisfied that conditions for a waiver of consent are satisfied. Second, informed consent to randomization may not be required if it is not possible to approach subjects at the time of randomization. Third, when potential subjects are approached after cluster randomization, they must be provided with a detailed description of the interventions in the trial arm to which their cluster has been randomized; detailed information on interventions in other trial arms need not be provided. Fourth, while passive consent may serve a variety of practical ends, it is not a substitute for valid informed consent. Fifth, while health professionals may have a moral obligation to participate as subjects in research, this does not diminish the necessity of informed consent to study participation. PMID:21906277

Measurement Error Correction Formula for Cluster-Level Group Differences in Cluster Randomized and Observational Studies

ERIC Educational Resources Information Center

Cho, Sun-Joo; Preacher, Kristopher J.

2016-01-01

Multilevel modeling (MLM) is frequently used to detect cluster-level group differences in cluster randomized trial and observational studies. Group differences on the outcomes (posttest scores) are detected by controlling for the covariate (pretest scores) as a proxy variable for unobserved factors that predict future attributes. The pretest and…

Handling Correlations between Covariates and Random Slopes in Multilevel Models

ERIC Educational Resources Information Center

Bates, Michael David; Castellano, Katherine E.; Rabe-Hesketh, Sophia; Skrondal, Anders

2014-01-01

This article discusses estimation of multilevel/hierarchical linear models that include cluster-level random intercepts and random slopes. Viewing the models as structural, the random intercepts and slopes represent the effects of omitted cluster-level covariates that may be correlated with included covariates. The resulting correlations between…

A pattern-mixture model approach for handling missing continuous outcome data in longitudinal cluster randomized trials.

PubMed

Fiero, Mallorie H; Hsu, Chiu-Hsieh; Bell, Melanie L

2017-11-20

We extend the pattern-mixture approach to handle missing continuous outcome data in longitudinal cluster randomized trials, which randomize groups of individuals to treatment arms, rather than the individuals themselves. Individuals who drop out at the same time point are grouped into the same dropout pattern. We approach extrapolation of the pattern-mixture model by applying multilevel multiple imputation, which imputes missing values while appropriately accounting for the hierarchical data structure found in cluster randomized trials. To assess parameters of interest under various missing data assumptions, imputed values are multiplied by a sensitivity parameter, k, which increases or decreases imputed values. Using simulated data, we show that estimates of parameters of interest can vary widely under differing missing data assumptions. We conduct a sensitivity analysis using real data from a cluster randomized trial by increasing k until the treatment effect inference changes. By performing a sensitivity analysis for missing data, researchers can assess whether certain missing data assumptions are reasonable for their cluster randomized trial. Copyright © 2017 John Wiley & Sons, Ltd.

Composing Music with Complex Networks

NASA Astrophysics Data System (ADS)

Liu, Xiaofan; Tse, Chi K.; Small, Michael

In this paper we study the network structure in music and attempt to compose music artificially. Networks are constructed with nodes and edges corresponding to musical notes and their co-occurrences. We analyze sample compositions from Bach, Mozart, Chopin, as well as other types of music including Chinese pop music. We observe remarkably similar properties in all networks constructed from the selected compositions. Power-law exponents of degree distributions, mean degrees, clustering coefficients, mean geodesic distances, etc. are reported. With the network constructed, music can be created by using a biased random walk algorithm, which begins with a randomly chosen note and selects the subsequent notes according to a simple set of rules that compares the weights of the edges, weights of the nodes, and/or the degrees of nodes. The newly created music from complex networks will be played in the presentation.

Methods for sample size determination in cluster randomized trials

PubMed Central

Rutterford, Clare; Copas, Andrew; Eldridge, Sandra

2015-01-01

Background: The use of cluster randomized trials (CRTs) is increasing, along with the variety in their design and analysis. The simplest approach for their sample size calculation is to calculate the sample size assuming individual randomization and inflate this by a design effect to account for randomization by cluster. The assumptions of a simple design effect may not always be met; alternative or more complicated approaches are required. Methods: We summarise a wide range of sample size methods available for cluster randomized trials. For those familiar with sample size calculations for individually randomized trials but with less experience in the clustered case, this manuscript provides formulae for a wide range of scenarios with associated explanation and recommendations. For those with more experience, comprehensive summaries are provided that allow quick identification of methods for a given design, outcome and analysis method. Results: We present first those methods applicable to the simplest two-arm, parallel group, completely randomized design followed by methods that incorporate deviations from this design such as: variability in cluster sizes; attrition; non-compliance; or the inclusion of baseline covariates or repeated measures. The paper concludes with methods for alternative designs. Conclusions: There is a large amount of methodology available for sample size calculations in CRTs. This paper gives the most comprehensive description of published methodology for sample size calculation and provides an important resource for those designing these trials. PMID:26174515

Search for Directed Networks by Different Random Walk Strategies

NASA Astrophysics Data System (ADS)

Zhu, Zi-Qi; Jin, Xiao-Ling; Huang, Zhi-Long

2012-03-01

A comparative study is carried out on the efficiency of five different random walk strategies searching on directed networks constructed based on several typical complex networks. Due to the difference in search efficiency of the strategies rooted in network clustering, the clustering coefficient in a random walker's eye on directed networks is defined and computed to be half of the corresponding undirected networks. The search processes are performed on the directed networks based on Erdös—Rényi model, Watts—Strogatz model, Barabási—Albert model and clustered scale-free network model. It is found that self-avoiding random walk strategy is the best search strategy for such directed networks. Compared to unrestricted random walk strategy, path-iteration-avoiding random walks can also make the search process much more efficient. However, no-triangle-loop and no-quadrangle-loop random walks do not improve the search efficiency as expected, which is different from those on undirected networks since the clustering coefficient of directed networks are smaller than that of undirected networks.

Determining the efficacy of guppies and pyriproxyfen (Sumilarv® 2MR) combined with community engagement on dengue vectors in Cambodia: study protocol for a randomized controlled trial.

PubMed

Hustedt, John; Doum, Dyna; Keo, Vanney; Ly, Sokha; Sam, BunLeng; Chan, Vibol; Alexander, Neal; Bradley, John; Prasetyo, Didot Budi; Rachmat, Agus; Muhammad, Shafique; Lopes, Sergio; Leang, Rithea; Hii, Jeffrey

2017-08-04

Evidence on the effectiveness of low-cost, sustainable, biological vector-control tools for the Aedes mosquitoes is limited. Therefore, the purpose of this trial is to estimate the impact of guppy fish (guppies), in combination with the use of the larvicide pyriproxyfen (Sumilarv® 2MR), and Communication for Behavioral Impact (COMBI) activities to reduce entomological indices in Cambodia. In this cluster randomized controlled, superiority trial, 30 clusters comprising one or more villages each (with approximately 170 households) will be allocated, in a 1:1:1 ratio, to receive either (1) three interventions (guppies, Sumilarv® 2MR, and COMBI activities), (2) two interventions (guppies and COMBI activities), or (3) control (standard vector control). Households will be invited to participate, and entomology surveys among 40 randomly selected households per cluster will be carried out quarterly. The primary outcome will be the population density of adult female Aedes mosquitoes (i.e., number per house) trapped using adult resting collections. Secondary outcome measures will include the House Index, Container Index, Breteau Index, Pupae Per House, Pupae Per Person, mosquito infection rate, guppy fish coverage, Sumilarv® 2MR coverage, and percentage of respondents with knowledge about Aedes mosquitoes causing dengue. In the primary analysis, adult female Aedes density and mosquito infection rates will be aggregated over follow-up time points to give a single rate per cluster. This will be analyzed by negative binomial regression, yielding density ratios. This trial is expected to provide robust estimates of the intervention effect. A rigorous evaluation of these vector-control interventions is vital to developing an evidence-based dengue control strategy and to help direct government resources. Current Controlled Trials, ID: ISRCTN85307778 . Registered on 25 October 2015.

Combined universal and selective prevention for adolescent alcohol use: a cluster randomized controlled trial.

PubMed

Teesson, M; Newton, N C; Slade, T; Carragher, N; Barrett, E L; Champion, K E; Kelly, E V; Nair, N K; Stapinski, L A; Conrod, P J

2017-07-01

No existing models of alcohol prevention concurrently adopt universal and selective approaches. This study aims to evaluate the first combined universal and selective approach to alcohol prevention. A total of 26 Australian schools with 2190 students (mean age: 13.3 years) were randomized to receive: universal prevention (Climate Schools); selective prevention (Preventure); combined prevention (Climate Schools and Preventure; CAP); or health education as usual (control). Primary outcomes were alcohol use, binge drinking and alcohol-related harms at 6, 12 and 24 months. Climate, Preventure and CAP students demonstrated significantly lower growth in their likelihood to drink and binge drink, relative to controls over 24 months. Preventure students displayed significantly lower growth in their likelihood to experience alcohol harms, relative to controls. While adolescents in both the CAP and Climate groups demonstrated slower growth in drinking compared with adolescents in the control group over the 2-year study period, CAP adolescents demonstrated faster growth in drinking compared with Climate adolescents. Findings support universal, selective and combined approaches to alcohol prevention. Particularly novel are the findings of no advantage of the combined approach over universal or selective prevention alone.

Cluster Tails for Critical Power-Law Inhomogeneous Random Graphs

NASA Astrophysics Data System (ADS)

van der Hofstad, Remco; Kliem, Sandra; van Leeuwaarden, Johan S. H.

2018-04-01

Recently, the scaling limit of cluster sizes for critical inhomogeneous random graphs of rank-1 type having finite variance but infinite third moment degrees was obtained in Bhamidi et al. (Ann Probab 40:2299-2361, 2012). It was proved that when the degrees obey a power law with exponent τ \\in (3,4), the sequence of clusters ordered in decreasing size and multiplied through by n^{-(τ -2)/(τ -1)} converges as n→ ∞ to a sequence of decreasing non-degenerate random variables. Here, we study the tails of the limit of the rescaled largest cluster, i.e., the probability that the scaling limit of the largest cluster takes a large value u, as a function of u. This extends a related result of Pittel (J Combin Theory Ser B 82(2):237-269, 2001) for the Erdős-Rényi random graph to the setting of rank-1 inhomogeneous random graphs with infinite third moment degrees. We make use of delicate large deviations and weak convergence arguments.

Multiple Imputation in Two-Stage Cluster Samples Using The Weighted Finite Population Bayesian Bootstrap.

PubMed

Zhou, Hanzhi; Elliott, Michael R; Raghunathan, Trivellore E

2016-06-01

Multistage sampling is often employed in survey samples for cost and convenience. However, accounting for clustering features when generating datasets for multiple imputation is a nontrivial task, particularly when, as is often the case, cluster sampling is accompanied by unequal probabilities of selection, necessitating case weights. Thus, multiple imputation often ignores complex sample designs and assumes simple random sampling when generating imputations, even though failing to account for complex sample design features is known to yield biased estimates and confidence intervals that have incorrect nominal coverage. In this article, we extend a recently developed, weighted, finite-population Bayesian bootstrap procedure to generate synthetic populations conditional on complex sample design data that can be treated as simple random samples at the imputation stage, obviating the need to directly model design features for imputation. We develop two forms of this method: one where the probabilities of selection are known at the first and second stages of the design, and the other, more common in public use files, where only the final weight based on the product of the two probabilities is known. We show that this method has advantages in terms of bias, mean square error, and coverage properties over methods where sample designs are ignored, with little loss in efficiency, even when compared with correct fully parametric models. An application is made using the National Automotive Sampling System Crashworthiness Data System, a multistage, unequal probability sample of U.S. passenger vehicle crashes, which suffers from a substantial amount of missing data in "Delta-V," a key crash severity measure.

Multiple Imputation in Two-Stage Cluster Samples Using The Weighted Finite Population Bayesian Bootstrap

PubMed Central

Zhou, Hanzhi; Elliott, Michael R.; Raghunathan, Trivellore E.

2017-01-01

Multistage sampling is often employed in survey samples for cost and convenience. However, accounting for clustering features when generating datasets for multiple imputation is a nontrivial task, particularly when, as is often the case, cluster sampling is accompanied by unequal probabilities of selection, necessitating case weights. Thus, multiple imputation often ignores complex sample designs and assumes simple random sampling when generating imputations, even though failing to account for complex sample design features is known to yield biased estimates and confidence intervals that have incorrect nominal coverage. In this article, we extend a recently developed, weighted, finite-population Bayesian bootstrap procedure to generate synthetic populations conditional on complex sample design data that can be treated as simple random samples at the imputation stage, obviating the need to directly model design features for imputation. We develop two forms of this method: one where the probabilities of selection are known at the first and second stages of the design, and the other, more common in public use files, where only the final weight based on the product of the two probabilities is known. We show that this method has advantages in terms of bias, mean square error, and coverage properties over methods where sample designs are ignored, with little loss in efficiency, even when compared with correct fully parametric models. An application is made using the National Automotive Sampling System Crashworthiness Data System, a multistage, unequal probability sample of U.S. passenger vehicle crashes, which suffers from a substantial amount of missing data in “Delta-V,” a key crash severity measure. PMID:29226161

Assortativity and leadership emerge from anti-preferential attachment in heterogeneous networks.

PubMed

Sendiña-Nadal, I; Danziger, M M; Wang, Z; Havlin, S; Boccaletti, S

2016-02-18

Real-world networks have distinct topologies, with marked deviations from purely random networks. Many of them exhibit degree-assortativity, with nodes of similar degree more likely to link to one another. Though microscopic mechanisms have been suggested for the emergence of other topological features, assortativity has proven elusive. Assortativity can be artificially implanted in a network via degree-preserving link permutations, however this destroys the graph's hierarchical clustering and does not correspond to any microscopic mechanism. Here, we propose the first generative model which creates heterogeneous networks with scale-free-like properties in degree and clustering distributions and tunable realistic assortativity. Two distinct populations of nodes are incrementally added to an initial network by selecting a subgraph to connect to at random. One population (the followers) follows preferential attachment, while the other population (the potential leaders) connects via anti-preferential attachment: they link to lower degree nodes when added to the network. By selecting the lower degree nodes, the potential leader nodes maintain high visibility during the growth process, eventually growing into hubs. The evolution of links in Facebook empirically validates the connection between the initial anti-preferential attachment and long term high degree. In this way, our work sheds new light on the structure and evolution of social networks.

Assortativity and leadership emerge from anti-preferential attachment in heterogeneous networks

NASA Astrophysics Data System (ADS)

Sendiña-Nadal, I.; Danziger, M. M.; Wang, Z.; Havlin, S.; Boccaletti, S.

2016-02-01

Real-world networks have distinct topologies, with marked deviations from purely random networks. Many of them exhibit degree-assortativity, with nodes of similar degree more likely to link to one another. Though microscopic mechanisms have been suggested for the emergence of other topological features, assortativity has proven elusive. Assortativity can be artificially implanted in a network via degree-preserving link permutations, however this destroys the graph’s hierarchical clustering and does not correspond to any microscopic mechanism. Here, we propose the first generative model which creates heterogeneous networks with scale-free-like properties in degree and clustering distributions and tunable realistic assortativity. Two distinct populations of nodes are incrementally added to an initial network by selecting a subgraph to connect to at random. One population (the followers) follows preferential attachment, while the other population (the potential leaders) connects via anti-preferential attachment: they link to lower degree nodes when added to the network. By selecting the lower degree nodes, the potential leader nodes maintain high visibility during the growth process, eventually growing into hubs. The evolution of links in Facebook empirically validates the connection between the initial anti-preferential attachment and long term high degree. In this way, our work sheds new light on the structure and evolution of social networks.

Prevalence of latent tuberculous infection among adults in the general population of Ca Mau, Viet Nam.

PubMed

Marks, G B; Nhung, N V; Nguyen, T A; Hoa, N B; Khoa, T H; Son, N V; Phuong, N T B; Tin, D M; Ho, J; Fox, G J

2018-03-01

The study was conducted in a randomly selected sample of persons aged 15 years living in Ca Mau Province, southern Viet Nam. To estimate the prevalence of latent tuberculous infection (LTBI) in the general adult population of this province of Viet Nam. The secondary objective was to examine age and sex differences in prevalence. A cross-sectional survey was conducted in a cluster-random sample of the population. Clusters were subcommunes. The presence of LTBI was assessed using the QuantiFERON®-TB Gold In-Tube test system. QuantiFERON tests were performed among 1319 persons aged 15 years (77.7% of those selected). The overall prevalence of positive tests was 36.8% (95%CI 33.4-40.3). The prevalence of a positive test was lower in females than in males (31.0% vs. 44.7%, OR 0.57, 95%CI 0.45-0.72, P < 0.0001). The prevalence of positive tests increased with increasing age quintile (P < 0.0001). More than one third of the general adult population in a province in southern Viet Nam have evidence of LTBI. Although LTBI prevalence is higher in males, the sex difference is not as great as that for TB notification rates.

Refining a complex diagnostic construct: subtyping Dysthymia with the Shedler-Westen Assessment Procedure-II.

PubMed

Huprich, Steven K; Defife, Jared; Westen, Drew

2014-01-01

We sought to determine whether meaningful subtypes of Dysthymic patients could be identified when grouping them by similar personality profiles. A random, national sample of psychiatrists and clinical psychologists (n=1201) described a randomly selected current patient with personality pathology using the descriptors in the Shedler-Westen Assessment Procedure-II (SWAP-II), completed assessments of patients' adaptive functioning, and provided DSM-IV Axis I and II diagnoses. We applied Q-factor cluster analyses to those patients diagnosed with Dysthymic Disorder. Four clusters were identified-High Functioning, Anxious/Dysphoric, Emotionally Dysregulated, and Narcissistic. These factor scores corresponded with a priori hypotheses regarding diagnostic comorbidity and level of adaptive functioning. We compared these groups to diagnostic constructs described and empirically identified in the past literature. The results converge with past and current ideas about the ways in which chronic depression and personality are related and offer an enhanced means by which to understand a heterogeneous diagnostic category that is empirically grounded and clinically useful. © 2013 Published by Elsevier B.V.

Person mobility in the design and analysis of cluster-randomized cohort prevention trials.

PubMed

Vuchinich, Sam; Flay, Brian R; Aber, Lawrence; Bickman, Leonard

2012-06-01

Person mobility is an inescapable fact of life for most cluster-randomized (e.g., schools, hospitals, clinic, cities, state) cohort prevention trials. Mobility rates are an important substantive consideration in estimating the effects of an intervention. In cluster-randomized trials, mobility rates are often correlated with ethnicity, poverty and other variables associated with disparity. This raises the possibility that estimated intervention effects may generalize to only the least mobile segments of a population and, thus, create a threat to external validity. Such mobility can also create threats to the internal validity of conclusions from randomized trials. Researchers must decide how to deal with persons who leave study clusters during a trial (dropouts), persons and clusters that do not comply with an assigned intervention, and persons who enter clusters during a trial (late entrants), in addition to the persons who remain for the duration of a trial (stayers). Statistical techniques alone cannot solve the key issues of internal and external validity raised by the phenomenon of person mobility. This commentary presents a systematic, Campbellian-type analysis of person mobility in cluster-randomized cohort prevention trials. It describes four approaches for dealing with dropouts, late entrants and stayers with respect to data collection, analysis and generalizability. The questions at issue are: 1) From whom should data be collected at each wave of data collection? 2) Which cases should be included in the analyses of an intervention effect? and 3) To what populations can trial results be generalized? The conclusions lead to recommendations for the design and analysis of future cluster-randomized cohort prevention trials.

The median hazard ratio: a useful measure of variance and general contextual effects in multilevel survival analysis.

PubMed

Austin, Peter C; Wagner, Philippe; Merlo, Juan

2017-03-15

Multilevel data occurs frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models (MLRM). MLRM incorporate cluster-specific random effects which allow one to partition the total individual variance into between-cluster variation and between-individual variation. Statistically, MLRM account for the dependency of the data within clusters and provide correct estimates of uncertainty around regression coefficients. Substantively, the magnitude of the effect of clustering provides a measure of the General Contextual Effect (GCE). When outcomes are binary, the GCE can also be quantified by measures of heterogeneity like the Median Odds Ratio (MOR) calculated from a multilevel logistic regression model. Time-to-event outcomes within a multilevel structure occur commonly in epidemiological and medical research. However, the Median Hazard Ratio (MHR) that corresponds to the MOR in multilevel (i.e., 'frailty') Cox proportional hazards regression is rarely used. Analogously to the MOR, the MHR is the median relative change in the hazard of the occurrence of the outcome when comparing identical subjects from two randomly selected different clusters that are ordered by risk. We illustrate the application and interpretation of the MHR in a case study analyzing the hazard of mortality in patients hospitalized for acute myocardial infarction at hospitals in Ontario, Canada. We provide R code for computing the MHR. The MHR is a useful and intuitive measure for expressing cluster heterogeneity in the outcome and, thereby, estimating general contextual effects in multilevel survival analysis. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

R package to estimate intracluster correlation coefficient with confidence interval for binary data.

PubMed

Chakraborty, Hrishikesh; Hossain, Akhtar

2018-03-01

The Intracluster Correlation Coefficient (ICC) is a major parameter of interest in cluster randomized trials that measures the degree to which responses within the same cluster are correlated. There are several types of ICC estimators and its confidence intervals (CI) suggested in the literature for binary data. Studies have compared relative weaknesses and advantages of ICC estimators as well as its CI for binary data and suggested situations where one is advantageous in practical research. The commonly used statistical computing systems currently facilitate estimation of only a very few variants of ICC and its CI. To address the limitations of current statistical packages, we developed an R package, ICCbin, to facilitate estimating ICC and its CI for binary responses using different methods. The ICCbin package is designed to provide estimates of ICC in 16 different ways including analysis of variance methods, moments based estimation, direct probabilistic methods, correlation based estimation, and resampling method. CI of ICC is estimated using 5 different methods. It also generates cluster binary data using exchangeable correlation structure. ICCbin package provides two functions for users. The function rcbin() generates cluster binary data and the function iccbin() estimates ICC and it's CI. The users can choose appropriate ICC and its CI estimate from the wide selection of estimates from the outputs. The R package ICCbin presents very flexible and easy to use ways to generate cluster binary data and to estimate ICC and it's CI for binary response using different methods. The package ICCbin is freely available for use with R from the CRAN repository (https://cran.r-project.org/package=ICCbin). We believe that this package can be a very useful tool for researchers to design cluster randomized trials with binary outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

Finding SDSS Galaxy Clusters in 4-dimensional Color Space Using the False Discovery Rate

NASA Astrophysics Data System (ADS)

Nichol, R. C.; Miller, C. J.; Reichart, D.; Wasserman, L.; Genovese, C.; SDSS Collaboration

2000-12-01

We describe a recently developed statistical technique that provides a meaningful cut-off in probability-based decision making. We are concerned with multiple testing, where each test produces a well-defined probability (or p-value). By well-known, we mean that the null hypothesis used to determine the p-value is fully understood and appropriate. The method is entitled False Discovery Rate (FDR) and its largest advantage over other measures is that it allows one to specify a maximal amount of acceptable error. As an example of this tool, we apply FDR to a four-dimensional clustering algorithm using SDSS data. For each galaxy (or test galaxy), we count the number of neighbors that fit within one standard deviation of a four dimensional Gaussian centered on that test galaxy. The mean and standard deviation of that Gaussian are determined from the colors and errors of the test galaxy. We then take that same Gaussian and place it on a random selection of n galaxies and make a similar count. In the limit of large n, we expect the median count around these random galaxies to represent a typical field galaxy. For every test galaxy we determine the probability (or p-value) that it is a field galaxy based on these counts. A low p-value implies that the test galaxy is in a cluster environment. Once we have a p-value for every galaxy, we use FDR to determine at what level we should make our probability cut-off. Once this cut-off is made, we have a final sample of galaxies that are cluster-like galaxies. Using FDR, we also know the maximum amount of field contamination in our cluster galaxy sample. We present our preliminary galaxy clustering results using these methods.

A multicomponent matched filter cluster confirmation tool for eROSITA: initial application to the RASS and DES-SV data sets

NASA Astrophysics Data System (ADS)

Klein, M.; Mohr, J. J.; Desai, S.; Israel, H.; Allam, S.; Benoit-Lévy, A.; Brooks, D.; Buckley-Geer, E.; Carnero Rosell, A.; Carrasco Kind, M.; Cunha, C. E.; da Costa, L. N.; Dietrich, J. P.; Eifler, T. F.; Evrard, A. E.; Frieman, J.; Gruen, D.; Gruendl, R. A.; Gutierrez, G.; Honscheid, K.; James, D. J.; Kuehn, K.; Lima, M.; Maia, M. A. G.; March, M.; Melchior, P.; Menanteau, F.; Miquel, R.; Plazas, A. A.; Reil, K.; Romer, A. K.; Sanchez, E.; Santiago, B.; Scarpine, V.; Schubnell, M.; Sevilla-Noarbe, I.; Smith, M.; Soares-Santos, M.; Sobreira, F.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; Collaboration, the DES

2018-03-01

We describe a multicomponent matched filter (MCMF) cluster confirmation tool designed for the study of large X-ray source catalogues produced by the upcoming X-ray all-sky survey mission eROSITA. We apply the method to confirm a sample of 88 clusters with redshifts 0.05 < z < 0.8 in the recently published 2RXS catalogue from the ROSAT All-Sky Survey (RASS) over the 208 deg2 region overlapped by the Dark Energy Survey (DES) Science Verification (DES-SV) data set. In our pilot study, we examine all X-ray sources, regardless of their extent. Our method employs a multicolour red sequence (RS) algorithm that incorporates the X-ray count rate and peak position in determining the region of interest for follow-up and extracts the positionally and colour-weighted optical richness λMCMF as a function of redshift for each source. Peaks in the λMCMF-redshift distribution are identified and used to extract photometric redshifts, richness and uncertainties. The significances of all optical counterparts are characterized using the distribution of richnesses defined along random lines of sight. These significances are used to extract cluster catalogues and to estimate the contamination by random superpositions of unassociated optical systems. The delivered photometric redshift accuracy is δz/(1 + z) = 0.010. We find a well-defined X-ray luminosity-λMCMF relation with an intrinsic scatter of δln (λMCMF|Lx) = 0.21. Matching our catalogue with the DES-SV redMaPPer catalogue yields good agreement in redshift and richness estimates; comparing our catalogue with the South Pole Telescope (SPT) selected clusters shows no inconsistencies. SPT clusters in our data set are consistent with the high-mass extension of the RASS-based λMCMF-mass relation.

Role of delay-based reward in the spatial cooperation

NASA Astrophysics Data System (ADS)

Wang, Xu-Wen; Nie, Sen; Jiang, Luo-Luo; Wang, Bing-Hong; Chen, Shi-Ming

2017-01-01

Strategy selection in games, a typical decision making, usually brings noticeable reward for players which have discounted value if the delay appears. The discounted value is measure: earning sooner with a small reward or later with a delayed larger reward. Here, we investigate effects of delayed rewards on the cooperation in structured population. It is found that delayed reward supports the spreading of cooperation in square lattice, small-world and random networks. In particular, intermediate reward differences between delays impel the highest cooperation level. Interestingly, cooperative individuals with the same delay time steps form clusters to resist the invasion of defects, and cooperative individuals with lowest delay reward survive because they form the largest clusters in the lattice.

Feasibility of feature-based indexing, clustering, and search of clinical trials: A case study of breast cancer trials from ClinicalTrials.gov

PubMed Central

Boland, Mary Regina; Miotto, Riccardo; Gao, Junfeng; Weng, Chunhua

2013-01-01

Summary Background When standard therapies fail, clinical trials provide experimental treatment opportunities for patients with drug-resistant illnesses or terminal diseases. Clinical Trials can also provide free treatment and education for individuals who otherwise may not have access to such care. To find relevant clinical trials, patients often search online; however, they often encounter a significant barrier due to the large number of trials and in-effective indexing methods for reducing the trial search space. Objectives This study explores the feasibility of feature-based indexing, clustering, and search of clinical trials and informs designs to automate these processes. Methods We decomposed 80 randomly selected stage III breast cancer clinical trials into a vector of eligibility features, which were organized into a hierarchy. We clustered trials based on their eligibility feature similarities. In a simulated search process, manually selected features were used to generate specific eligibility questions to filter trials iteratively. Results We extracted 1,437 distinct eligibility features and achieved an inter-rater agreement of 0.73 for feature extraction for 37 frequent features occurring in more than 20 trials. Using all the 1,437 features we stratified the 80 trials into six clusters containing trials recruiting similar patients by patient-characteristic features, five clusters by disease-characteristic features, and two clusters by mixed features. Most of the features were mapped to one or more Unified Medical Language System (UMLS) concepts, demonstrating the utility of named entity recognition prior to mapping with the UMLS for automatic feature extraction. Conclusions It is feasible to develop feature-based indexing and clustering methods for clinical trials to identify trials with similar target populations and to improve trial search efficiency. PMID:23666475

Feasibility of feature-based indexing, clustering, and search of clinical trials. A case study of breast cancer trials from ClinicalTrials.gov.

PubMed

Boland, M R; Miotto, R; Gao, J; Weng, C

2013-01-01

When standard therapies fail, clinical trials provide experimental treatment opportunities for patients with drug-resistant illnesses or terminal diseases. Clinical Trials can also provide free treatment and education for individuals who otherwise may not have access to such care. To find relevant clinical trials, patients often search online; however, they often encounter a significant barrier due to the large number of trials and in-effective indexing methods for reducing the trial search space. This study explores the feasibility of feature-based indexing, clustering, and search of clinical trials and informs designs to automate these processes. We decomposed 80 randomly selected stage III breast cancer clinical trials into a vector of eligibility features, which were organized into a hierarchy. We clustered trials based on their eligibility feature similarities. In a simulated search process, manually selected features were used to generate specific eligibility questions to filter trials iteratively. We extracted 1,437 distinct eligibility features and achieved an inter-rater agreement of 0.73 for feature extraction for 37 frequent features occurring in more than 20 trials. Using all the 1,437 features we stratified the 80 trials into six clusters containing trials recruiting similar patients by patient-characteristic features, five clusters by disease-characteristic features, and two clusters by mixed features. Most of the features were mapped to one or more Unified Medical Language System (UMLS) concepts, demonstrating the utility of named entity recognition prior to mapping with the UMLS for automatic feature extraction. It is feasible to develop feature-based indexing and clustering methods for clinical trials to identify trials with similar target populations and to improve trial search efficiency.

Effect Sizes in Three-Level Cluster-Randomized Experiments

ERIC Educational Resources Information Center

Hedges, Larry V.

2011-01-01

Research designs involving cluster randomization are becoming increasingly important in educational and behavioral research. Many of these designs involve two levels of clustering or nesting (students within classes and classes within schools). Researchers would like to compute effect size indexes based on the standardized mean difference to…

Measures of clustering and heterogeneity in multilevel Poisson regression analyses of rates/count data

PubMed Central

Austin, Peter C.; Stryhn, Henrik; Leckie, George; Merlo, Juan

2017-01-01

Multilevel data occur frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models. These models incorporate cluster‐specific random effects that allow one to partition the total variation in the outcome into between‐cluster variation and between‐individual variation. The magnitude of the effect of clustering provides a measure of the general contextual effect. When outcomes are binary or time‐to‐event in nature, the general contextual effect can be quantified by measures of heterogeneity like the median odds ratio or the median hazard ratio, respectively, which can be calculated from a multilevel regression model. Outcomes that are integer counts denoting the number of times that an event occurred are common in epidemiological and medical research. The median (incidence) rate ratio in multilevel Poisson regression for counts that corresponds to the median odds ratio or median hazard ratio for binary or time‐to‐event outcomes respectively is relatively unknown and is rarely used. The median rate ratio is the median relative change in the rate of the occurrence of the event when comparing identical subjects from 2 randomly selected different clusters that are ordered by rate. We also describe how the variance partition coefficient, which denotes the proportion of the variation in the outcome that is attributable to between‐cluster differences, can be computed with count outcomes. We illustrate the application and interpretation of these measures in a case study analyzing the rate of hospital readmission in patients discharged from hospital with a diagnosis of heart failure. PMID:29114926

Cluster-randomized, controlled trial of computer-based decision support for selecting long-term anti-thrombotic therapy after acute ischaemic stroke.

PubMed

Weir, C J; Lees, K R; MacWalter, R S; Muir, K W; Wallesch, C-W; McLelland, E V; Hendry, A

2003-02-01

Identifying the appropriate long-term anti-thrombotic therapy following acute ischaemic stroke is a challenging area in which computer-based decision support may provide assistance. To evaluate the influence on prescribing practice of a computer-based decision support system (CDSS) that provided patient-specific estimates of the expected ischaemic and haemorrhagic vascular event rates under each potential anti-thrombotic therapy. Cluster-randomized controlled trial. We recruited patients who presented for a first investigation of ischaemic stroke or TIA symptoms, excluding those with a poor prognosis or major contraindication to anticoagulation. After observation of routine prescribing practice (6 months) in each hospital, centres were randomized for 6 months to either control (routine practice observed) or intervention (practice observed while the CDSS provided patient-specific information). We compared, between control and intervention centres, the risk reduction (estimated by the CDSS) in ischaemic and haemorrhagic vascular events achieved by long-term anti-thrombotic therapy, and the proportions of subjects prescribed the optimal therapy identified by the CDSS. Sixteen hospitals recruited 1952 subjects. When the CDSS provided information, the mean relative risk reduction attained by prescribing increased by 2.7 percentage units (95%CI -0.3 to 5.7) and the odds ratio for the optimal therapy being prescribed was 1.32 (0.83 to 1.80). Some 55% (5/9) of clinicians believed the CDSS had influenced their prescribing. Cluster-randomized trials provide excellent frameworks for evaluating novel clinical management methods. Our CDSS was feasible to implement and acceptable to clinicians, but did not substantially influence prescribing practice for anti-thrombotic drugs after acute ischaemic stroke.

The Effects of Including Observed Means or Latent Means as Covariates in Multilevel Models for Cluster Randomized Trials

ERIC Educational Resources Information Center

Aydin, Burak; Leite, Walter L.; Algina, James

2016-01-01

We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte…

The Design of Cluster Randomized Trials with Random Cross-Classifications

ERIC Educational Resources Information Center

Moerbeek, Mirjam; Safarkhani, Maryam

2018-01-01

Data from cluster randomized trials do not always have a pure hierarchical structure. For instance, students are nested within schools that may be crossed by neighborhoods, and soldiers are nested within army units that may be crossed by mental health-care professionals. It is important that the random cross-classification is taken into account…

Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

PubMed Central

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

Information sharing and sorting in a community

NASA Astrophysics Data System (ADS)

Bhattacherjee, Biplab; Manna, S. S.; Mukherjee, Animesh

2013-06-01

We present the results of a detailed numerical study of a model for the sharing and sorting of information in a community consisting of a large number of agents. The information gathering takes place in a sequence of mutual bipartite interactions where randomly selected pairs of agents communicate with each other to enhance their knowledge and sort out the common information. Although our model is less restricted compared to the well-established naming game, the numerical results strongly indicate that the whole set of exponents characterizing this model are different from those of the naming game and they assume nontrivial values. Finally, it appears that in analogy to the emergence of clusters in the phenomenon of percolation, one can define clusters of agents here having the same information. We have studied in detail the growth of the largest cluster in this article and performed its finite-size scaling analysis.

The two-point correlation function for groups of galaxies in the Center for Astrophysics redshift survey

NASA Technical Reports Server (NTRS)

Ramella, Massimo; Geller, Margaret J.; Huchra, John P.

1990-01-01

The large-scale distribution of groups of galaxies selected from complete slices of the CfA redshift survey extension is examined. The survey is used to reexamine the contribution of group members to the galaxy correlation function. The relationship between the correlation function for groups and those calculated for rich clusters is discussed, and the results for groups are examined as an extension of the relation between correlation function amplitude and richness. The group correlation function indicates that groups and individual galaxies are equivalent tracers of the large-scale matter distribution. The distribution of group centers is equivalent to random sampling of the galaxy distribution. The amplitude of the correlation function for groups is consistent with an extrapolation of the amplitude-richness relation for clusters. The amplitude scaled by the mean intersystem separation is also consistent with results for richer clusters.

Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex.

PubMed

Lindsay, Grace W; Rigotti, Mattia; Warden, Melissa R; Miller, Earl K; Fusi, Stefano

2017-11-08

Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear "mixed" selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which the PFC exhibits computationally relevant properties, such as mixed selectivity, and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data show significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and enables the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results provide clues about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training. SIGNIFICANCE STATEMENT The prefrontal cortex is a brain region believed to support the ability of animals to engage in complex behavior. How neurons in this area respond to stimuli-and in particular, to combinations of stimuli ("mixed selectivity")-is a topic of interest. Even though models with random feedforward connectivity are capable of creating computationally relevant mixed selectivity, such a model does not match the levels of mixed selectivity seen in the data analyzed in this study. Adding simple Hebbian learning to the model increases mixed selectivity to the correct level and makes the model match the data on several other relevant measures. This study thus offers predictions on how mixed selectivity and other properties evolve with training. Copyright © 2017 the authors 0270-6474/17/3711021-16$15.00/0.

The effect of asthma education program on knowledge of school teachers: a randomized controlled trial.

PubMed

Kawafha, Mariam M; Tawalbeh, Loai Issa

2015-04-01

The purpose of this study was to examine the effect of an asthma education program on schoolteachers' knowledge. Pre-test-post-test experimental randomized controlled design was used. A multistage-cluster sampling technique was used to randomly select governorate, primary schools, and schoolteachers. Schoolteachers were randomly assigned either to the experimental group (n = 36) and attended three educational sessions or to the control group (n = 38) who did not receive any intervention. Knowledge about asthma was measured using the Asthma General Knowledge Questionnaire for Adults (AGKQA). The results indicated that teachers in the experimental group showed significantly (p < .001) higher knowledge of asthma in the first post-test and the second post-test compared with those in the control group. Implementing asthma education enhanced schoolteachers' knowledge of asthma. The asthma education program should target schoolteachers to improve knowledge about asthma. © The Author(s) 2014.

Clues from Bent Jets

NASA Astrophysics Data System (ADS)

Kohler, Susanna

2018-04-01

Powerful jets emitted from the centers of distant galaxies make for spectacular signposts in the radio sky. Can observations of these jets reveal information about the environments that surround them?Signposts in the SkyVLA FIRST images of seven bent double-lobed radio galaxies from the authors sample. [Adapted from Silverstein et al. 2018]An active supermassive black hole lurking in a galactic center can put on quite a show! These beasts fling out accreting material, often forming intense jets that punch their way out of their host galaxies. As the jets propagate, they expand into large lobes of radio emission that we can spot from Earth observable signs of the connection between distant supermassive black holes and the galaxies in which they live.These distinctive double-lobed radio galaxies (DLRGs) dont all look the same. In particular, though the jets are emitted from the black holes two poles, the lobes of DLRGs dont always extend perfectly in opposite directions; often, the jets become bent on larger scales, appearing to us to subtend angles of less than 180 degrees.Can we use our observations of DLRG shapes and distributions to learn about their surroundings? A new study led by Ezekiel Silverstein (University of Michigan) has addressed this question by exploring DLRGs living in dense galaxy-cluster environments.Projected density of DLRGcentral galaxy matches (black) compared to a control sample of random positionscentral galaxy matches (red) for different distances from acluster center. DLRGs have a higher likelihood of being located close to a cluster center. [Silverstein et al. 2018]Living Near the HubTo build a sample of DLRGs in dense environments, Silverstein and collaborators started from a large catalog of DLRGs in Sloan Digital Sky Survey quasars with radio lobes visible in Very Large Array data. They then cross-matched these against three galaxy catalogs to produce a sample of 44 DLRGs that are each paired to a nearby massive galaxy, galaxy group, or galaxy cluster.To determine if these DLRGs locations are unusual, the authors next constructed a control sample of random galaxies using the same selection biases as their DLRG sample.Silverstein and collaborators found that the density of DLRGs as a function of distance from a cluster center drops off more rapidly than the density of galaxies in a typical cluster. Observed DLRGs are therefore more likely than random galaxies to be found near galaxy groups and clusters. The authors speculate that this may be a selection effect: DLRGs further from cluster centers may be less bright, preventing their detection.Bent Under PressureThe angle subtended by the DLRG radio lobes, plotted against the distance of the DLRG to the cluster center. Central galaxies (red circle) experience different physics and are therefore excluded from the sample. In the remaining sample, bent DLRGs appear to favor cluster centers, compared to unbent DLRGs. [Silverstein et al. 2018]In addition, Silverstein and collaborators found that location appears to affect the shape of a DLRG. Bent DLRGs (those with a measured angle between their lobes of 170 or smaller) are more likely to be found near a cluster center than unbent DLRGs (those with angles of 170180). The fraction of bent DLRGs is 78% within 3 million light-years of the cluster center, and 56% within double that distance compared to a typical fraction of just 29% in the field.These results support the idea that ram pressure the pressure experienced by a galaxy as it moves through the higher density environment closer to the center of a cluster is what bends the DLRGs.Whats next to learn? This study relies on a fairly small sample, so Silverstein and collaborators hope that future deep optical surveys will increase the completeness of cluster catalogs, enabling further testing of these outcomes and the exploration of other physics of galaxy-cluster environments.CitationEzekiel M Silverstein et al 2018 AJ 155 14. doi:10.3847/1538-3881/aa9d2e

Efficient sampling of complex network with modified random walk strategies

NASA Astrophysics Data System (ADS)

Xie, Yunya; Chang, Shuhua; Zhang, Zhipeng; Zhang, Mi; Yang, Lei

2018-02-01

We present two novel random walk strategies, choosing seed node (CSN) random walk and no-retracing (NR) random walk. Different from the classical random walk sampling, the CSN and NR strategies focus on the influences of the seed node choice and path overlap, respectively. Three random walk samplings are applied in the Erdös-Rényi (ER), Barabási-Albert (BA), Watts-Strogatz (WS), and the weighted USAir networks, respectively. Then, the major properties of sampled subnets, such as sampling efficiency, degree distributions, average degree and average clustering coefficient, are studied. The similar conclusions can be reached with these three random walk strategies. Firstly, the networks with small scales and simple structures are conducive to the sampling. Secondly, the average degree and the average clustering coefficient of the sampled subnet tend to the corresponding values of original networks with limited steps. And thirdly, all the degree distributions of the subnets are slightly biased to the high degree side. However, the NR strategy performs better for the average clustering coefficient of the subnet. In the real weighted USAir networks, some obvious characters like the larger clustering coefficient and the fluctuation of degree distribution are reproduced well by these random walk strategies.

A tripartite clustering analysis on microRNA, gene and disease model.

PubMed

Shen, Chengcheng; Liu, Ying

2012-02-01

Alteration of gene expression in response to regulatory molecules or mutations could lead to different diseases. MicroRNAs (miRNAs) have been discovered to be involved in regulation of gene expression and a wide variety of diseases. In a tripartite biological network of human miRNAs, their predicted target genes and the diseases caused by altered expressions of these genes, valuable knowledge about the pathogenicity of miRNAs, involved genes and related disease classes can be revealed by co-clustering miRNAs, target genes and diseases simultaneously. Tripartite co-clustering can lead to more informative results than traditional co-clustering with only two kinds of members and pass the hidden relational information along the relation chain by considering multi-type members. Here we report a spectral co-clustering algorithm for k-partite graph to find clusters with heterogeneous members. We use the method to explore the potential relationships among miRNAs, genes and diseases. The clusters obtained from the algorithm have significantly higher density than randomly selected clusters, which means members in the same cluster are more likely to have common connections. Results also show that miRNAs in the same family based on the hairpin sequences tend to belong to the same cluster. We also validate the clustering results by checking the correlation of enriched gene functions and disease classes in the same cluster. Finally, widely studied miR-17-92 and its paralogs are analyzed as a case study to reveal that genes and diseases co-clustered with the miRNAs are in accordance with current research findings.

Cluster Randomized Test-Negative Design (CR-TND) Trials: A Novel and Efficient Method to Assess the Efficacy of Community Level Dengue Interventions.

PubMed

Anders, Katherine L; Cutcher, Zoe; Kleinschmidt, Immo; Donnelly, Christl A; Ferguson, Neil M; Indriani, Citra; O'Neill, Scott L; Jewell, Nicholas P; Simmons, Cameron P

2018-05-07

Cluster randomized trials are the gold standard for assessing efficacy of community-level interventions, such as vector control strategies against dengue. We describe a novel cluster randomized trial methodology with a test-negative design, which offers advantages over traditional approaches. It utilizes outcome-based sampling of patients presenting with a syndrome consistent with the disease of interest, who are subsequently classified as test-positive cases or test-negative controls on the basis of diagnostic testing. We use simulations of a cluster trial to demonstrate validity of efficacy estimates under the test-negative approach. This demonstrates that, provided study arms are balanced for both test-negative and test-positive illness at baseline and that other test-negative design assumptions are met, the efficacy estimates closely match true efficacy. We also briefly discuss analytical considerations for an odds ratio-based effect estimate arising from clustered data, and outline potential approaches to analysis. We conclude that application of the test-negative design to certain cluster randomized trials could increase their efficiency and ease of implementation.

Quenched Large Deviations for Simple Random Walks on Percolation Clusters Including Long-Range Correlations

NASA Astrophysics Data System (ADS)

Berger, Noam; Mukherjee, Chiranjib; Okamura, Kazuki

2018-03-01

We prove a quenched large deviation principle (LDP) for a simple random walk on a supercritical percolation cluster (SRWPC) on {Z^d} ({d ≥ 2}). The models under interest include classical Bernoulli bond and site percolation as well as models that exhibit long range correlations, like the random cluster model, the random interlacement and the vacant set of random interlacements (for {d ≥ 3}) and the level sets of the Gaussian free field ({d≥ 3}). Inspired by the methods developed by Kosygina et al. (Commun Pure Appl Math 59:1489-1521, 2006) for proving quenched LDP for elliptic diffusions with a random drift, and by Yilmaz (Commun Pure Appl Math 62(8):1033-1075, 2009) and Rosenbluth (Quenched large deviations for multidimensional random walks in a random environment: a variational formula. Ph.D. thesis, NYU, arXiv:0804.1444v1) for similar results regarding elliptic random walks in random environment, we take the point of view of the moving particle and prove a large deviation principle for the quenched distribution of the pair empirical measures of the environment Markov chain in the non-elliptic case of SRWPC. Via a contraction principle, this reduces easily to a quenched LDP for the distribution of the mean velocity of the random walk and both rate functions admit explicit variational formulas. The main difficulty in our set up lies in the inherent non-ellipticity as well as the lack of translation-invariance stemming from conditioning on the fact that the origin belongs to the infinite cluster. We develop a unifying approach for proving quenched large deviations for SRWPC based on exploiting coercivity properties of the relative entropies in the context of convex variational analysis, combined with input from ergodic theory and invoking geometric properties of the supercritical percolation cluster.

Quenched Large Deviations for Simple Random Walks on Percolation Clusters Including Long-Range Correlations

NASA Astrophysics Data System (ADS)

Berger, Noam; Mukherjee, Chiranjib; Okamura, Kazuki

2017-12-01

We prove a quenched large deviation principle (LDP) for a simple random walk on a supercritical percolation cluster (SRWPC) on {Z^d} ({d ≥ 2} ). The models under interest include classical Bernoulli bond and site percolation as well as models that exhibit long range correlations, like the random cluster model, the random interlacement and the vacant set of random interlacements (for {d ≥ 3} ) and the level sets of the Gaussian free field ({d≥ 3} ). Inspired by the methods developed by Kosygina et al. (Commun Pure Appl Math 59:1489-1521, 2006) for proving quenched LDP for elliptic diffusions with a random drift, and by Yilmaz (Commun Pure Appl Math 62(8):1033-1075, 2009) and Rosenbluth (Quenched large deviations for multidimensional random walks in a random environment: a variational formula. Ph.D. thesis, NYU, arXiv:0804.1444v1) for similar results regarding elliptic random walks in random environment, we take the point of view of the moving particle and prove a large deviation principle for the quenched distribution of the pair empirical measures of the environment Markov chain in the non-elliptic case of SRWPC. Via a contraction principle, this reduces easily to a quenched LDP for the distribution of the mean velocity of the random walk and both rate functions admit explicit variational formulas. The main difficulty in our set up lies in the inherent non-ellipticity as well as the lack of translation-invariance stemming from conditioning on the fact that the origin belongs to the infinite cluster. We develop a unifying approach for proving quenched large deviations for SRWPC based on exploiting coercivity properties of the relative entropies in the context of convex variational analysis, combined with input from ergodic theory and invoking geometric properties of the supercritical percolation cluster.

Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex

PubMed Central

Lindsay, Grace W.

2017-01-01

Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear “mixed” selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which the PFC exhibits computationally relevant properties, such as mixed selectivity, and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data show significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and enables the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results provide clues about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training. SIGNIFICANCE STATEMENT The prefrontal cortex is a brain region believed to support the ability of animals to engage in complex behavior. How neurons in this area respond to stimuli—and in particular, to combinations of stimuli (“mixed selectivity”)—is a topic of interest. Even though models with random feedforward connectivity are capable of creating computationally relevant mixed selectivity, such a model does not match the levels of mixed selectivity seen in the data analyzed in this study. Adding simple Hebbian learning to the model increases mixed selectivity to the correct level and makes the model match the data on several other relevant measures. This study thus offers predictions on how mixed selectivity and other properties evolve with training. PMID:28986463

Identification of cultivars and validation of genetic relationships in Mangifera indica L. using RAPD markers.

PubMed

Schnell, R J; Ronning, C M; Knight, R J

1995-02-01

Twenty-five accessions of mango were examined for random amplified polymorphic DNA (RAPD) genetic markers with 80 10-mer random primers. Of the 80 primers screened, 33 did not amplify, 19 were monomorphic, and 28 gave reproducible, polymorphic DNA amplification patterns. Eleven primers were selected from the 28 for the study. The number of bands generated was primer- and genotype-dependent, and ranged from 1 to 10. No primer gave unique banding patterns for each of the 25 accessions; however, ten different combinations of 2 primer banding patterns produced unique fingerprints for each accession. A maternal half-sib (MHS) family was included among the 25 accessions to see if genetic relationships could be detected. RAPD data were used to generate simple matching coefficients, which were analyzed phenetically and by means of principal coordinate analysis (PCA). The MHS clustered together in both the phenetic and the PCA while the randomly selected accessions were scattered with no apparent pattern. The uses of RAPD analysis for Mangifera germ plasm classification and clonal identification are discussed.

Clustering, randomness, and regularity in cloud fields. 4. Stratocumulus cloud fields

NASA Astrophysics Data System (ADS)

Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.

1994-07-01

To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (>900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.

Clustering, randomness, and regularity in cloud fields. 4: Stratocumulus cloud fields

NASA Technical Reports Server (NTRS)

Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.

1994-01-01

To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (more than 900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.

The Hubble Space Telescope Medium Deep Survey Cluster Sample: Methodology and Data

NASA Astrophysics Data System (ADS)

Ostrander, E. J.; Nichol, R. C.; Ratnatunga, K. U.; Griffiths, R. E.

1998-12-01

We present a new, objectively selected, sample of galaxy overdensities detected in the Hubble Space Telescope Medium Deep Survey (MDS). These clusters/groups were found using an automated procedure that involved searching for statistically significant galaxy overdensities. The contrast of the clusters against the field galaxy population is increased when morphological data are used to search around bulge-dominated galaxies. In total, we present 92 overdensities above a probability threshold of 99.5%. We show, via extensive Monte Carlo simulations, that at least 60% of these overdensities are likely to be real clusters and groups and not random line-of-sight superpositions of galaxies. For each overdensity in the MDS cluster sample, we provide a richness and the average of the bulge-to-total ratio of galaxies within each system. This MDS cluster sample potentially contains some of the most distant clusters/groups ever detected, with about 25% of the overdensities having estimated redshifts z > ~0.9. We have made this sample publicly available to facilitate spectroscopic confirmation of these clusters and help more detailed studies of cluster and galaxy evolution. We also report the serendipitous discovery of a new cluster close on the sky to the rich optical cluster Cl l0016+16 at z = 0.546. This new overdensity, HST 001831+16208, may be coincident with both an X-ray source and a radio source. HST 001831+16208 is the third cluster/group discovered near to Cl 0016+16 and appears to strengthen the claims of Connolly et al. of superclustering at high redshift.

Molecular epidemiology of goat pox viruses.

PubMed

Roy, P; Jaisree, S; Balakrishnan, S; Senthilkumar, K; Mahaprabhu, R; Mishra, A; Maity, B; Ghosh, T K; Karmakar, A P

2018-02-01

Goat pox disease outbreaks were observed in different places affecting Black Bengal Goats in West Bengal (WB) and Tellicherry, Vembur and non-descriptive breeds in Tamil Nadu (TN) causing severe lesions and mortality up to 30%. Clinical specimens from all the outbreaks were screened by polymerase chain reaction followed by restriction fragment length polymorphism (PCR-RFLP) and confirmed the diseases as Goat Pox. Virus isolation in Vero cell line was done with randomly selected ten samples, cytopathic effects (CPE) characterized by syncytia and intracytoplasmic inclusion bodies were observed after several blind passages. Nucleotide sequence of complete p32 gene using randomly selected two isolates and three clinical specimens revealed presence of Goat pox virus (GTPV)-specific signature residues in all the sequences. Phylogenetic analysis using the present five sequences along with GenBank data of GTPV complete p32 gene sequences showed all the GTPV sequences cluster together except Pellor strain (NC004003) and FZ Chinese strain (KC951854). The five sequences either from WB or TN cluster more closely with GTPV isolates of Maharashtra state that were responsible for cross species outbreak of pox disease in both sheep (KF468759) and goats (KF468762) in India during the year 2010. All the Indian goat pox viruses, including the Mukteswar strain, isolated in 1946 and sequence reported in 2004 clustered together with the GTPVs causing the recent outbreaks. It was observed that GTPVs caused similar clinical manifestation irrespective of their geographical locations and breed characteristics, no variation observed among the Indian isolates based on p32 gene over the period of seventy years and disease outbreaks could not be observed or reported in vaccinated goats. © 2017 Blackwell Verlag GmbH.

General Framework for Effect Sizes in Cluster Randomized Experiments

ERIC Educational Resources Information Center

VanHoudnos, Nathan

2016-01-01

Cluster randomized experiments are ubiquitous in modern education research. Although a variety of modeling approaches are used to analyze these data, perhaps the most common methodology is a normal mixed effects model where some effects, such as the treatment effect, are regarded as fixed, and others, such as the effect of group random assignment…

Improving Language Comprehension in Preschool Children with Language Difficulties: A Cluster Randomized Trial

ERIC Educational Resources Information Center

Hagen, Åste M.; Melby-Lervåg, Monica; Lervåg, Arne

2017-01-01

Background: Children with language comprehension difficulties are at risk of educational and social problems, which in turn impede employment prospects in adulthood. However, few randomized trials have examined how such problems can be ameliorated during the preschool years. Methods: We conducted a cluster randomized trial in 148 preschool…

The Walking School Bus and children's physical activity: A pilot cluster randomized controlled trial

USDA-ARS?s Scientific Manuscript database

To evaluate the impact of a "walking school bus" program on children's rates of active commuting to school and physical activity. We conducted a pilot cluster randomized controlled trial among 4th-graders from 8 schools in Houston, Texas (N = 149). Random allocation to treatment or control condition...

Quantifying the impact of fixed effects modeling of clusters in multiple imputation for cluster randomized trials

PubMed Central

Andridge, Rebecca. R.

2011-01-01

In cluster randomized trials (CRTs), identifiable clusters rather than individuals are randomized to study groups. Resulting data often consist of a small number of clusters with correlated observations within a treatment group. Missing data often present a problem in the analysis of such trials, and multiple imputation (MI) has been used to create complete data sets, enabling subsequent analysis with well-established analysis methods for CRTs. We discuss strategies for accounting for clustering when multiply imputing a missing continuous outcome, focusing on estimation of the variance of group means as used in an adjusted t-test or ANOVA. These analysis procedures are congenial to (can be derived from) a mixed effects imputation model; however, this imputation procedure is not yet available in commercial statistical software. An alternative approach that is readily available and has been used in recent studies is to include fixed effects for cluster, but the impact of using this convenient method has not been studied. We show that under this imputation model the MI variance estimator is positively biased and that smaller ICCs lead to larger overestimation of the MI variance. Analytical expressions for the bias of the variance estimator are derived in the case of data missing completely at random (MCAR), and cases in which data are missing at random (MAR) are illustrated through simulation. Finally, various imputation methods are applied to data from the Detroit Middle School Asthma Project, a recent school-based CRT, and differences in inference are compared. PMID:21259309

Prevalence of skeletal and eye malformations in frogs from north-central United States: estimations based on collections from randomly selected sites.

PubMed

Schoff, Patrick K; Johnson, Catherine M; Schotthoefer, Anna M; Murphy, Joseph E; Lieske, Camilla; Cole, Rebecca A; Johnson, Lucinda B; Beasley, Val R

2003-07-01

Skeletal malformation rates for several frog species were determined in a set of randomly selected wetlands in the north-central USA over three consecutive years. In 1998, 62 sites yielded 389 metamorphic frogs, nine (2.3%) of which had skeletal or eye malformations. A subset of the original sites was surveyed in the following 2 yr. In 1999, 1,085 metamorphic frogs were collected from 36 sites and 17 (1.6%) had skeletal or eye malformations, while in 2000, examination of 1,131 metamorphs yielded 16 (1.4%) with skeletal or eye malformations. Hindlimb malformations predominated in all three years, but other abnormalities, involving forelimb, eye, and pelvis were also found. Northern leopard frogs (Rana pipiens) constituted the majority of collected metamorphs as well as most of the malformed specimens. However, malformations were also noted in mink frogs (R. septentrionalis), wood frogs (R. sylvatica), and gray tree frogs (Hyla spp.). The malformed specimens were found in clustered sites in all three years but the cluster locations were not the same in any year. The malformation rates reported here are higher than the 0.3% rate determined for metamorphic frogs collected from similar sites in Minnesota in the 1960s, and thus, appear to represent an elevation of an earlier baseline malformation rate.

Prevalence of skeletal and eye malformations in frogs from north-central United States: estimations based on collections from randomly selected sites

USGS Publications Warehouse

Schoff, P.K.; Johnson, C.M.; Schotthoefer, A.M.; Murphy, J.E.; Lieske, C.; Cole, Rebecca A.; Johnson, L.B.; Beasley, V.R.

2003-01-01

Skeletal malformation rates for several frog species were determined in a set of randomly selected wetlands in the north-central USA over three consecutive years. In 1998, 62 sites yielded 389 metamorphic frogs, nine (2.3%) of which had skeletal or eye malformations. A subset of the original sites was surveyed in the following 2 yr. In 1999, 1,085 metamorphic frogs were collected from 36 sites and 17 (1.6%) had skeletal or eye malformations, while in 2000, examination of 1,131 metamorphs yielded 16 (1.4%) with skeletal or eye malformations. Hindlimb malformations predominated in all three years, but other abnormalities, involving forelimb, eye, and pelvis were also found. Northern leopard frogs (Rana pipiens) constituted the majority of collected metamorphs as well as most of the malformed specimens. However, malformations were also noted in mink frogs (R. septentrionalis), wood frogs (R. sylvatica), and gray tree frogs (Hyla spp.). The malformed specimens were found in clustered sites in all three years but the cluster locations were not the same in any year. The malformation rates reported here are higher than the 0.3% rate determined for metamorphic frogs collected from similar sites in Minnesota in the 1960s, and thus, appear to represent an elevation of an earlier baseline malformation rate.

The median hazard ratio: a useful measure of variance and general contextual effects in multilevel survival analysis

PubMed Central

Wagner, Philippe; Merlo, Juan

2016-01-01

Multilevel data occurs frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models (MLRM). MLRM incorporate cluster‐specific random effects which allow one to partition the total individual variance into between‐cluster variation and between‐individual variation. Statistically, MLRM account for the dependency of the data within clusters and provide correct estimates of uncertainty around regression coefficients. Substantively, the magnitude of the effect of clustering provides a measure of the General Contextual Effect (GCE). When outcomes are binary, the GCE can also be quantified by measures of heterogeneity like the Median Odds Ratio (MOR) calculated from a multilevel logistic regression model. Time‐to‐event outcomes within a multilevel structure occur commonly in epidemiological and medical research. However, the Median Hazard Ratio (MHR) that corresponds to the MOR in multilevel (i.e., ‘frailty’) Cox proportional hazards regression is rarely used. Analogously to the MOR, the MHR is the median relative change in the hazard of the occurrence of the outcome when comparing identical subjects from two randomly selected different clusters that are ordered by risk. We illustrate the application and interpretation of the MHR in a case study analyzing the hazard of mortality in patients hospitalized for acute myocardial infarction at hospitals in Ontario, Canada. We provide R code for computing the MHR. The MHR is a useful and intuitive measure for expressing cluster heterogeneity in the outcome and, thereby, estimating general contextual effects in multilevel survival analysis. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:27885709

The Relation between Globular Cluster Systems and Supermassive Black Holes in Spiral Galaxies: The Case Study of NGC 4258

NASA Astrophysics Data System (ADS)

González-Lópezlira, Rosa A.; Lomelí-Núñez, Luis; Álamo-Martínez, Karla; Órdenes-Briceño, Yasna; Loinard, Laurent; Georgiev, Iskren Y.; Muñoz, Roberto P.; Puzia, Thomas H.; Bruzual A., Gustavo; Gwyn, Stephen

2017-02-01

We aim to explore the relationship between globular cluster total number, {N}{GC}, and central black hole mass, M •, in spiral galaxies, and compare it with that recently reported for ellipticals. We present results for the Sbc galaxy NGC 4258, from Canada-France-Hawaii Telescope data. Thanks to water masers with Keplerian rotation in a circumnuclear disk, NGC 4258 has the most precisely measured extragalactic distance and supermassive black hole mass to date. The globular cluster (GC) candidate selection is based on the ({u}* -{I}\\prime ) versus ({I}\\prime -{K}s) diagram, which is a superb tool to distinguish GCs from foreground stars, background galaxies, and young stellar clusters, and hence can provide the best number counts of GCs from photometry alone, virtually free of contamination, even if the galaxy is not completely edge-on. The mean optical and optical-near-infrared colors of the clusters are consistent with those of the Milky Way and M 31, after extinction is taken into account. We directly identify 39 GC candidates; after completeness correction, GC luminosity function extrapolation, and correction for spatial coverage, we calculate a total {N}{GC}=144+/- {31}-36+38 (random and systematic uncertainties, respectively). We have thus increased to six the sample of spiral galaxies with measurements of both M • and {N}{GC}. NGC 4258 has a specific frequency {S}{{N}}=0.4+/- 0.1 (random uncertainty), and is consistent within 2σ with the {N}{GC} versus M • correlation followed by elliptical galaxies. The Milky Way continues to be the only spiral that deviates significantly from the relation.

densityCut: an efficient and versatile topological approach for automatic clustering of biological data

PubMed Central

Ding, Jiarui; Shah, Sohrab; Condon, Anne

2016-01-01

Motivation: Many biological data processing problems can be formalized as clustering problems to partition data points into sensible and biologically interpretable groups. Results: This article introduces densityCut, a novel density-based clustering algorithm, which is both time- and space-efficient and proceeds as follows: densityCut first roughly estimates the densities of data points from a K-nearest neighbour graph and then refines the densities via a random walk. A cluster consists of points falling into the basin of attraction of an estimated mode of the underlining density function. A post-processing step merges clusters and generates a hierarchical cluster tree. The number of clusters is selected from the most stable clustering in the hierarchical cluster tree. Experimental results on ten synthetic benchmark datasets and two microarray gene expression datasets demonstrate that densityCut performs better than state-of-the-art algorithms for clustering biological datasets. For applications, we focus on the recent cancer mutation clustering and single cell data analyses, namely to cluster variant allele frequencies of somatic mutations to reveal clonal architectures of individual tumours, to cluster single-cell gene expression data to uncover cell population compositions, and to cluster single-cell mass cytometry data to detect communities of cells of the same functional states or types. densityCut performs better than competing algorithms and is scalable to large datasets. Availability and Implementation: Data and the densityCut R package is available from https://bitbucket.org/jerry00/densitycut_dev. Contact: condon@cs.ubc.ca or sshah@bccrc.ca or jiaruid@cs.ubc.ca Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153661

A pilot cluster randomized controlled trial of structured goal-setting following stroke.

PubMed

Taylor, William J; Brown, Melanie; William, Levack; McPherson, Kathryn M; Reed, Kirk; Dean, Sarah G; Weatherall, Mark

2012-04-01

To determine the feasibility, the cluster design effect and the variance and minimal clinical importance difference in the primary outcome in a pilot study of a structured approach to goal-setting. A cluster randomized controlled trial. Inpatient rehabilitation facilities. People who were admitted to inpatient rehabilitation following stroke who had sufficient cognition to engage in structured goal-setting and complete the primary outcome measure. Structured goal elicitation using the Canadian Occupational Performance Measure. Quality of life at 12 weeks using the Schedule for Individualised Quality of Life (SEIQOL-DW), Functional Independence Measure, Short Form 36 and Patient Perception of Rehabilitation (measuring satisfaction with rehabilitation). Assessors were blinded to the intervention. Four rehabilitation services and 41 patients were randomized. We found high values of the intraclass correlation for the outcome measures (ranging from 0.03 to 0.40) and high variance of the SEIQOL-DW (SD 19.6) in relation to the minimally importance difference of 2.1, leading to impractically large sample size requirements for a cluster randomized design. A cluster randomized design is not a practical means of avoiding contamination effects in studies of inpatient rehabilitation goal-setting. Other techniques for coping with contamination effects are necessary.

Sampling procedures for inventory of commercial volume tree species in Amazon Forest.

PubMed

Netto, Sylvio P; Pelissari, Allan L; Cysneiros, Vinicius C; Bonazza, Marcelo; Sanquetta, Carlos R

2017-01-01

The spatial distribution of tropical tree species can affect the consistency of the estimators in commercial forest inventories, therefore, appropriate sampling procedures are required to survey species with different spatial patterns in the Amazon Forest. For this, the present study aims to evaluate the conventional sampling procedures and introduce the adaptive cluster sampling for volumetric inventories of Amazonian tree species, considering the hypotheses that the density, the spatial distribution and the zero-plots affect the consistency of the estimators, and that the adaptive cluster sampling allows to obtain more accurate volumetric estimation. We use data from a census carried out in Jamari National Forest, Brazil, where trees with diameters equal to or higher than 40 cm were measured in 1,355 plots. Species with different spatial patterns were selected and sampled with simple random sampling, systematic sampling, linear cluster sampling and adaptive cluster sampling, whereby the accuracy of the volumetric estimation and presence of zero-plots were evaluated. The sampling procedures applied to species were affected by the low density of trees and the large number of zero-plots, wherein the adaptive clusters allowed concentrating the sampling effort in plots with trees and, thus, agglutinating more representative samples to estimate the commercial volume.

Impact of Probiotics on Necrotizing Enterocolitis

PubMed Central

Underwood, Mark A.

2016-01-01

A large number of randomized placebo-controlled clinical trials and cohort studies have demonstrated a decrease in the incidence of necrotizing enterocolitis with administration of probiotic microbes. These studies have prompted many neonatologists to adopt routine prophylactic administration of probiotics while others await more definitive studies and/or probiotic products with demonstrated purity and stable numbers of live organisms. Cross-contamination and inadequate sample size limit the value of further traditional placebo-controlled randomized controlled trials. Key areas for future research include mechanisms of protection, optimum probiotic species or strains (or combinations thereof) and duration of treatment, interactions between diet and the administered probiotic, and the influence of genetic polymorphisms in the mother and infant on probiotic response. Next generation probiotics selected based on bacterial genetics rather than ease of production and large cluster-randomized clinical trials hold great promise for NEC prevention. PMID:27836423

Mobile phone intervention reduces perinatal mortality in zanzibar: secondary outcomes of a cluster randomized controlled trial.

PubMed

Lund, Stine; Rasch, Vibeke; Hemed, Maryam; Boas, Ida Marie; Said, Azzah; Said, Khadija; Makundu, Mkoko Hassan; Nielsen, Birgitte Bruun

2014-03-26

Mobile phones are increasingly used in health systems in developing countries and innovative technical solutions have great potential to overcome barriers of access to reproductive and child health care. However, despite widespread support for the use of mobile health technologies, evidence for its role in health care is sparse. We aimed to evaluate the association between a mobile phone intervention and perinatal mortality in a resource-limited setting. This study was a pragmatic, cluster-randomized, controlled trial with primary health care facilities in Zanzibar as the unit of randomization. At their first antenatal care visit, 2550 pregnant women (1311 interventions and 1239 controls) who attended antenatal care at selected primary health care facilities were included in this study and followed until 42 days after delivery. Twenty-four primary health care facilities in six districts were randomized to either mobile phone intervention or standard care. The intervention consisted of a mobile phone text message and voucher component. Secondary outcome measures included stillbirth, perinatal mortality, and death of a child within 42 days after birth as a proxy of neonatal mortality. Within the first 42 days of life, 2482 children were born alive, 54 were stillborn, and 36 died. The overall perinatal mortality rate in the study was 27 per 1000 total births. The rate was lower in the intervention clusters, 19 per 1000 births, than in the control clusters, 36 per 1000 births. The intervention was associated with a significant reduction in perinatal mortality with an odds ratio (OR) of 0.50 (95% CI 0.27-0.93). Other secondary outcomes showed an insignificant reduction in stillbirth (OR 0.65, 95% CI 0.34-1.24) and an insignificant reduction in death within the first 42 days of life (OR 0.79, 95% CI 0.36-1.74). Mobile phone applications may contribute to improved health of the newborn and should be considered by policy makers in resource-limited settings. ClinicalTrials.gov NCT01821222; http://www.clinicaltrials.gov/ct2/show/NCT01821222 (Archived by WebCite at http://www.webcitation.org/6NqxnxYn0).

Combining Computational Methods for Hit to Lead Optimization in Mycobacterium tuberculosis Drug Discovery

PubMed Central

Ekins, Sean; Freundlich, Joel S.; Hobrath, Judith V.; White, E. Lucile; Reynolds, Robert C

2013-01-01

Purpose Tuberculosis treatments need to be shorter and overcome drug resistance. Our previous large scale phenotypic high-throughput screening against Mycobacterium tuberculosis (Mtb) has identified 737 active compounds and thousands that are inactive. We have used this data for building computational models as an approach to minimize the number of compounds tested. Methods A cheminformatics clustering approach followed by Bayesian machine learning models (based on publicly available Mtb screening data) was used to illustrate that application of these models for screening set selections can enrich the hit rate. Results In order to explore chemical diversity around active cluster scaffolds of the dose-response hits obtained from our previous Mtb screens a set of 1924 commercially available molecules have been selected and evaluated for antitubercular activity and cytotoxicity using Vero, THP-1 and HepG2 cell lines with 4.3%, 4.2% and 2.7% hit rates, respectively. We demonstrate that models incorporating antitubercular and cytotoxicity data in Vero cells can significantly enrich the selection of non-toxic actives compared to random selection. Across all cell lines, the Molecular Libraries Small Molecule Repository (MLSMR) and cytotoxicity model identified ~10% of the hits in the top 1% screened (>10 fold enrichment). We also showed that seven out of nine Mtb active compounds from different academic published studies and eight out of eleven Mtb active compounds from a pharmaceutical screen (GSK) would have been identified by these Bayesian models. Conclusion Combining clustering and Bayesian models represents a useful strategy for compound prioritization and hit-to lead optimization of antitubercular agents. PMID:24132686

Finding and testing network communities by lumped Markov chains.

PubMed

Piccardi, Carlo

2011-01-01

Identifying communities (or clusters), namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. Yet, there is a lack of formal criteria for defining communities and for testing their significance. We propose a sharp definition that is based on a quality threshold. By means of a lumped Markov chain model of a random walker, a quality measure called "persistence probability" is associated to a cluster, which is then defined as an "α-community" if such a probability is not smaller than α. Consistently, a partition composed of α-communities is an "α-partition." These definitions turn out to be very effective for finding and testing communities. If a set of candidate partitions is available, setting the desired α-level allows one to immediately select the α-partition with the finest decomposition. Simultaneously, the persistence probabilities quantify the quality of each single community. Given its ability in individually assessing each single cluster, this approach can also disclose single well-defined communities even in networks that overall do not possess a definite clusterized structure.

Determining the Number of Clusters in a Data Set Without Graphical Interpretation

NASA Technical Reports Server (NTRS)

Aguirre, Nathan S.; Davies, Misty D.

2011-01-01

Cluster analysis is a data mining technique that is meant ot simplify the process of classifying data points. The basic clustering process requires an input of data points and the number of clusters wanted. The clustering algorithm will then pick starting C points for the clusters, which can be either random spatial points or random data points. It then assigns each data point to the nearest C point where "nearest usually means Euclidean distance, but some algorithms use another criterion. The next step is determining whether the clustering arrangement this found is within a certain tolerance. If it falls within this tolerance, the process ends. Otherwise the C points are adjusted based on how many data points are in each cluster, and the steps repeat until the algorithm converges,

A Cluster-Randomized Trial of Restorative Practices: An Illustration to Spur High-Quality Research and Evaluation

ERIC Educational Resources Information Center

Acosta, Joie D.; Chinman, Matthew; Ebener, Patricia; Phillips, Andrea; Xenakis, Lea; Malone, Patrick S.

2016-01-01

Restorative practices in schools lack rigorous evaluation studies. As an example of rigorous school-based research, this article describes the first randomized control trial of restorative practices to date, the Study of Restorative Practices. It is a 5-year, cluster-randomized controlled trial (RCT) of the Restorative Practices Intervention (RPI)…

Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects

PubMed Central

2012-01-01

Background Time-course gene expression data such as yeast cell cycle data may be periodically expressed. To cluster such data, currently used Fourier series approximations of periodic gene expressions have been found not to be sufficiently adequate to model the complexity of the time-course data, partly due to their ignoring the dependence between the expression measurements over time and the correlation among gene expression profiles. We further investigate the advantages and limitations of available models in the literature and propose a new mixture model with autoregressive random effects of the first order for the clustering of time-course gene-expression profiles. Some simulations and real examples are given to demonstrate the usefulness of the proposed models. Results We illustrate the applicability of our new model using synthetic and real time-course datasets. We show that our model outperforms existing models to provide more reliable and robust clustering of time-course data. Our model provides superior results when genetic profiles are correlated. It also gives comparable results when the correlation between the gene profiles is weak. In the applications to real time-course data, relevant clusters of coregulated genes are obtained, which are supported by gene-function annotation databases. Conclusions Our new model under our extension of the EMMIX-WIRE procedure is more reliable and robust for clustering time-course data because it adopts a random effects model that allows for the correlation among observations at different time points. It postulates gene-specific random effects with an autocorrelation variance structure that models coregulation within the clusters. The developed R package is flexible in its specification of the random effects through user-input parameters that enables improved modelling and consequent clustering of time-course data. PMID:23151154

Leveraging contact network structure in the design of cluster randomized trials.

PubMed

Harling, Guy; Wang, Rui; Onnela, Jukka-Pekka; De Gruttola, Victor

2017-02-01

In settings like the Ebola epidemic, where proof-of-principle trials have provided evidence of efficacy but questions remain about the effectiveness of different possible modes of implementation, it may be useful to conduct trials that not only generate information about intervention effects but also themselves provide public health benefit. Cluster randomized trials are of particular value for infectious disease prevention research by virtue of their ability to capture both direct and indirect effects of intervention, the latter of which depends heavily on the nature of contact networks within and across clusters. By leveraging information about these networks-in particular the degree of connection across randomized units, which can be obtained at study baseline-we propose a novel class of connectivity-informed cluster trial designs that aim both to improve public health impact (speed of epidemic control) and to preserve the ability to detect intervention effects. We several designs for cluster randomized trials with staggered enrollment, in each of which the order of enrollment is based on the total number of ties (contacts) from individuals within a cluster to individuals in other clusters. Our designs can accommodate connectivity based either on the total number of external connections at baseline or on connections only to areas yet to receive the intervention. We further consider a "holdback" version of the designs in which control clusters are held back from re-randomization for some time interval. We investigate the performance of these designs in terms of epidemic control outcomes (time to end of epidemic and cumulative incidence) and power to detect intervention effect, by simulating vaccination trials during an SEIR-type epidemic outbreak using a network-structured agent-based model. We compare results to those of a traditional Stepped Wedge trial. In our simulation studies, connectivity-informed designs lead to a 20% reduction in cumulative incidence compared to comparable traditional study designs, but have little impact on epidemic length. Power to detect intervention effect is reduced in all connectivity-informed designs, but "holdback" versions provide power that is very close to that of a traditional Stepped Wedge approach. Incorporating information about cluster connectivity in the design of cluster randomized trials can increase their public health impact, especially in acute outbreak settings. Using this information helps control outbreaks-by minimizing the number of cross-cluster infections-with very modest cost in terms of power to detect effectiveness.

Partially oxidized iridium clusters within dendrimers: size-controlled synthesis and selective hydrogenation of 2-nitrobenzaldehyde

NASA Astrophysics Data System (ADS)

Higaki, Tatsuya; Kitazawa, Hirokazu; Yamazoe, Seiji; Tsukuda, Tatsuya

2016-06-01

Iridium clusters nominally composed of 15, 30 or 60 atoms were size-selectively synthesized within OH-terminated poly(amidoamine) dendrimers of generation 6. Spectroscopic characterization revealed that the Ir clusters were partially oxidized. All the Ir clusters efficiently converted 2-nitrobenzaldehyde to anthranil and 2-aminobenzaldehyde under atmospheric hydrogen at room temperature in toluene via selective hydrogenation of the NO2 group. The selectivity toward 2-aminobenzaldehyde over anthranil was improved with the reduction of the cluster size. The improved selectivity is ascribed to more efficient reduction than intramolecular heterocyclization of a hydroxylamine intermediate on smaller clusters that have a higher Ir(0)-phase population on the surface.Iridium clusters nominally composed of 15, 30 or 60 atoms were size-selectively synthesized within OH-terminated poly(amidoamine) dendrimers of generation 6. Spectroscopic characterization revealed that the Ir clusters were partially oxidized. All the Ir clusters efficiently converted 2-nitrobenzaldehyde to anthranil and 2-aminobenzaldehyde under atmospheric hydrogen at room temperature in toluene via selective hydrogenation of the NO2 group. The selectivity toward 2-aminobenzaldehyde over anthranil was improved with the reduction of the cluster size. The improved selectivity is ascribed to more efficient reduction than intramolecular heterocyclization of a hydroxylamine intermediate on smaller clusters that have a higher Ir(0)-phase population on the surface. Electronic supplementary information (ESI) available. See DOI: 10.1039/c6nr01460g

Cluster-randomized controlled trial of the effects of free glasses on purchase of children's glasses in China: The PRICE (Potentiating Rural Investment in Children's Eyecare) study.

PubMed

Wang, Xiuqin; Congdon, Nathan; Ma, Yue; Hu, Min; Zhou, Yuan; Liao, Weiqi; Jin, Ling; Xiao, Baixiang; Wu, Xiaoyi; Ni, Ming; Yi, Hongmei; Huang, Yiwen; Varga, Beatrice; Zhang, Hong; Cun, Yongkang; Li, Xianshun; Yang, Luhua; Liang, Chaoguang; Huang, Wan; Rozelle, Scott; Ma, Xiaochen

2017-01-01

Offering free glasses can be important to increase children's wear. We sought to assess whether "Upgrade glasses" could avoid reduced glasses sales when offering free glasses to children in China. In this cluster-randomized, controlled trial, children with uncorrected visual acuity (VA)< = 6/12 in either eye correctable to >6/12 in both eyes at 138 randomly-selected primary schools in 9 counties in Guangdong and Yunnan provinces, China, were randomized by school to one of four groups: glasses prescription only (Control); Free Glasses; Free Glasses + offer of $15 Upgrade Glasses; Free Glasses + offer of $30 Upgrade Glasses. Spectacle purchase (main outcome) was assessed 6 months after randomization. Among 10,234 children screened, 882 (8.62%, mean age 10.6 years, 45.5% boys) were eligible and randomized: 257 (29.1%) at 37 schools to Control; 253 (28.7%) at 32 schools to Free Glasses; 187 (21.2%) at 31 schools to Free Glasses + $15 Upgrade; and 185 (21.0%) at 27 schools to Free Glasses +$30 Upgrade. Baseline ownership among these children needing glasses was 11.8% (104/882), and 867 (98.3%) children completed follow-up. Glasses purchase was significantly less likely when free glasses were given: Control: 59/250 = 23.6%; Free glasses: 32/252 = 12.7%, P = 0.010. Offering Upgrade Glasses eliminated this difference: Free + $15 Upgrade: 39/183 = 21.3%, multiple regression relative risk (RR) 0.90 (0.56-1.43), P = 0.65; Free + $30 Upgrade: 38/182 = 20.9%, RR 0.91 (0.59, 1.42), P = 0.69. Upgrade glasses can prevent reductions in glasses purchase when free spectacles are provided, providing important program income. ClinicalTrials.gov Identifier: NCT02231606. Registered on 31 August 2014.

Untreated head and neck surgical disease in Sierra Leone: a cross-sectional, countrywide survey.

PubMed

Van Buren, Nicholas C; Groen, Reinou S; Kushner, Adam L; Samai, Mohamed; Kamara, Thaim B; Ying, Jian; Meier, Jeremy D

2014-10-01

Demonstrate how the Surgeons OverSeas Assessment of Surgical Need (SOSAS) can be used to determine the burden of head and neck (H&N) surgical disease in developing countries and identify reasons for untreated disease. Cluster randomized, cross-sectional, countrywide survey. Sierra Leone. The survey was administered to 75 of 9671 enumeration areas in Sierra Leone between January 9 and February 3, 2012, with 25 households in each cluster randomly selected for the survey. A household representative and 2 randomly selected household members were interviewed. Need for surgical care was based on participants' responses to whether they had an H&N condition that they believed needed surgical care. Of 1875 households, data were analyzed for 1843 (98%), with 3645 total respondents. Seven hundred and one H&N surgical conditions were reported as occurring during the lifetime of the 3645 respondents (19.2%).The current prevalence of H&N conditions in need of a surgical consultation was 11.8%. No money (60.1%) was the most common reason respondents reported for not receiving medical care. A bivariate analysis demonstrated that age, village type, education, and type of condition may be predictors for seeking health care and/or receiving surgical care. These results show limited access for patients to be evaluated for a potential H&N surgical condition in Sierra Leone. The true incidence of untreated surgical disease is unknown as most respondents were not evaluated by a surgeon. This survey could be used in other countries as health care professionals assess surgical needs throughout the world. © American Academy of Otolaryngology—Head and Neck Surgery Foundation 2014.

Does typing of Chlamydia trachomatis using housekeeping multilocus sequence typing reveal different sexual networks among heterosexuals and men who have sex with men?

PubMed

Versteeg, Bart; Bruisten, Sylvia M; van der Ende, Arie; Pannekoek, Yvonne

2016-04-18

Chlamydia trachomatis infections remain the most common bacterial sexually transmitted infection worldwide. To gain more insight into the epidemiology and transmission of C. trachomatis, several schemes of multilocus sequence typing (MLST) have been developed. We investigated the clustering of C. trachomatis strains derived from men who have sex with men (MSM) and heterosexuals using the MLST scheme based on 7 housekeeping genes (MLST-7) adapted for clinical specimens and a high-resolution MLST scheme based on 6 polymorphic genes, including ompA (hr-MLST-6). Specimens from 100 C. trachomatis infected men who have sex with men (MSM) and 100 heterosexual women were randomly selected from previous studies and sequenced. We adapted the MLST-7 scheme to a nested assay to be suitable for direct typing of clinical specimens. All selected specimens were typed using both the adapted MLST-7 scheme and the hr-MLST-6 scheme. Clustering of C. trachomatis strains derived from MSM and heterosexuals was assessed using minimum spanning tree analysis. Sufficient chlamydial DNA was present in 188 of the 200 (94 %) selected samples. Using the adapted MLST-7 scheme, full MLST profiles were obtained for 187 of 188 tested specimens resulting in a high success rate of 99.5 %. Of these 187 specimens, 91 (48.7 %) were from MSM and 96 (51.3 %) from heterosexuals. We detected 21 sequence types (STs) using the adapted MLST-7 and 79 STs using the hr-MLST-6 scheme. Minimum spanning tree analyses was used to examine the clustering of MLST-7 data, which showed no reflection of separate transmission in MSM and heterosexual hosts. Moreover, typing using the hr-MLST-6 scheme identified genetically related clusters within each of clusters that were identified by using the MLST-7 scheme. No distinct transmission of C. trachomatis could be observed in MSM and heterosexuals using the adapted MLST-7 scheme in contrast to using the hr-MLST-6. In addition, we compared clustering of both MLST schemes and demonstrated that typing using the hr-MLST-6 scheme is able to identify genetically related clusters of C. trachomatis strains within each of the clusters that were identified by using the MLST-7 scheme.

No Effect of Insecticide Treated Curtain Deployment on Aedes Infestation in a Cluster Randomized Trial in a Setting of Low Dengue Transmission in Guantanamo, Cuba

PubMed Central

Lambert, Isora; Montada, Domingo; Baly, Alberto; Van der Stuyft, Patrick

2015-01-01

Objective & Methodology The current study evaluated the effectiveness and cost-effectiveness of Insecticide Treated Curtain (ITC) deployment for reducing dengue vector infestation levels in the Cuban context with intensive routine control activities. A cluster randomized controlled trial took place in Guantanamo city, east Cuba. Twelve neighborhoods (about 500 households each) were selected among the ones with the highest Aedes infestation levels in the previous two years, and were randomly allocated to the intervention and control arms. Long lasting ITC (PermaNet) were distributed in the intervention clusters in March 2009. Routine control activities were continued in the whole study area. In both study arms, we monitored monthly pre- and post-intervention House Index (HI, number of houses with at least 1 container with Aedes immature stages/100 houses inspected), during 12 and 18 months respectively. We evaluated the effect of ITC deployment on HI by fitting a generalized linear regression model with a negative binomial link function to these data. Principal Findings At distribution, the ITC coverage (% of households using ≥1 ITC) reached 98.4%, with a median of 3 ITC distributed/household. After 18 months, the coverage remained 97.4%. The local Aedes species was susceptible to deltamethrin (mosquito mortality rate of 99.7%) and the residual deltamethrin activity in the ITC was within acceptable levels (mosquito mortality rate of 73.1%) after one year of curtain use. Over the 18 month observation period after ITC distribution, the adjusted HI rate ratio, intervention versus control clusters, was 1.15 (95% CI 0.57 to 2.34). The annualized cost per household of ITC implementation was 3.8 USD, against 16.8 USD for all routine ACP activities. Conclusion Deployment of ITC in a setting with already intensive routine Aedes control actions does not lead to reductions in Aedes infestation levels. PMID:25794192

A sampling design framework for monitoring secretive marshbirds

USGS Publications Warehouse

Johnson, D.H.; Gibbs, J.P.; Herzog, M.; Lor, S.; Niemuth, N.D.; Ribic, C.A.; Seamans, M.; Shaffer, T.L.; Shriver, W.G.; Stehman, S.V.; Thompson, W.L.

2009-01-01

A framework for a sampling plan for monitoring marshbird populations in the contiguous 48 states is proposed here. The sampling universe is the breeding habitat (i.e. wetlands) potentially used by marshbirds. Selection protocols would be implemented within each of large geographical strata, such as Bird Conservation Regions. Site selection will be done using a two-stage cluster sample. Primary sampling units (PSUs) would be land areas, such as legal townships, and would be selected by a procedure such as systematic sampling. Secondary sampling units (SSUs) will be wetlands or portions of wetlands in the PSUs. SSUs will be selected by a randomized spatially balanced procedure. For analysis, the use of a variety of methods as a means of increasing confidence in conclusions that may be reached is encouraged. Additional effort will be required to work out details and implement the plan.

Nutrition education improves dietary diversity of children 6-23 months at community-level: Results from a cluster randomized controlled trial in Malawi

PubMed Central

Kuchenbecker, Judith; Reinbott, Anika; Mtimuni, Beatrice; Krawinkel, Michael B.

2017-01-01

Background: Low dietary quality and quantity and inappropriate feeding practices can cause undernutrition. Poor nutritional status in early childhood is associated with growth faltering. The objective of the study was to assess the potential of community-based nutrition education to improve height-for-age z-scores in children 6–23 months of age. Methods and Findings: We carried out a cluster-randomized-controlled trial to assess the effectiveness of nutrition education. A total of 24 Extension Planning Area Sections served as clusters. The selection criteria were: the position of the extension officer was staffed and the sections had been selected by the project for activities in its first project year. The sections were randomized into intervention and control restricted on mean height for age Z-score using baseline information. In the intervention area, food security activities and community-based nutrition education was implemented. The control area received food security activities only. At baseline (2011) and endline (2014), caregivers with a child below two years of age were enrolled. Data assessment included anthropometric measurements, interviews on socio-economic status, dietary intake and feeding practices. A difference-in-differences estimator was used to calculate intervention effects. A positive impact on child dietary diversity was observed (B (SE) = 0.39 (0.15), p = 0.01; 95%CI 0.09–0.68). There was a non-significant positive intervention effect on mean height-for-age z-scores (B (SE) = 0.17 (0.12), p = 0.15; 95%CI -0.06–0.41). Limitations: The 24h dietary recalls used to measure dietary diversity did not consider quantities of consumed foods. Unrecorded poor quality of consumed foods might have masked a potential benefit of increased child dietary diversity on growth. Conclusions: Participatory community-based nutrition education for caregivers improved child dietary diversity even in a food insecure area. Nutrition education should be part of programs in food insecure settings aiming at ameliorating food insecurity among communities. PMID:28426678

Extension of mixture-of-experts networks for binary classification of hierarchical data.

PubMed

Ng, Shu-Kay; McLachlan, Geoffrey J

2007-09-01

For many applied problems in the context of medically relevant artificial intelligence, the data collected exhibit a hierarchical or clustered structure. Ignoring the interdependence between hierarchical data can result in misleading classification. In this paper, we extend the mechanism for mixture-of-experts (ME) networks for binary classification of hierarchical data. Another extension is to quantify cluster-specific information on data hierarchy by random effects via the generalized linear mixed-effects model (GLMM). The extension of ME networks is implemented by allowing for correlation in the hierarchical data in both the gating and expert networks via the GLMM. The proposed model is illustrated using a real thyroid disease data set. In our study, we consider 7652 thyroid diagnosis records from 1984 to early 1987 with complete information on 20 attribute values. We obtain 10 independent random splits of the data into a training set and a test set in the proportions 85% and 15%. The test sets are used to assess the generalization performance of the proposed model, based on the percentage of misclassifications. For comparison, the results obtained from the ME network with independence assumption are also included. With the thyroid disease data, the misclassification rate on test sets for the extended ME network is 8.9%, compared to 13.9% for the ME network. In addition, based on model selection methods described in Section 2, a network with two experts is selected. These two expert networks can be considered as modeling two groups of patients with high and low incidence rates. Significant variation among the predicted cluster-specific random effects is detected in the patient group with low incidence rate. It is shown that the extended ME network outperforms the ME network for binary classification of hierarchical data. With the thyroid disease data, useful information on the relative log odds of patients with diagnosed conditions at different periods can be evaluated. This information can be taken into consideration for the assessment of treatment planning of the disease. The proposed extended ME network thus facilitates a more general approach to incorporate data hierarchy mechanism in network modeling.

A Cluster-Randomized Trial of Insecticide-Treated Curtains for Dengue Vector Control in Thailand

PubMed Central

Lenhart, Audrey; Trongtokit, Yuwadee; Alexander, Neal; Apiwathnasorn, Chamnarn; Satimai, Wichai; Vanlerberghe, Veerle; Van der Stuyft, Patrick; McCall, Philip J.

2013-01-01

The efficacy of insecticide-treated window curtains (ITCs) for dengue vector control was evaluated in Thailand in a cluster-randomized controlled trial. A total of 2,037 houses in 26 clusters was randomized to receive the intervention or act as control (no treatment). Entomological surveys measured Aedes infestations (Breteau index, house index, container index, and pupae per person index) and oviposition indices (mean numbers of eggs laid in oviposition traps) immediately before and after intervention, and at 3-month intervals over 12 months. There were no consistent statistically significant differences in entomological indices between intervention and control clusters, although oviposition indices were lower (P < 0.01) in ITC clusters during the wet season. It is possible that the open housing structures in the study reduced the likelihood of mosquitoes making contact with ITCs. ITCs deployed in a region where this house design is common may be unsuitable for dengue vector control. PMID:23166195

Energetic Materials Effects on Essential Soil Processes: Decomposition of Orchard Grass (Dactylis glomerata) Litter in Soil Contaminated with Energetic Materials

DTIC Science & Technology

2014-02-01

moisture level of 14% dry soil mass was maintained for the duration of the study by weekly additions of ASTM Type I water. Soil samples were collected...maintain the initial soil moisture level. One cluster of Orchard grass straw was harvested from a set of randomly selected replicate containers...decomposition is among the most integrating processes within the soil ecosystem because it involves complex interactions of soil microbial, plant , and

A Cluster Randomized Controlled Trial Testing the Effectiveness of Houvast: A Strengths-Based Intervention for Homeless Young Adults

ERIC Educational Resources Information Center

Krabbenborg, Manon A. M.; Boersma, Sandra N.; van der Veld, William M.; van Hulst, Bente; Vollebergh, Wilma A. M.; Wolf, Judith R. L. M.

2017-01-01

Objective: To test the effectiveness of Houvast: a strengths-based intervention for homeless young adults. Method: A cluster randomized controlled trial was conducted with 10 Dutch shelter facilities randomly allocated to an intervention and a control group. Homeless young adults were interviewed when entering the facility and when care ended.…

Hierarchical Kohonenen net for anomaly detection in network security.

PubMed

Sarasamma, Suseela T; Zhu, Qiuming A; Huff, Julie

2005-04-01

A novel multilevel hierarchical Kohonen Net (K-Map) for an intrusion detection system is presented. Each level of the hierarchical map is modeled as a simple winner-take-all K-Map. One significant advantage of this multilevel hierarchical K-Map is its computational efficiency. Unlike other statistical anomaly detection methods such as nearest neighbor approach, K-means clustering or probabilistic analysis that employ distance computation in the feature space to identify the outliers, our approach does not involve costly point-to-point computation in organizing the data into clusters. Another advantage is the reduced network size. We use the classification capability of the K-Map on selected dimensions of data set in detecting anomalies. Randomly selected subsets that contain both attacks and normal records from the KDD Cup 1999 benchmark data are used to train the hierarchical net. We use a confidence measure to label the clusters. Then we use the test set from the same KDD Cup 1999 benchmark to test the hierarchical net. We show that a hierarchical K-Map in which each layer operates on a small subset of the feature space is superior to a single-layer K-Map operating on the whole feature space in detecting a variety of attacks in terms of detection rate as well as false positive rate.

Efficacy, Safety and Cost of Insecticide Treated Wall Lining, Insecticide Treated Bed Nets and Indoor Wall Wash with Lime for Visceral Leishmaniasis Vector Control in the Indian Sub-continent: A Multi-country Cluster Randomized Controlled Trial

PubMed Central

Das, Pradeep; Ghosh, Debashis; Priyanka, Jyoti; Matlashewski, Greg; Kroeger, Axel; Upfill-Brown, Alexander

2016-01-01

Background We investigated the efficacy, safety and cost of lime wash of household walls plus treatment of sand fly breeding places with bleach (i.e. environmental management or EM), insecticide impregnated durable wall lining (DWL), and bed net impregnation with slow release insecticide (ITN) for sand fly control in the Indian sub-continent. Methods This multi-country cluster randomized controlled trial had 24 clusters in each three sites with eight clusters per high, medium or low sand fly density stratum. Every cluster included 45–50 households. Five households from each cluster were randomly selected for entomological measurements including sand fly density and mortality at one, three, nine and twelve months post intervention. Household interviews were conducted for socioeconomic information and intervention acceptability assessment. Cost for each intervention was calculated. There was a control group without intervention. Findings Sand fly mortality [mean and 95%CI] ranged from 84% (81%-87%) at one month to 74% (71%-78%) at 12 months for DWL, 75% (71%-79%) at one month to 49% (43%-55%) at twelve months for ITN, and 44% (34%-53%) at one month to 22% (14%-29%) at twelve months for EM. Adjusted intervention effect on sand fly density measured by incidence rate ratio ranged from 0.28 (0.23–0.34) at one month to 0.62 (0.51–0.75) at 12 months for DWL; 0.72 (0.62–0.85) at one month to 1.02 (0.86–1.22) at 12 months for ITN; and 0.89 (0.76–1.03) at one months to 1.49 (1.26–1.74) at 12 months for EM. Household acceptance of EM was 74% compared to 94% for both DWL and ITN. Operational cost per household in USD was about 5, 8, and 2 for EM, DWL and ITN, respectively. Minimal adverse reactions were reported for EM and ITN while 36% of households with DWL reported transient itching. Interpretation DWL is the most effective, durable and acceptable control method followed by ITN. The Visceral Leishmaniasis (VL) Elimination Program in the Indian sub-continent should consider DWL and ITN for sand fly control in addition to IRS. PMID:27533097

An effective intervention algorithm for promoting cooperation in the prisoner's dilemma game with multiple stable states

NASA Astrophysics Data System (ADS)

Li, Y. S.; Xu, C.; Hui, P. M.

2018-07-01

Multiple stable states, hysteresis, sensitivity to initial distributions, and a control algorithm for promoting cooperation are studied in an evolutionary prisoner's dilemma with agents connected into a regular random network. A system could evolve into states of different cooperative frequencies xc in different runs, even starting with the same initial cooperative frequency xc(in) and payoff parameters. For a large reward R, some values of xc(in) either take the system to a group of low cooperative frequency (LCF) states or to a few high cooperative frequency (HCF) states. These states differ by their network structures, with cooperative players connected into ring-like structure in LCF states and compact clusters in HCF states. Hysteresis in xc is observed when R is swept down and up, when the final state of the previous R is used as the initial state of the next R. The analysis led us to propose a closed pack cluster algorithm that gives HCF states effectively. The algorithm intervenes the system at some point in time by selectively switching some non-cooperative D-agents into cooperative C-agents at the peripheral of an existing cluster of C-agents. It ensures protection of a small C-cluster from which more cooperation can be induced. Practically, a governing body may first allow a society to evolve freely and then derive suitable policy to promote selected pockets of good practices for attaining a higher level of common good.

Internal Cluster Validation on Earthquake Data in the Province of Bengkulu

NASA Astrophysics Data System (ADS)

Rini, D. S.; Novianti, P.; Fransiska, H.

2018-04-01

K-means method is an algorithm for cluster n object based on attribute to k partition, where k < n. There is a deficiency of algorithms that is before the algorithm is executed, k points are initialized randomly so that the resulting data clustering can be different. If the random value for initialization is not good, the clustering becomes less optimum. Cluster validation is a technique to determine the optimum cluster without knowing prior information from data. There are two types of cluster validation, which are internal cluster validation and external cluster validation. This study aims to examine and apply some internal cluster validation, including the Calinski-Harabasz (CH) Index, Sillhouette (S) Index, Davies-Bouldin (DB) Index, Dunn Index (D), and S-Dbw Index on earthquake data in the Bengkulu Province. The calculation result of optimum cluster based on internal cluster validation is CH index, S index, and S-Dbw index yield k = 2, DB Index with k = 6 and Index D with k = 15. Optimum cluster (k = 6) based on DB Index gives good results for clustering earthquake in the Bengkulu Province.

Mapping Health Data: Improved Privacy Protection With Donut Method Geomasking

PubMed Central

Hampton, Kristen H.; Fitch, Molly K.; Allshouse, William B.; Doherty, Irene A.; Gesink, Dionne C.; Leone, Peter A.; Serre, Marc L.; Miller, William C.

2010-01-01

A major challenge in mapping health data is protecting patient privacy while maintaining the spatial resolution necessary for spatial surveillance and outbreak identification. A new adaptive geomasking technique, referred to as the donut method, extends current methods of random displacement by ensuring a user-defined minimum level of geoprivacy. In donut method geomasking, each geocoded address is relocated in a random direction by at least a minimum distance, but less than a maximum distance. The authors compared the donut method with current methods of random perturbation and aggregation regarding measures of privacy protection and cluster detection performance by masking multiple disease field simulations under a range of parameters. Both the donut method and random perturbation performed better than aggregation in cluster detection measures. The performance of the donut method in geoprivacy measures was at least 42.7% higher and in cluster detection measures was less than 4.8% lower than that of random perturbation. Results show that the donut method provides a consistently higher level of privacy protection with a minimal decrease in cluster detection performance, especially in areas where the risk to individual geoprivacy is greatest. PMID:20817785

Mapping health data: improved privacy protection with donut method geomasking.

PubMed

Hampton, Kristen H; Fitch, Molly K; Allshouse, William B; Doherty, Irene A; Gesink, Dionne C; Leone, Peter A; Serre, Marc L; Miller, William C

2010-11-01

A major challenge in mapping health data is protecting patient privacy while maintaining the spatial resolution necessary for spatial surveillance and outbreak identification. A new adaptive geomasking technique, referred to as the donut method, extends current methods of random displacement by ensuring a user-defined minimum level of geoprivacy. In donut method geomasking, each geocoded address is relocated in a random direction by at least a minimum distance, but less than a maximum distance. The authors compared the donut method with current methods of random perturbation and aggregation regarding measures of privacy protection and cluster detection performance by masking multiple disease field simulations under a range of parameters. Both the donut method and random perturbation performed better than aggregation in cluster detection measures. The performance of the donut method in geoprivacy measures was at least 42.7% higher and in cluster detection measures was less than 4.8% lower than that of random perturbation. Results show that the donut method provides a consistently higher level of privacy protection with a minimal decrease in cluster detection performance, especially in areas where the risk to individual geoprivacy is greatest.

Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic.

PubMed

Ma, Yue; Yin, Fei; Zhang, Tao; Zhou, Xiaohua Andrew; Li, Xiaosong

2016-01-01

Spatial scan statistics are widely used in various fields. The performance of these statistics is influenced by parameters, such as maximum spatial cluster size, and can be improved by parameter selection using performance measures. Current performance measures are based on the presence of clusters and are thus inapplicable to data sets without known clusters. In this work, we propose a novel overall performance measure called maximum clustering set-proportion (MCS-P), which is based on the likelihood of the union of detected clusters and the applied dataset. MCS-P was compared with existing performance measures in a simulation study to select the maximum spatial cluster size. Results of other performance measures, such as sensitivity and misclassification, suggest that the spatial scan statistic achieves accurate results in most scenarios with the maximum spatial cluster sizes selected using MCS-P. Given that previously known clusters are not required in the proposed strategy, selection of the optimal maximum cluster size with MCS-P can improve the performance of the scan statistic in applications without identified clusters.

Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic

PubMed Central

Ma, Yue; Yin, Fei; Zhang, Tao; Zhou, Xiaohua Andrew; Li, Xiaosong

2016-01-01

Spatial scan statistics are widely used in various fields. The performance of these statistics is influenced by parameters, such as maximum spatial cluster size, and can be improved by parameter selection using performance measures. Current performance measures are based on the presence of clusters and are thus inapplicable to data sets without known clusters. In this work, we propose a novel overall performance measure called maximum clustering set–proportion (MCS-P), which is based on the likelihood of the union of detected clusters and the applied dataset. MCS-P was compared with existing performance measures in a simulation study to select the maximum spatial cluster size. Results of other performance measures, such as sensitivity and misclassification, suggest that the spatial scan statistic achieves accurate results in most scenarios with the maximum spatial cluster sizes selected using MCS-P. Given that previously known clusters are not required in the proposed strategy, selection of the optimal maximum cluster size with MCS-P can improve the performance of the scan statistic in applications without identified clusters. PMID:26820646

A Distributed Data-Gathering Protocol Using AUV in Underwater Sensor Networks.

PubMed

Khan, Jawaad Ullah; Cho, Ho-Shin

2015-08-06

In this paper, we propose a distributed data-gathering scheme using an autonomous underwater vehicle (AUV) working as a mobile sink to gather data from a randomly distributed underwater sensor network where sensor nodes are clustered around several cluster headers. Unlike conventional data-gathering schemes where the AUV visits either every node or every cluster header, the proposed scheme allows the AUV to visit some selected nodes named path-nodes in a way that reduces the overall transmission power of the sensor nodes. Monte Carlo simulations are performed to investigate the performance of the proposed scheme compared with several preexisting techniques employing the AUV in terms of total amount of energy consumption, standard deviation of each node's energy consumption, latency to gather data at a sink, and controlling overhead. Simulation results show that the proposed scheme not only reduces the total energy consumption but also distributes the energy consumption more uniformly over the network, thereby increasing the lifetime of the network.

A Distributed Data-Gathering Protocol Using AUV in Underwater Sensor Networks

PubMed Central

Khan, Jawaad Ullah; Cho, Ho-Shin

2015-01-01

In this paper, we propose a distributed data-gathering scheme using an autonomous underwater vehicle (AUV) working as a mobile sink to gather data from a randomly distributed underwater sensor network where sensor nodes are clustered around several cluster headers. Unlike conventional data-gathering schemes where the AUV visits either every node or every cluster header, the proposed scheme allows the AUV to visit some selected nodes named path-nodes in a way that reduces the overall transmission power of the sensor nodes. Monte Carlo simulations are performed to investigate the performance of the proposed scheme compared with several preexisting techniques employing the AUV in terms of total amount of energy consumption, standard deviation of each node’s energy consumption, latency to gather data at a sink, and controlling overhead. Simulation results show that the proposed scheme not only reduces the total energy consumption but also distributes the energy consumption more uniformly over the network, thereby increasing the lifetime of the network. PMID:26287189

Uniform deposition of size-selected clusters using Lissajous scanning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beniya, Atsushi; Watanabe, Yoshihide, E-mail: e0827@mosk.tytlabs.co.jp; Hirata, Hirohito

2016-05-15

Size-selected clusters can be deposited on the surface using size-selected cluster ion beams. However, because of the cross-sectional intensity distribution of the ion beam, it is difficult to define the coverage of the deposited clusters. The aggregation probability of the cluster depends on coverage, whereas cluster size on the surface depends on the position, despite the size-selected clusters are deposited. It is crucial, therefore, to deposit clusters uniformly on the surface. In this study, size-selected clusters were deposited uniformly on surfaces by scanning the cluster ions in the form of Lissajous pattern. Two sets of deflector electrodes set in orthogonalmore » directions were placed in front of the sample surface. Triangular waves were applied to the electrodes with an irrational frequency ratio to ensure that the ion trajectory filled the sample surface. The advantages of this method are simplicity and low cost of setup compared with raster scanning method. The authors further investigated CO adsorption on size-selected Pt{sub n} (n = 7, 15, 20) clusters uniformly deposited on the Al{sub 2}O{sub 3}/NiAl(110) surface and demonstrated the importance of uniform deposition.« less

X-ray versus infrared selection of distant galaxy clusters: A case study using the XMM-LSS and SpARCS cluster samples

NASA Astrophysics Data System (ADS)

Willis, J. P.; Ramos-Ceja, M. E.; Muzzin, A.; Pacaud, F.; Yee, H. K. C.; Wilson, G.

2018-04-01

We present a comparison of two samples of z > 0.8 galaxy clusters selected using different wavelength-dependent techniques and examine the physical differences between them. We consider 18 clusters from the X-ray selected XMM-LSS distant cluster survey and 92 clusters from the optical-MIR selected SpARCS cluster survey. Both samples are selected from the same approximately 9 square degree sky area and we examine them using common XMM-Newton, Spitzer-SWIRE and CFHT Legacy Survey data. Clusters from each sample are compared employing aperture measures of X-ray and MIR emission. We divide the SpARCS distant cluster sample into three sub-samples: a) X-ray bright, b) X-ray faint, MIR bright, and c) X-ray faint, MIR faint clusters. We determine that X-ray and MIR selected clusters display very similar surface brightness distributions of galaxy MIR light. In addition, the average location and amplitude of the galaxy red sequence as measured from stacked colour histograms is very similar in the X-ray and MIR-selected samples. The sub-sample of X-ray faint, MIR bright clusters displays a distribution of BCG-barycentre position offsets which extends to higher values than all other samples. This observation indicates that such clusters may exist in a more disturbed state compared to the majority of the distant cluster population sampled by XMM-LSS and SpARCS. This conclusion is supported by stacked X-ray images for the X-ray faint, MIR bright cluster sub-sample that display weak, centrally-concentrated X-ray emission, consistent with a population of growing clusters accreting from an extended envelope of material.

[Study on correction of data bias caused by different missing mechanisms in survey of medical expenditure among students enrolling in Urban Resident Basic Medical Insurance].

PubMed

Zhang, Haixia; Zhao, Junkang; Gu, Caijiao; Cui, Yan; Rong, Huiying; Meng, Fanlong; Wang, Tong

2015-05-01

The study of the medical expenditure and its influencing factors among the students enrolling in Urban Resident Basic Medical Insurance (URBMI) in Taiyuan indicated that non response bias and selection bias coexist in dependent variable of the survey data. Unlike previous studies only focused on one missing mechanism, a two-stage method to deal with two missing mechanisms simultaneously was suggested in this study, combining multiple imputation with sample selection model. A total of 1 190 questionnaires were returned by the students (or their parents) selected in child care settings, schools and universities in Taiyuan by stratified cluster random sampling in 2012. In the returned questionnaires, 2.52% existed not missing at random (NMAR) of dependent variable and 7.14% existed missing at random (MAR) of dependent variable. First, multiple imputation was conducted for MAR by using completed data, then sample selection model was used to correct NMAR in multiple imputation, and a multi influencing factor analysis model was established. Based on 1 000 times resampling, the best scheme of filling the random missing values is the predictive mean matching (PMM) method under the missing proportion. With this optimal scheme, a two stage survey was conducted. Finally, it was found that the influencing factors on annual medical expenditure among the students enrolling in URBMI in Taiyuan included population group, annual household gross income, affordability of medical insurance expenditure, chronic disease, seeking medical care in hospital, seeking medical care in community health center or private clinic, hospitalization, hospitalization canceled due to certain reason, self medication and acceptable proportion of self-paid medical expenditure. The two-stage method combining multiple imputation with sample selection model can deal with non response bias and selection bias effectively in dependent variable of the survey data.

Using Cluster Bootstrapping to Analyze Nested Data With a Few Clusters.

PubMed

Huang, Francis L

2018-04-01

Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials are performed with a low number of clusters (~20 groups). Although multilevel models are often used to analyze nested data, researchers may be concerned of potentially biased results due to having only a few groups under study. Cluster bootstrapping has been suggested as an alternative procedure when analyzing clustered data though it has seen very little use in educational and psychological studies. Using a Monte Carlo simulation that varied the number of clusters, average cluster size, and intraclass correlations, we compared standard errors using cluster bootstrapping with those derived using ordinary least squares regression and multilevel models. Results indicate that cluster bootstrapping, though more computationally demanding, can be used as an alternative procedure for the analysis of clustered data when treatment effects at the group level are of primary interest. Supplementary material showing how to perform cluster bootstrapped regressions using R is also provided.

Effect of a School-Based Supervised Tooth Brushing Program In Mexico City: A Cluster Randomized Intervention.

PubMed

Borges-Yáñez, S Aída; Castrejón-Pérez, Roberto Carlos; Camacho, María Esther Irigoyen

Large-scale school-based programs effectively provide health education and preventive strategies. SaludARTE is a school-based program, including supervised tooth brushing, implemented in 51 elementary schools in Mexico City. To assess the three-month efficacy of supervised tooth brushing in reducing dental plaque, gingival inflammation, and bleeding on probing in schoolchildren participating in SaludARTE. This was a pragmatic cluster randomized intervention, with two parallel branches. Four randomly selected schools participating in SaludARTE (n=200) and one control school, which did not participate in the program (CG) (n=50), were assessed. Clusters were not randomly allocated to intervention. The main outcomes were as follows: mean percentage gingival units with no inflammation, dental surfaces with no dental plaque, and gingival margins with no bleeding. The independent variable was supervised tooth brushing at school once a day after a meal. Guardians and children responded to a questionnaire on sociodemographic and oral hygiene practices, and children were examined dentally. Mean percentage differences were compared (baseline and follow-up). A total of 75% of guardians from the intervention group (IG) and 77% from the CG answered the questionnaire. Of these, 89.3% were women, with a mean age of 36.9±8.5 years. No differences in sociodemographic variables were observed between groups, and 151 children from the IG and 35 from the CG were examined at baseline and follow-up. Mean percentage differences for plaque-free surfaces (8.8±28.5%) and healthy gingival units (23.3%±23.2%) were significantly higher in the IG. The school-supervised tooth brushing program is effective in improving oral hygiene and had a greater impact on plaque and gingivitis than on gingival bleeding. It is necessary to reinforce the oral health education component of the program.

Reference Values of Within-District Intraclass Correlations of Academic Achievement by District Characteristics: Results from a Meta-Analysis of District-Specific Values

ERIC Educational Resources Information Center

Hedberg, E. C.; Hedges, Larry V.

2014-01-01

Randomized experiments are often considered the strongest designs to study the impact of educational interventions. Perhaps the most prevalent class of designs used in large scale education experiments is the cluster randomized design in which entire schools are assigned to treatments. In cluster randomized trials (CRTs) that assign schools to…

Disentangling giant component and finite cluster contributions in sparse random matrix spectra.

PubMed

Kühn, Reimer

2016-04-01

We describe a method for disentangling giant component and finite cluster contributions to sparse random matrix spectra, using sparse symmetric random matrices defined on Erdős-Rényi graphs as an example and test bed. Our methods apply to sparse matrices defined in terms of arbitrary graphs in the configuration model class, as long as they have finite mean degree.

Clustering, randomness and regularity in cloud fields. I - Theoretical considerations. II - Cumulus cloud fields

NASA Technical Reports Server (NTRS)

Weger, R. C.; Lee, J.; Zhu, Tianri; Welch, R. M.

1992-01-01

The current controversy existing in reference to the regularity vs. clustering in cloud fields is examined by means of analysis and simulation studies based upon nearest-neighbor cumulative distribution statistics. It is shown that the Poisson representation of random point processes is superior to pseudorandom-number-generated models and that pseudorandom-number-generated models bias the observed nearest-neighbor statistics towards regularity. Interpretation of this nearest-neighbor statistics is discussed for many cases of superpositions of clustering, randomness, and regularity. A detailed analysis is carried out of cumulus cloud field spatial distributions based upon Landsat, AVHRR, and Skylab data, showing that, when both large and small clouds are included in the cloud field distributions, the cloud field always has a strong clustering signal.

What is the role and authority of gatekeepers in cluster randomized trials in health research?

PubMed Central

2012-01-01

This article is part of a series of papers examining ethical issues in cluster randomized trials (CRTs) in health research. In the introductory paper in this series, we set out six areas of inquiry that must be addressed if the CRT is to be set on a firm ethical foundation. This paper addresses the sixth of the questions posed, namely, what is the role and authority of gatekeepers in CRTs in health research? ‘Gatekeepers’ are individuals or bodies that represent the interests of cluster members, clusters, or organizations. The need for gatekeepers arose in response to the difficulties in obtaining informed consent because of cluster randomization, cluster-level interventions, and cluster size. In this paper, we call for a more restrictive understanding of the role and authority of gatekeepers. Previous papers in this series have provided solutions to the challenges posed by informed consent in CRTs without the need to invoke gatekeepers. We considered that consent to randomization is not required when cluster members are approached for consent at the earliest opportunity and before any study interventions or data-collection procedures have started. Further, when cluster-level interventions or cluster size means that obtaining informed consent is not possible, a waiver of consent may be appropriate. In this paper, we suggest that the role of gatekeepers in protecting individual interests in CRTs should be limited. Generally, gatekeepers do not have the authority to provide proxy consent for cluster members. When a municipality or other community has a legitimate political authority that is empowered to make such decisions, cluster permission may be appropriate; however, gatekeepers may usefully protect cluster interests in other ways. Cluster consultation may ensure that the CRT addresses local health needs, and is conducted in accord with local values and customs. Gatekeepers may also play an important role in protecting the interests of organizations, such as hospitals, nursing homes, general practices, and schools. In these settings, permission to access the organization relies on resource implications and adherence to institutional policies. PMID:22834691

An Empirically Derived Taxonomy for Personality Diagnosis: Bridging Science and Practice in Conceptualizing Personality

PubMed Central

Westen, Drew; Shedler, Jonathan; Bradley, Bekh; DeFife, Jared A.

2013-01-01

Objective The authors describe a system for diagnosing personality pathology that is empirically derived, clinically relevant, and practical for day-to-day use. Method A random national sample of psychiatrists and clinical psychologists (N=1,201) described a randomly selected current patient with any degree of personality dysfunction (from minimal to severe) using the descriptors in the Shedler-Westen Assessment Procedure–II and completed additional research forms. Results The authors applied factor analysis to identify naturally occurring diagnostic groupings within the patient sample. The analysis yielded 10 clinically coherent personality diagnoses organized into three higher-order clusters: internalizing, externalizing, and borderline-dysregulated. The authors selected the most highly rated descriptors to construct a diagnostic prototype for each personality syndrome. In a second, independent sample, research interviewers and patients’ treating clinicians were able to diagnose the personality syndromes with high agreement and minimal comorbidity among diagnoses. Conclusions The empirically derived personality prototypes described here provide a framework for personality diagnosis that is both empirically based and clinically relevant. PMID:22193534

Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering

PubMed Central

2010-01-01

Background Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre-processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization. Result We evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selection (19) or clustering method (11). The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that background correction is preferable, in particular if the gene selection is successful. However, this is an area that needs to be studied further in order to draw any general conclusions. Conclusions The choice of cluster analysis, and in particular gene selection, has a large impact on the ability to cluster individuals correctly based on expression profiles. Normalization has a positive effect, but the relative performance of different normalizations is an area that needs more research. In summary, although clustering, gene selection and normalization are considered standard methods in bioinformatics, our comprehensive analysis shows that selecting the right methods, and the right combinations of methods, is far from trivial and that much is still unexplored in what is considered to be the most basic analysis of genomic data. PMID:20937082

Sampling Methods in Cardiovascular Nursing Research: An Overview.

PubMed

Kandola, Damanpreet; Banner, Davina; O'Keefe-McCarthy, Sheila; Jassal, Debbie

2014-01-01

Cardiovascular nursing research covers a wide array of topics from health services to psychosocial patient experiences. The selection of specific participant samples is an important part of the research design and process. The sampling strategy employed is of utmost importance to ensure that a representative sample of participants is chosen. There are two main categories of sampling methods: probability and non-probability. Probability sampling is the random selection of elements from the population, where each element of the population has an equal and independent chance of being included in the sample. There are five main types of probability sampling including simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. Non-probability sampling methods are those in which elements are chosen through non-random methods for inclusion into the research study and include convenience sampling, purposive sampling, and snowball sampling. Each approach offers distinct advantages and disadvantages and must be considered critically. In this research column, we provide an introduction to these key sampling techniques and draw on examples from the cardiovascular research. Understanding the differences in sampling techniques may aid nurses in effective appraisal of research literature and provide a reference pointfor nurses who engage in cardiovascular research.

A review of selection-based tests of abiotic surrogates for species representation.

PubMed

Beier, Paul; Sutcliffe, Patricia; Hjort, Jan; Faith, Daniel P; Pressey, Robert L; Albuquerque, Fabio

2015-06-01

Because conservation planners typically lack data on where species occur, environmental surrogates--including geophysical settings and climate types--have been used to prioritize sites within a planning area. We reviewed 622 evaluations of the effectiveness of abiotic surrogates in representing species in 19 study areas. Sites selected using abiotic surrogates represented more species than an equal number of randomly selected sites in 43% of tests (55% for plants) and on average improved on random selection of sites by about 8% (21% for plants). Environmental diversity (ED) (42% median improvement on random selection) and biotically informed clusters showed promising results and merit additional testing. We suggest 4 ways to improve performance of abiotic surrogates. First, analysts should consider a broad spectrum of candidate variables to define surrogates, including rarely used variables related to geographic separation, distance from coast, hydrology, and within-site abiotic diversity. Second, abiotic surrogates should be defined at fine thematic resolution. Third, sites (the landscape units prioritized within a planning area) should be small enough to ensure that surrogates reflect species' environments and to produce prioritizations that match the spatial resolution of conservation decisions. Fourth, if species inventories are available for some planning units, planners should define surrogates based on the abiotic variables that most influence species turnover in the planning area. Although species inventories increase the cost of using abiotic surrogates, a modest number of inventories could provide the data needed to select variables and evaluate surrogates. Additional tests of nonclimate abiotic surrogates are needed to evaluate the utility of conserving nature's stage as a strategy for conservation planning in the face of climate change. © 2015 Society for Conservation Biology.

The Wilcoxon signed rank test for paired comparisons of clustered data.

PubMed

Rosner, Bernard; Glynn, Robert J; Lee, Mei-Ling T

2006-03-01

The Wilcoxon signed rank test is a frequently used nonparametric test for paired data (e.g., consisting of pre- and posttreatment measurements) based on independent units of analysis. This test cannot be used for paired comparisons arising from clustered data (e.g., if paired comparisons are available for each of two eyes of an individual). To incorporate clustering, a generalization of the randomization test formulation for the signed rank test is proposed, where the unit of randomization is at the cluster level (e.g., person), while the individual paired units of analysis are at the subunit within cluster level (e.g., eye within person). An adjusted variance estimate of the signed rank test statistic is then derived, which can be used for either balanced (same number of subunits per cluster) or unbalanced (different number of subunits per cluster) data, with an exchangeable correlation structure, with or without tied values. The resulting test statistic is shown to be asymptotically normal as the number of clusters becomes large, if the cluster size is bounded. Simulation studies are performed based on simulating correlated ranked data from a signed log-normal distribution. These studies indicate appropriate type I error for data sets with > or =20 clusters and a superior power profile compared with either the ordinary signed rank test based on the average cluster difference score or the multivariate signed rank test of Puri and Sen. Finally, the methods are illustrated with two data sets, (i) an ophthalmologic data set involving a comparison of electroretinogram (ERG) data in retinitis pigmentosa (RP) patients before and after undergoing an experimental surgical procedure, and (ii) a nutritional data set based on a randomized prospective study of nutritional supplements in RP patients where vitamin E intake outside of study capsules is compared before and after randomization to monitor compliance with nutritional protocols.

Coarsening of protein clusters on subcellular drops exhibits strong and sudden size selectivity

NASA Astrophysics Data System (ADS)

Brown, Aidan; Rutenberg, Andrew

2015-03-01

Autophagy is an important process for the degradation of cellular components, with receptor proteins targeting substrates to downstream autophagy machinery. An important question is how receptor protein interactions lead to their selective accumulation on autophagy substrates. Receptor proteins have recently been observed in clusters, raising the possibility that clustering could affect autophagy selectivity. We investigate the clustering dynamics of the autophagy receptor protein NBR1. In addition to standard receptor protein domains, NBR1 has a ``J'' domain that anchors it to membranes, and a coiled-coil domain that enhances self-interaction. We model coarsening clusters of NBR1 on the surfaces of a polydisperse collection of drops, representing organelles. Despite the disconnected nature of the drop surfaces, we recover dynamical scaling of cluster sizes. Significantly, we find that at a well-defined time after coarsening begins, clusters evaporate from smaller drops and grow on larger drops. Thus, coarsening-driven size selection will localize protein clusters to larger substrates, leaving smaller substrates without clusters. This provides a possible physical mechanism for autophagy selectivity, and can explain reports of size selection during peroxisome degradation.

X-ray versus infrared selection of distant galaxy clusters: a case study using the XMM-LSS and SpARCS cluster samples

NASA Astrophysics Data System (ADS)

Willis, J. P.; Ramos-Ceja, M. E.; Muzzin, A.; Pacaud, F.; Yee, H. K. C.; Wilson, G.

2018-07-01

We present a comparison of two samples of z> 0.8 galaxy clusters selected using different wavelength-dependent techniques and examine the physical differences between them. We consider 18 clusters from the X-ray-selected XMM Large Scale Structure (LSS) distant cluster survey and 92 clusters from the optical-mid-infrared (MIR)-selected Spitzer Adaptation of the Red Sequence Cluster survey (SpARCS) cluster survey. Both samples are selected from the same approximately 9 sq deg sky area and we examine them using common XMM-Newton, Spitizer Wide-Area Infrared Extra-galactic (SWIRE) survey, and Canada-France-Hawaii Telescope Legacy Survey data. Clusters from each sample are compared employing aperture measures of X-ray and MIR emission. We divide the SpARCS distant cluster sample into three sub-samples: (i) X-ray bright, (ii) X-ray faint, MIR bright, and (iii) X-ray faint, MIR faint clusters. We determine that X-ray- and MIR-selected clusters display very similar surface brightness distributions of galaxy MIR light. In addition, the average location and amplitude of the galaxy red sequence as measured from stacked colour histograms is very similar in the X-ray- and MIR-selected samples. The sub-sample of X-ray faint, MIR bright clusters displays a distribution of brightest cluster galaxy-barycentre position offsets which extends to higher values than all other samples. This observation indicates that such clusters may exist in a more disturbed state compared to the majority of the distant cluster population sampled by XMM-LSS and SpARCS. This conclusion is supported by stacked X-ray images for the X-ray faint, MIR bright cluster sub-sample that display weak, centrally concentrated X-ray emission, consistent with a population of growing clusters accreting from an extended envelope of material.

CTX-M Enzymes: Origin and Diffusion

PubMed Central

Cantón, Rafael; González-Alba, José María; Galán, Juan Carlos

2012-01-01

CTX-M β-lactamases are considered a paradigm in the evolution of a resistance mechanism. Incorporation of different chromosomal blaCTX-M related genes from different species of Kluyvera has derived in different CTX-M clusters. In silico analyses have shown that this event has occurred at least nine times; in CTX-M-1 cluster (3), CTX-M-2 and CTX-M-9 clusters (2 each), and CTX-M-8 and CTX-M-25 clusters (1 each). This has been mainly produced by the participation of genetic mobilization units such as insertion sequences (ISEcp1 or ISCR1) and the later incorporation in hierarchical structures associated with multifaceted genetic structures including complex class 1 integrons and transposons. The capture of these blaCTX-M genes from the environment by highly mobilizable structures could have been a random event. Moreover, after incorporation within these structures, β-lactam selective force such as that exerted by cefotaxime and ceftazidime has fueled mutational events underscoring diversification of different clusters. Nevertheless, more variants of CTX-M enzymes, including those not inhibited by β-lactamase inhibitors such as clavulanic acid (IR-CTX-M variants), only obtained under in in vitro experiments, are still waiting to emerge in the clinical setting. Penetration and the later global spread of CTX-M producing organisms have been produced with the participation of the so-called “epidemic resistance plasmids” often carried in multi-drug resistant and virulent high-risk clones. All these facts but also the incorporation and co-selection of emerging resistance determinants within CTX-M producing bacteria, such as those encoding carbapenemases, depict the currently complex pandemic scenario of multi-drug resistant isolates. PMID:22485109

Sample size calculations for the design of cluster randomized trials: A summary of methodology.

PubMed

Gao, Fei; Earnest, Arul; Matchar, David B; Campbell, Michael J; Machin, David

2015-05-01

Cluster randomized trial designs are growing in popularity in, for example, cardiovascular medicine research and other clinical areas and parallel statistical developments concerned with the design and analysis of these trials have been stimulated. Nevertheless, reviews suggest that design issues associated with cluster randomized trials are often poorly appreciated and there remain inadequacies in, for example, describing how the trial size is determined and the associated results are presented. In this paper, our aim is to provide pragmatic guidance for researchers on the methods of calculating sample sizes. We focus attention on designs with the primary purpose of comparing two interventions with respect to continuous, binary, ordered categorical, incidence rate and time-to-event outcome variables. Issues of aggregate and non-aggregate cluster trials, adjustment for variation in cluster size and the effect size are detailed. The problem of establishing the anticipated magnitude of between- and within-cluster variation to enable planning values of the intra-cluster correlation coefficient and the coefficient of variation are also described. Illustrative examples of calculations of trial sizes for each endpoint type are included. Copyright © 2015 Elsevier Inc. All rights reserved.

The Chandra Strong Lens Sample: Revealing Baryonic Physics In Strong Lensing Selected Clusters

NASA Astrophysics Data System (ADS)

Bayliss, Matthew

2017-08-01

We propose for Chandra imaging of the hot intra-cluster gas in a unique new sample of 29 galaxy clusters selected purely on their strong gravitational lensing signatures. This will be the first program targeting a purely strong lensing selected cluster sample, enabling new comparisons between the ICM properties and scaling relations of strong lensing and mass/ICM selected cluster samples. Chandra imaging, combined with high precision strong lens models, ensures powerful constraints on the distribution and state of matter in the cluster cores. This represents a novel angle from which we can address the role played by baryonic physics |*| the infamous |*|gastrophysics|*| in shaping the cores of massive clusters, and opens up an exciting new galaxy cluster discovery space with Chandra.

The Chandra Strong Lens Sample: Revealing Baryonic Physics In Strong Lensing Selected Clusters

NASA Astrophysics Data System (ADS)

Bayliss, Matthew

2017-09-01

We propose for Chandra imaging of the hot intra-cluster gas in a unique new sample of 29 galaxy clusters selected purely on their strong gravitational lensing signatures. This will be the first program targeting a purely strong lensing selected cluster sample, enabling new comparisons between the ICM properties and scaling relations of strong lensing and mass/ICM selected cluster samples. Chandra imaging, combined with high precision strong lens models, ensures powerful constraints on the distribution and state of matter in the cluster cores. This represents a novel angle from which we can address the role played by baryonic physics -- the infamous ``gastrophysics''-- in shaping the cores of massive clusters, and opens up an exciting new galaxy cluster discovery space with Chandra.

Genetic parameters and signatures of selection in two divergent laying hen lines selected for feather pecking behaviour.

PubMed

Grams, Vanessa; Wellmann, Robin; Preuß, Siegfried; Grashorn, Michael A; Kjaer, Jörgen B; Bessei, Werner; Bennewitz, Jörn

2015-09-30

Feather pecking (FP) in laying hens is a well-known and multi-factorial behaviour with a genetic background. In a selection experiment, two lines were developed for 11 generations for high (HFP) and low (LFP) feather pecking, respectively. Starting with the second generation of selection, there was a constant difference in mean number of FP bouts between both lines. We used the data from this experiment to perform a quantitative genetic analysis and to map selection signatures. Pedigree and phenotypic data were available for the last six generations of both lines. Univariate quantitative genetic analyses were conducted using mixed linear and generalized mixed linear models assuming a Poisson distribution. Selection signatures were mapped using 33,228 single nucleotide polymorphisms (SNPs) genotyped on 41 HFP and 34 LFP individuals of generation 11. For each SNP, we estimated Wright's fixation index (FST). We tested the null hypothesis that FST is driven purely by genetic drift against the alternative hypothesis that it is driven by genetic drift and selection. The mixed linear model failed to analyze the LFP data because of the large number of 0s in the observation vector. The Poisson model fitted the data well and revealed a small but continuous genetic trend in both lines. Most of the 17 genome-wide significant SNPs were located on chromosomes 3 and 4. Thirteen clusters with at least two significant SNPs within an interval of 3 Mb maximum were identified. Two clusters were mapped on chromosomes 3, 4, 8 and 19. Of the 17 genome-wide significant SNPs, 12 were located within the identified clusters. This indicates a non-random distribution of significant SNPs and points to the presence of selection sweeps. Data on FP should be analysed using generalised linear mixed models assuming a Poisson distribution, especially if the number of FP bouts is small and the distribution is heavily peaked at 0. The FST-based approach was suitable to map selection signatures that need to be confirmed by linkage or association mapping.

The effect of community health worker-led education on women's health and treatment-seeking: A cluster randomised trial and nested process evaluation in Gujarat, India.

PubMed

Desai, Sapna; Mahal, Ajay; Sinha, Tara; Schellenberg, Joanna; Cousens, Simon

2017-12-01

A community-based health insurance scheme operated by the Self-Employed Women's Association in Gujarat, India reported that the leading reasons for inpatient hospitalisation claims by its members were diarrhoea, fever and hysterectomy - the latter at the average age of 37. This claims pattern raised concern regarding potentially unnecessary hospitalisation amongst low-income women. A cluster randomised trial and mixed methods process evaluation were designed to evaluate whether and how a community health worker-led education intervention amongst insured and uninsured adult women could reduce insurance claims, as well as hospitalisation and morbidity, related to diarrhoea, fever and hysterectomy. The 18-month intervention consisted of health workers providing preventive care information to women in a group setting in 14 randomly selected clusters, while health workers continued with regular activities in 14 comparison clusters. Claims data were collected from an administrative database, and four household surveys were conducted amongst a cohort of 1934 randomly selected adult women. 30% of insured women and 18% of uninsured women reported attending sessions. There was no evidence of an intervention effect on the primary outcome, insurance claims (risk ratio (RR) = 1.03; 95% confidence interval (CI) 0.81, 1.30) or secondary outcomes amongst insured and uninsured women, hospitalisation (RR = 1.05; 95% CI 0.58, 1.90) and morbidity (RR = 1.09; 95% CI 0.87, 1.38) related to the three conditions. The process evaluation suggested that participants retained knowledge from the sessions, but barriers to behaviour change were not overcome. We detected no evidence of an effect of this health worker-led intervention to decrease claims, hospitalisation and morbidity related to diarrhoea, fever and hysterectomy. Strategies that capitalise on health workers' role in the community and knowledge, as well as those that address the social determinants of diarrhoea, fever and the frequency of hysterectomy - such as water and sanitation infrastructure and access to primary gynaecological care - emerged as areas to strengthen future interventions.

Role of mtDNA haplogroups in the prevalence of osteoarthritis in different geographic populations: a meta-analysis.

PubMed

Shen, Jin-Ming; Feng, Lei; Feng, Chun

2014-01-01

Osteoarthritis (OA) is the most common form of arthritis and has become an increasingly important public-health problem. However, the pathogenesis of OA is still unclear. In recent years, its correlation with mtDNA haplogroups attracts much attention. We aimed to perform a meta-analysis to investigate the association between mtDNA haplogroups and OA. Published English or Chinese literature from PubMed, Web of Science, SDOS, and CNKI was retrieved up until April 15, 2014. Case-control or cohort studies that detected the frequency of mtDNA haplogroups in OA patients and controls were included. The quality of the included studies was evaluated by the Newcastle-Ottawa Scale (NOS) assessment. A meta-analysis was conducted to calculate pooled odds ratio (OR) with 95% confidence interval (CI) through the random or fixed effect model, which was selected based on the between-study heterogeneity assessed by Q test and I2 test. Subgroup analysis was performed to explore the origin of heterogeneity. A total of 6 case-control studies (10590 cases and 7161 controls) with an average NOS score of 6.9 were involved. For the analysis between mtDNA haplogroup J and OA, random model was selected due to high heterogeneity. No significant association was found initially (OR = 0.73, 95%CI: 0.52-1.03), however, once any study from UK population was removed the association emerged. Further subgroup analysis demonstrated that there was a significant association in Spain population (OR = 0.57, 95%CI: 0.46-0.71), but not in UK population. Also, subgroup analysis revealed that there was a significant correlation between cluster TJ and OA in Spain population (OR = 0.70, 95%CI: 0.58-0.84), although not in UK population. No significant correlation was found between haplogroup T/cluster HV/cluster KU and OA. Our current meta-analysis suggests that mtDNA haplogroup J and cluster TJ correlate with the risk of OA in Spanish population, but the associations in other populations require further investigation.

Sample size determination for GEE analyses of stepped wedge cluster randomized trials.

PubMed

Li, Fan; Turner, Elizabeth L; Preisser, John S

2018-06-19

In stepped wedge cluster randomized trials, intact clusters of individuals switch from control to intervention from a randomly assigned period onwards. Such trials are becoming increasingly popular in health services research. When a closed cohort is recruited from each cluster for longitudinal follow-up, proper sample size calculation should account for three distinct types of intraclass correlations: the within-period, the inter-period, and the within-individual correlations. Setting the latter two correlation parameters to be equal accommodates cross-sectional designs. We propose sample size procedures for continuous and binary responses within the framework of generalized estimating equations that employ a block exchangeable within-cluster correlation structure defined from the distinct correlation types. For continuous responses, we show that the intraclass correlations affect power only through two eigenvalues of the correlation matrix. We demonstrate that analytical power agrees well with simulated power for as few as eight clusters, when data are analyzed using bias-corrected estimating equations for the correlation parameters concurrently with a bias-corrected sandwich variance estimator. © 2018, The International Biometric Society.

A randomized breast-feeding promotion intervention did not reduce child obesity in Belarus.

PubMed

Kramer, Michael S; Matush, Lidia; Vanilovich, Irina; Platt, Robert W; Bogdanovich, Natalia; Sevkovskaya, Zinaida; Dzikovich, Irina; Shishko, Gyorgy; Collet, Jean-Paul; Martin, Richard M; Smith, George Davey; Gillman, Matthew W; Chalmers, Beverley; Hodnett, Ellen; Shapiro, Stanley

2009-02-01

The evidence that breast-feeding protects against obesity is based on observational studies, with potential for confounding and selection bias. This article summarizes a previously published study in which we assessed whether an intervention designed to promote exclusive and prolonged breast-feeding affects children's height, weight, adiposity, and blood pressure (BP) at age 6.5 y. The Promotion of Breastfeeding Intervention Trial (PROBIT) is a cluster-randomized trial of a breast-feeding promotion intervention based on the WHO/UNICEF Baby-Friendly Hospital Initiative. A total of 17,046 healthy breast-fed infants were enrolled from 31 Belarussian maternity hospitals and affiliated clinics, of whom 13,889 (81.5%) were followed up at 6.5 y with duplicate measurements of height, weight, waist circumference, triceps and subscapular skinfold thicknesses, systolic and diastolic BP. Analysis was based on intention to treat, with statistical adjustment for clustering within hospitals/clinics to permit inferences at the individual level. The experimental intervention led to a large increase in exclusive breast-feeding at 3 mo (43.3% vs. 6.4%, P < 0.001) and a significantly higher prevalence of any breast-feeding throughout infancy. No significant intervention effects were observed on height, BMI, adiposity measures, or BP. The breast-feeding promotion intervention resulted in substantial increases in the duration and exclusivity of breast-feeding yet did not reduce measures of adiposity at age 6.5 y. Previous reports of protective effects against obesity may reflect uncontrolled bias caused by confounding and selection.

Pressure ulcer multidisciplinary teams via telemedicine: a pragmatic cluster randomized stepped wedge trial in long term care.

PubMed

Stern, Anita; Mitsakakis, Nicholas; Paulden, Mike; Alibhai, Shabbir; Wong, Josephine; Tomlinson, George; Brooker, Ann-Sylvia; Krahn, Murray; Zwarenstein, Merrick

2014-02-24

The study was conducted to determine the clinical and cost effectiveness of enhanced multi-disciplinary teams (EMDTs) vs. 'usual care' for the treatment of pressure ulcers in long term care (LTC) facilities in Ontario, Canada We conducted a multi-method study: a pragmatic cluster randomized stepped-wedge trial, ethnographic observation and in-depth interviews, and an economic evaluation. Long term care facilities (clusters) were randomly allocated to start dates of the intervention. An advance practice nurse (APN) with expertise in skin and wound care visited intervention facilities to educate staff on pressure ulcer prevention and treatment, supported by an off-site hospital based expert multi-disciplinary wound care team via email, telephone, or video link as needed. The primary outcome was rate of reduction in pressure ulcer surface area (cm2/day) measured on before and after standard photographs by an assessor blinded to facility allocation. Secondary outcomes were time to healing, probability of healing, pressure ulcer incidence, pressure ulcer prevalence, wound pain, hospitalization, emergency department visits, utility, and cost. 12 of 15 eligible LTC facilities were randomly selected to participate and randomized to start date of the intervention following the stepped wedge design. 137 residents with a total of 259 pressure ulcers (stage 2 or greater) were recruited over the 17 month study period. No statistically significant differences were found between control and intervention periods on any of the primary or secondary outcomes. The economic evaluation demonstrated a mean reduction in direct care costs of $650 per resident compared to 'usual care'. The qualitative study suggested that onsite support by APN wound specialists was welcomed, and is responsible for reduced costs through discontinuation of expensive non evidence based treatments. Insufficient allocation of nursing home staff time to wound care may explain the lack of impact on healing. Enhanced multi-disciplinary wound care teams were cost effective, with most benefit through cost reduction initiated by APNs, but did not improve the treatment of pressure ulcers in nursing homes. Policy makers should consider the potential yield of strengthening evidence based primary care within LTC facilities, through outreach by APNs. ClinicalTrials.gov identifier NCT01232764.

Cluster headache: clinical features and therapeutic options.

PubMed

Gaul, Charly; Diener, Hans-Christoph; Müller, Oliver M

2011-08-01

Cluster headache is the most common type of trigemino-autonomic headache, affecting ca. 120 000 persons in Germany alone. The attacks of pain are in the periorbital area on one side, last 90 minutes on average, and are accompanied by trigemino-autonomic manifestations and restlessness. Most patients have episodic cluster headache; about 15% have chronic cluster headache, with greater impairment of their quality of life. The attacks often possess a circadian and seasonal rhythm. Selective literature review Oxygen inhalation and triptans are effective acute treatment for cluster attacks. First-line drugs for attack prophylaxis include verapamil and cortisone; alternatively, lithium and topiramate can be given. Short-term relief can be obtained by the subcutaneous infiltration of local anesthetics and steroids along the course of the greater occipital nerve, although most of the evidence in favor of this is not derived from randomized clinical trials. Patients whose pain is inadequately relieved by drug treatment can be offered newer, invasive treatments, such as deep brain stimulation in the hypothalamus (DBS) and bilateral occipital nerve stimulation (ONS). Pharmacotherapy for the treatment of acute attacks and for attack prophylaxis is effective in most patients. For the minority who do not gain adequate relief, newer invasive techniques are available in some referral centers. Definitive conclusions as to their value cannot yet be drawn from the available data.

Space-time clusters for early detection of grizzly bear predation.

PubMed

Kermish-Wells, Joseph; Massolo, Alessandro; Stenhouse, Gordon B; Larsen, Terrence A; Musiani, Marco

2018-01-01

Accurate detection and classification of predation events is important to determine predation and consumption rates by predators. However, obtaining this information for large predators is constrained by the speed at which carcasses disappear and the cost of field data collection. To accurately detect predation events, researchers have used GPS collar technology combined with targeted site visits. However, kill sites are often investigated well after the predation event due to limited data retrieval options on GPS collars (VHF or UHF downloading) and to ensure crew safety when working with large predators. This can lead to missing information from small-prey (including young ungulates) kill sites due to scavenging and general site deterioration (e.g., vegetation growth). We used a space-time permutation scan statistic (STPSS) clustering method (SaTScan) to detect predation events of grizzly bears ( Ursus arctos ) fitted with satellite transmitting GPS collars. We used generalized linear mixed models to verify predation events and the size of carcasses using spatiotemporal characteristics as predictors. STPSS uses a probability model to compare expected cluster size (space and time) with the observed size. We applied this method retrospectively to data from 2006 to 2007 to compare our method to random GPS site selection. In 2013-2014, we applied our detection method to visit sites one week after their occupation. Both datasets were collected in the same study area. Our approach detected 23 of 27 predation sites verified by visiting 464 random grizzly bear locations in 2006-2007, 187 of which were within space-time clusters and 277 outside. Predation site detection increased by 2.75 times (54 predation events of 335 visited clusters) using 2013-2014 data. Our GLMMs showed that cluster size and duration predicted predation events and carcass size with high sensitivity (0.72 and 0.94, respectively). Coupling GPS satellite technology with clusters using a program based on space-time probability models allows for prompt visits to predation sites. This enables accurate identification of the carcass size and increases fieldwork efficiency in predation studies.

Relaxation dynamics of maximally clustered networks

NASA Astrophysics Data System (ADS)

Klaise, Janis; Johnson, Samuel

2018-01-01

We study the relaxation dynamics of fully clustered networks (maximal number of triangles) to an unclustered state under two different edge dynamics—the double-edge swap, corresponding to degree-preserving randomization of the configuration model, and single edge replacement, corresponding to full randomization of the Erdős-Rényi random graph. We derive expressions for the time evolution of the degree distribution, edge multiplicity distribution and clustering coefficient. We show that under both dynamics networks undergo a continuous phase transition in which a giant connected component is formed. We calculate the position of the phase transition analytically using the Erdős-Rényi phenomenology.

Ranking and clustering of nodes in networks with smart teleportation

NASA Astrophysics Data System (ADS)

Lambiotte, R.; Rosvall, M.

2012-05-01

Random teleportation is a necessary evil for ranking and clustering directed networks based on random walks. Teleportation enables ergodic solutions, but the solutions must necessarily depend on the exact implementation and parametrization of the teleportation. For example, in the commonly used PageRank algorithm, the teleportation rate must trade off a heavily biased solution with a uniform solution. Here we show that teleportation to links rather than nodes enables a much smoother trade-off and effectively more robust results. We also show that, by not recording the teleportation steps of the random walker, we can further reduce the effect of teleportation with dramatic effects on clustering.

Bayesian hierarchical models for cost-effectiveness analyses that use data from cluster randomized trials.

PubMed

Grieve, Richard; Nixon, Richard; Thompson, Simon G

2010-01-01

Cost-effectiveness analyses (CEA) may be undertaken alongside cluster randomized trials (CRTs) where randomization is at the level of the cluster (for example, the hospital or primary care provider) rather than the individual. Costs (and outcomes) within clusters may be correlated so that the assumption made by standard bivariate regression models, that observations are independent, is incorrect. This study develops a flexible modeling framework to acknowledge the clustering in CEA that use CRTs. The authors extend previous Bayesian bivariate models for CEA of multicenter trials to recognize the specific form of clustering in CRTs. They develop new Bayesian hierarchical models (BHMs) that allow mean costs and outcomes, and also variances, to differ across clusters. They illustrate how each model can be applied using data from a large (1732 cases, 70 primary care providers) CRT evaluating alternative interventions for reducing postnatal depression. The analyses compare cost-effectiveness estimates from BHMs with standard bivariate regression models that ignore the data hierarchy. The BHMs show high levels of cost heterogeneity across clusters (intracluster correlation coefficient, 0.17). Compared with standard regression models, the BHMs yield substantially increased uncertainty surrounding the cost-effectiveness estimates, and altered point estimates. The authors conclude that ignoring clustering can lead to incorrect inferences. The BHMs that they present offer a flexible modeling framework that can be applied more generally to CEA that use CRTs.

Random variability explains apparent global clustering of large earthquakes

USGS Publications Warehouse

Michael, A.J.

2011-01-01

The occurrence of 5 Mw ≥ 8.5 earthquakes since 2004 has created a debate over whether or not we are in a global cluster of large earthquakes, temporarily raising risks above long-term levels. I use three classes of statistical tests to determine if the record of M ≥ 7 earthquakes since 1900 can reject a null hypothesis of independent random events with a constant rate plus localized aftershock sequences. The data cannot reject this null hypothesis. Thus, the temporal distribution of large global earthquakes is well-described by a random process, plus localized aftershocks, and apparent clustering is due to random variability. Therefore the risk of future events has not increased, except within ongoing aftershock sequences, and should be estimated from the longest possible record of events.

Cluster Randomized Trial of a Church-Based Peer Counselor and Tailored Newsletter Intervention to Promote Colorectal Cancer Screening and Physical Activity among Older African Americans

ERIC Educational Resources Information Center

Leone, Lucia A.; Allicock, Marlyn; Pignone, Michael P.; Walsh, Joan F.; Johnson, La-Shell; Armstrong-Brown, Janelle; Carr, Carol C.; Langford, Aisha; Ni, Andy; Resnicow, Ken; Campbell, Marci K.

2016-01-01

Action Through Churches in Time to Save Lives (ACTS) of Wellness was a cluster randomized controlled trial developed to promote colorectal cancer screening and physical activity (PA) within urban African American churches. Churches were recruited from North Carolina (n = 12) and Michigan (n = 7) and were randomized to intervention (n = 10) or…

Cost-Effectiveness of a Long-Term Internet-Delivered Worksite Health Promotion Programme on Physical Activity and Nutrition: A Cluster Randomized Controlled Trial

ERIC Educational Resources Information Center

Robroek, Suzan J. W.; Polinder, Suzanne; Bredt, Folef J.; Burdorf, Alex

2012-01-01

This study aims to evaluate the cost-effectiveness of a long-term workplace health promotion programme on physical activity (PA) and nutrition. In total, 924 participants enrolled in a 2-year cluster randomized controlled trial, with departments (n = 74) within companies (n = 6) as the unit of randomization. The intervention was compared with a…

Best (but oft-forgotten) practices: designing, analyzing, and reporting cluster randomized controlled trials.

PubMed

Brown, Andrew W; Li, Peng; Bohan Brown, Michelle M; Kaiser, Kathryn A; Keith, Scott W; Oakes, J Michael; Allison, David B

2015-08-01

Cluster randomized controlled trials (cRCTs; also known as group randomized trials and community-randomized trials) are multilevel experiments in which units that are randomly assigned to experimental conditions are sets of grouped individuals, whereas outcomes are recorded at the individual level. In human cRCTs, clusters that are randomly assigned are typically families, classrooms, schools, worksites, or counties. With growing interest in community-based, public health, and policy interventions to reduce obesity or improve nutrition, the use of cRCTs has increased. Errors in the design, analysis, and interpretation of cRCTs are unfortunately all too common. This situation seems to stem in part from investigator confusion about how the unit of randomization affects causal inferences and the statistical procedures required for the valid estimation and testing of effects. In this article, we provide a brief introduction and overview of the importance of cRCTs and highlight and explain important considerations for the design, analysis, and reporting of cRCTs by using published examples. © 2015 American Society for Nutrition.

Safety of Spectacles for Children's Vision: A Cluster-Randomized Controlled Trial.

PubMed

Ma, Xiaochen; Congdon, Nathan; Yi, Hongmei; Zhou, Zhongqiang; Pang, Xiaopeng; Meltzer, Mirjam E; Shi, Yaojiang; He, Mingguang; Liu, Yizhi; Rozelle, Scott

2015-11-01

To study safety of children's glasses in rural China, where fear that glasses harm vision is an important barrier for families and policy makers. Exploratory analysis from a cluster-randomized, investigator-masked, controlled trial. Among primary schools (n = 252) in western China, children were randomized by school to 1 of 3 interventions: free glasses provided in class, vouchers for free glasses at a local facility, or glasses prescriptions only (Control group). The main outcome of this analysis is uncorrected visual acuity after 8 months, adjusted for baseline acuity. Among 19 934 children randomly selected for screening, 5852 myopic (spherical equivalent refractive error ≤-0.5 diopters) eyes of 3001 children (14.7%, mean age 10.5 years) had VA ≤6/12 without glasses correctable to >6/12 with glasses, and were eligible. Among these, 1903 (32.5%), 1798 (30.7%), and 2151 (36.8%) were randomized to Control, Voucher, and Free Glasses, respectively. Intention-to-treat analyses were performed on all 1831 (96.2%), 1699 (94.5%), and 2007 (93.3%) eyes of children with follow-up in Control, Voucher, and Free Glasses groups. Final visual acuity for eyes of children in the treatment groups (Free Glasses and Voucher) was significantly better than for Control children, adjusting only for baseline visual acuity (difference of 0.023 logMAR units [0.23 vision chart lines, 95% CI: 0.03, 0.43]) or for other baseline factors as well (0.025 logMAR units [0.25 lines, 95% CI 0.04, 0.45]). We found no evidence that spectacles promote decline in uncorrected vision with aging among children. Copyright © 2015 Elsevier Inc. All rights reserved.

Mean-cluster approach indicates cell sorting time scales are determined by collective dynamics

NASA Astrophysics Data System (ADS)

Beatrici, Carine P.; de Almeida, Rita M. C.; Brunnet, Leonardo G.

2017-03-01

Cell migration is essential to cell segregation, playing a central role in tissue formation, wound healing, and tumor evolution. Considering random mixtures of two cell types, it is still not clear which cell characteristics define clustering time scales. The mass of diffusing clusters merging with one another is expected to grow as td /d +2 when the diffusion constant scales with the inverse of the cluster mass. Cell segregation experiments deviate from that behavior. Explanations for that could arise from specific microscopic mechanisms or from collective effects, typical of active matter. Here we consider a power law connecting diffusion constant and cluster mass to propose an analytic approach to model cell segregation where we explicitly take into account finite-size corrections. The results are compared with active matter model simulations and experiments available in the literature. To investigate the role played by different mechanisms we considered different hypotheses describing cell-cell interaction: differential adhesion hypothesis and different velocities hypothesis. We find that the simulations yield normal diffusion for long time intervals. Analytic and simulation results show that (i) cluster evolution clearly tends to a scaling regime, disrupted only at finite-size limits; (ii) cluster diffusion is greatly enhanced by cell collective behavior, such that for high enough tendency to follow the neighbors, cluster diffusion may become independent of cluster size; (iii) the scaling exponent for cluster growth depends only on the mass-diffusion relation, not on the detailed local segregation mechanism. These results apply for active matter systems in general and, in particular, the mechanisms found underlying the increase in cell sorting speed certainly have deep implications in biological evolution as a selection mechanism.

Case-control geographic clustering for residential histories accounting for risk factors and covariates.

PubMed

Jacquez, Geoffrey M; Meliker, Jaymie R; Avruskin, Gillian A; Goovaerts, Pierre; Kaufmann, Andy; Wilson, Mark L; Nriagu, Jerome

2006-08-03

Methods for analyzing space-time variation in risk in case-control studies typically ignore residential mobility. We develop an approach for analyzing case-control data for mobile individuals and apply it to study bladder cancer in 11 counties in southeastern Michigan. At this time data collection is incomplete and no inferences should be drawn - we analyze these data to demonstrate the novel methods. Global, local and focused clustering of residential histories for 219 cases and 437 controls is quantified using time-dependent nearest neighbor relationships. Business address histories for 268 industries that release known or suspected bladder cancer carcinogens are analyzed. A logistic model accounting for smoking, gender, age, race and education specifies the probability of being a case, and is incorporated into the cluster randomization procedures. Sensitivity of clustering to definition of the proximity metric is assessed for 1 to 75 k nearest neighbors. Global clustering is partly explained by the covariates but remains statistically significant at 12 of the 14 levels of k considered. After accounting for the covariates 26 Local clusters are found in Lapeer, Ingham, Oakland and Jackson counties, with the clusters in Ingham and Oakland counties appearing in 1950 and persisting to the present. Statistically significant focused clusters are found about the business address histories of 22 industries located in Oakland (19 clusters), Ingham (2) and Jackson (1) counties. Clusters in central and southeastern Oakland County appear in the 1930's and persist to the present day. These methods provide a systematic approach for evaluating a series of increasingly realistic alternative hypotheses regarding the sources of excess risk. So long as selection of cases and controls is population-based and not geographically biased, these tools can provide insights into geographic risk factors that were not specifically assessed in the case-control study design.

Exploring Relations Between BCG & Cluster Properties in the SPectroscopic IDentification of eROSITA Sources Survey from 0.05 < z < 0.3

NASA Astrophysics Data System (ADS)

Furnell, Kate E.; Collins, Chris A.; Kelvin, Lee S.; Clerc, Nicolas; Baldry, Ivan K.; Finoguenov, Alexis; Erfanianfar, Ghazaleh; Comparat, Johan; Schneider, Donald P.

2018-04-01

We present a sample of 329 low to intermediate redshift (0.05 < z < 0.3) brightest cluster galaxies (BCGs) in X-ray selected clusters from the SPectroscopic IDentification of eRosita Sources (SPIDERS) survey, a spectroscopic survey within Sloan Digital Sky Survey-IV (SDSS-IV). We define our BCGs by simultaneous consideration of legacy X-ray data from ROSAT, maximum likelihood outputs from an optical cluster-finder algorithm and visual inspection. Using SDSS imaging data, we fit Sérsic profiles to our BCGs in three bands (g, r, i) with SIGMA, a GALFIT-based software wrapper. We examine the reliability of our fits by running our pipeline on ˜104 psf-convolved model profiles injected into 8 random cluster fields; we then use the results of this analysis to create a robust subsample of 198 BCGs. We outline three cluster properties of interest: overall cluster X-ray luminosity (LX), cluster richness as estimated by REDMAPPER (λ) and cluster halo mass (M200), which is estimated via velocity dispersion. In general, there are significant correlations with BCG stellar mass between all three environmental properties, but no significant trends arise with either Sérsic index or effective radius. There is no major environmental dependence on the strength of the relation between effective radius and BCG stellar mass. Stellar mass therefore arises as the most important factor governing BCG morphology. Our results indicate that our sample consists of a large number of relaxed, mature clusters containing broadly homogeneous BCGs up to z ˜ 0.3, suggesting that there is little evidence for much ongoing structural evolution for BCGs in these systems.

Developing appropriate methods for cost-effectiveness analysis of cluster randomized trials.

PubMed

Gomes, Manuel; Ng, Edmond S-W; Grieve, Richard; Nixon, Richard; Carpenter, James; Thompson, Simon G

2012-01-01

Cost-effectiveness analyses (CEAs) may use data from cluster randomized trials (CRTs), where the unit of randomization is the cluster, not the individual. However, most studies use analytical methods that ignore clustering. This article compares alternative statistical methods for accommodating clustering in CEAs of CRTs. Our simulation study compared the performance of statistical methods for CEAs of CRTs with 2 treatment arms. The study considered a method that ignored clustering--seemingly unrelated regression (SUR) without a robust standard error (SE)--and 4 methods that recognized clustering--SUR and generalized estimating equations (GEEs), both with robust SE, a "2-stage" nonparametric bootstrap (TSB) with shrinkage correction, and a multilevel model (MLM). The base case assumed CRTs with moderate numbers of balanced clusters (20 per arm) and normally distributed costs. Other scenarios included CRTs with few clusters, imbalanced cluster sizes, and skewed costs. Performance was reported as bias, root mean squared error (rMSE), and confidence interval (CI) coverage for estimating incremental net benefits (INBs). We also compared the methods in a case study. Each method reported low levels of bias. Without the robust SE, SUR gave poor CI coverage (base case: 0.89 v. nominal level: 0.95). The MLM and TSB performed well in each scenario (CI coverage, 0.92-0.95). With few clusters, the GEE and SUR (with robust SE) had coverage below 0.90. In the case study, the mean INBs were similar across all methods, but ignoring clustering underestimated statistical uncertainty and the value of further research. MLMs and the TSB are appropriate analytical methods for CEAs of CRTs with the characteristics described. SUR and GEE are not recommended for studies with few clusters.

Post-combat syndromes from the Boer war to the Gulf war: a cluster analysis of their nature and attribution.

PubMed

Jones, Edgar; Hodgins-Vermaas, Robert; McCartney, Helen; Everitt, Brian; Beech, Charlotte; Poynter, Denise; Palmer, Ian; Hyams, Kenneth; Wessely, Simon

2002-02-09

To discover whether post-combat syndromes have existed after modern wars and what relation they bear to each other. Review of medical and military records of servicemen and cluster analysis of symptoms. Records for 1856 veterans randomly selected from war pension files awarded from 1872 and from the Medical Assessment Programme for Gulf war veterans. Characteristic patterns of symptom clusters and their relation to dependent variables including war, diagnosis, predisposing physical illness, and exposure to combat; and servicemen's changing attributions for post-combat disorders. Three varieties of post-combat disorder were identified-a debility syndrome (associated with the 19th and early 20th centuries), somatic syndrome (related primarily to the first world war), and a neuropsychiatric syndrome (associated with the second world war and the Gulf conflict). The era in which the war occurred was overwhelmingly the best predictor of cluster membership. All modern wars have been associated with a syndrome characterised by unexplained medical symptoms. The form that these assume, the terms used to describe them, and the explanations offered by servicemen and doctors seem to be influenced by advances in medical science, changes in the nature of warfare, and underlying cultural forces.

How to cluster in parallel with neural networks

NASA Technical Reports Server (NTRS)

Kamgar-Parsi, Behzad; Gualtieri, J. A.; Devaney, Judy E.; Kamgar-Parsi, Behrooz

1988-01-01

Partitioning a set of N patterns in a d-dimensional metric space into K clusters - in a way that those in a given cluster are more similar to each other than the rest - is a problem of interest in astrophysics, image analysis and other fields. As there are approximately K(N)/K (factorial) possible ways of partitioning the patterns among K clusters, finding the best solution is beyond exhaustive search when N is large. Researchers show that this problem can be formulated as an optimization problem for which very good, but not necessarily optimal solutions can be found by using a neural network. To do this the network must start from many randomly selected initial states. The network is simulated on the MPP (a 128 x 128 SIMD array machine), where researchers use the massive parallelism not only in solving the differential equations that govern the evolution of the network, but also by starting the network from many initial states at once, thus obtaining many solutions in one run. Researchers obtain speedups of two to three orders of magnitude over serial implementations and the promise through Analog VLSI implementations of speedups comensurate with human perceptual abilities.

The Implications of "Contamination" for Experimental Design in Education

ERIC Educational Resources Information Center

Rhoads, Christopher H.

2011-01-01

Experimental designs that randomly assign entire clusters of individuals (e.g., schools and classrooms) to treatments are frequently advocated as a way of guarding against contamination of the estimated average causal effect of treatment. However, in the absence of contamination, experimental designs that randomly assign intact clusters to…

Random covering of the circle: the configuration-space of the free deposition process

NASA Astrophysics Data System (ADS)

Huillet, Thierry

2003-12-01

Consider a circle of circumference 1. Throw at random n points, sequentially, on this circle and append clockwise an arc (or rod) of length s to each such point. The resulting random set (the free gas of rods) is a collection of a random number of clusters with random sizes. It models a free deposition process on a 1D substrate. For such processes, we shall consider the occurrence times (number of rods) and probabilities, as n grows, of the following configurations: those avoiding rod overlap (the hard-rod gas), those for which the largest gap is smaller than rod length s (the packing gas), those (parking configurations) for which hard rod and packing constraints are both fulfilled and covering configurations. Special attention is paid to the statistical properties of each such (rare) configuration in the asymptotic density domain when ns = rgr, for some finite density rgr of points. Using results from spacings in the random division of the circle, explicit large deviation rate functions can be computed in each case from state equations. Lastly, a process consisting in selecting at random one of these specific equilibrium configurations (called the observable) can be modelled. When particularized to the parking model, this system produces parking configurations differently from Rényi's random sequential adsorption model.

Fuzzy Subspace Clustering

NASA Astrophysics Data System (ADS)

Borgelt, Christian

In clustering we often face the situation that only a subset of the available attributes is relevant for forming clusters, even though this may not be known beforehand. In such cases it is desirable to have a clustering algorithm that automatically weights attributes or even selects a proper subset. In this paper I study such an approach for fuzzy clustering, which is based on the idea to transfer an alternative to the fuzzifier (Klawonn and Höppner, What is fuzzy about fuzzy clustering? Understanding and improving the concept of the fuzzifier, In: Proc. 5th Int. Symp. on Intelligent Data Analysis, 254-264, Springer, Berlin, 2003) to attribute weighting fuzzy clustering (Keller and Klawonn, Int J Uncertain Fuzziness Knowl Based Syst 8:735-746, 2000). In addition, by reformulating Gustafson-Kessel fuzzy clustering, a scheme for weighting and selecting principal axes can be obtained. While in Borgelt (Feature weighting and feature selection in fuzzy clustering, In: Proc. 17th IEEE Int. Conf. on Fuzzy Systems, IEEE Press, Piscataway, NJ, 2008) I already presented such an approach for a global selection of attributes and principal axes, this paper extends it to a cluster-specific selection, thus arriving at a fuzzy subspace clustering algorithm (Parsons, Haque, and Liu, 2004).

Searching for galaxy clusters in the Kilo-Degree Survey

NASA Astrophysics Data System (ADS)

Radovich, M.; Puddu, E.; Bellagamba, F.; Roncarelli, M.; Moscardini, L.; Bardelli, S.; Grado, A.; Getman, F.; Maturi, M.; Huang, Z.; Napolitano, N.; McFarland, J.; Valentijn, E.; Bilicki, M.

2017-02-01

Aims: In this paper, we present the tools used to search for galaxy clusters in the Kilo Degree Survey (KiDS), and our first results. Methods: The cluster detection is based on an implementation of the optimal filtering technique that enables us to identify clusters as over-densities in the distribution of galaxies using their positions on the sky, magnitudes, and photometric redshifts. The contamination and completeness of the cluster catalog are derived using mock catalogs based on the data themselves. The optimal signal to noise threshold for the cluster detection is obtained by randomizing the galaxy positions and selecting the value that produces a contamination of less than 20%. Starting from a subset of clusters detected with high significance at low redshifts, we shift them to higher redshifts to estimate the completeness as a function of redshift: the average completeness is 85%. An estimate of the mass of the clusters is derived using the richness as a proxy. Results: We obtained 1858 candidate clusters with redshift 0

Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes.

PubMed

Wada, Masayoshi; Takahashi, Hiroki; Altaf-Ul-Amin, Md; Nakamura, Kensuke; Hirai, Masami Y; Ohta, Daisaku; Kanaya, Shigehiko

2012-07-15

Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of E<10(-5)) are included in 27 clusters. Five clusters are associated with metabolism, containing P450 genes restricted to the Brassica family and predicted to be involved in secondary metabolism. Operon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system. Copyright © 2012 Elsevier B.V. All rights reserved.

Accelerating Information Retrieval from Profile Hidden Markov Model Databases.

PubMed

Tamimi, Ahmad; Ashhab, Yaqoub; Tamimi, Hashem

2016-01-01

Profile Hidden Markov Model (Profile-HMM) is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases.

EVIDENCE FOR THE UNIVERSALITY OF PROPERTIES OF RED-SEQUENCE GALAXIES IN X-RAY- AND RED-SEQUENCE-SELECTED CLUSTERS AT z ∼ 1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Foltz, R.; Wilson, G.; DeGroot, A.

We study the slope, intercept, and scatter of the color–magnitude and color–mass relations for a sample of 10 infrared red-sequence-selected clusters at z ∼ 1. The quiescent galaxies in these clusters formed the bulk of their stars above z ≳ 3 with an age spread Δt ≳ 1 Gyr. We compare UVJ color–color and spectroscopic-based galaxy selection techniques, and find a 15% difference in the galaxy populations classified as quiescent by these methods. We compare the color–magnitude relations from our red-sequence selected sample with X-ray- and photometric-redshift-selected cluster samples of similar mass and redshift. Within uncertainties, we are unable tomore » detect any difference in the ages and star formation histories of quiescent cluster members in clusters selected by different methods, suggesting that the dominant quenching mechanism is insensitive to cluster baryon partitioning at z ∼ 1.« less

Psychosocial education improves low back pain beliefs: results from a cluster randomized clinical trial (NCT00373009) in a primary prevention setting.

PubMed

George, Steven Z; Teyhen, Deydre S; Wu, Samuel S; Wright, Alison C; Dugan, Jessica L; Yang, Guijun; Robinson, Michael E; Childs, John D

2009-07-01

The general population has a pessimistic view of low back pain (LBP), and evidence-based information has been used to positively influence LBP beliefs in previously reported mass media studies. However, there is a lack of randomized trials investigating whether LBP beliefs can be modified in primary prevention settings. This cluster randomized clinical trial investigated the effect of an evidence-based psychosocial educational program (PSEP) on LBP beliefs for soldiers completing military training. A military setting was selected for this clinical trial, because LBP is a common cause of soldier disability. Companies of soldiers (n = 3,792) were recruited, and cluster randomized to receive a PSEP or no education (control group, CG). The PSEP consisted of an interactive seminar, and soldiers were issued the Back Book for reference material. The primary outcome measure was the back beliefs questionnaire (BBQ), which assesses inevitable consequences of and ability to cope with LBP. The BBQ was administered before randomization and 12 weeks later. A linear mixed model was fitted for the BBQ at the 12-week follow-up, and a generalized linear mixed model was fitted for the dichotomous outcomes on BBQ change of greater than two points. Sensitivity analyses were performed to account for drop out. BBQ scores (potential range: 9-45) improved significantly from baseline of 25.6 +/- 5.7 (mean +/- SD) to 26.9 +/- 6.2 for those receiving the PSEP, while there was a significant decline from 26.1 +/- 5.7 to 25.6 +/- 6.0 for those in the CG. The adjusted mean BBQ score at follow-up for those receiving the PSEP was 1.49 points higher than those in the CG (P < 0.0001). The adjusted odds ratio of BBQ improvement of greater than two points for those receiving the PSEP was 1.51 (95% CI = 1.22-1.86) times that of those in the CG. BBQ improvement was also mildly associated with race and college education. Sensitivity analyses suggested minimal influence of drop out. In conclusion, soldiers that received the PSEP had an improvement in their beliefs related to the inevitable consequences of and ability to cope with LBP. This is the first randomized trial to show positive influence on LBP beliefs in a primary prevention setting, and these findings have potentially important public health implications for prevention of LBP.

For whom should we use selective decontamination of the digestive tract?

PubMed

de Smet, Anne Marie G A; Bonten, Marc J M; Kluytmans, Jan A J W

2012-04-01

This review discusses the relevant studies on selective decontamination of the digestive tract (SDD) published between 2009 and mid-2011. In a multicenter cluster-randomized cross-over study in the Netherlands, SDD and selective oropharyngeal decontamination (SOD) were associated with higher survival at day 28, with a lower incidence of ICU-acquired bacteremia and with less acquisition of respiratory tract colonization with antibiotic resistant pathogens, compared to standard care. A post-hoc analysis of this study suggests that SDD might be more effective in surgical patients and SOD in nonsurgical patients. In a randomized study perioperative use of SDD in patients undergoing gastrointestinal surgery was associated with lower incidences of anastomotic leakages. A Cochrane meta-analysis, not including any of the before mentioned studies, reported a reduction of respiratory tract infections in studies by using topical antibiotics only and higher survival rates when topical antibiotics were combined with parenteral antibiotics. Recent studies show that in ICUs with low levels of antibiotic resistance, SDD and SOD improved patient outcome and reduced infections and carriage with antibiotic-resistant pathogens. The effect in settings with higher levels of antibiotic resistance remains to be determined as well as the efficacy of SDD and SOD in specific patient groups.

Unsupervised text mining for assessing and augmenting GWAS results.

PubMed

Ailem, Melissa; Role, François; Nadif, Mohamed; Demenais, Florence

2016-04-01

Text mining can assist in the analysis and interpretation of large-scale biomedical data, helping biologists to quickly and cheaply gain confirmation of hypothesized relationships between biological entities. We set this question in the context of genome-wide association studies (GWAS), an actively emerging field that contributed to identify many genes associated with multifactorial diseases. These studies allow to identify groups of genes associated with the same phenotype, but provide no information about the relationships between these genes. Therefore, our objective is to leverage unsupervised text mining techniques using text-based cosine similarity comparisons and clustering applied to candidate and random gene vectors, in order to augment the GWAS results. We propose a generic framework which we used to characterize the relationships between 10 genes reported associated with asthma by a previous GWAS. The results of this experiment showed that the similarities between these 10 genes were significantly stronger than would be expected by chance (one-sided p-value<0.01). The clustering of observed and randomly selected gene also allowed to generate hypotheses about potential functional relationships between these genes and thus contributed to the discovery of new candidate genes for asthma. Copyright © 2016 Elsevier Inc. All rights reserved.

Phase Transition Behavior in a Neutral Evolution Model

NASA Astrophysics Data System (ADS)

King, Dawn; Scott, Adam; Maric, Nevena; Bahar, Sonya

2014-03-01

The complexity of interactions among individuals and between individuals and the environment make agent based modeling ideal for studying emergent speciation. This is a dynamically complex problem that can be characterized via the critical behavior of a continuous phase transition. Concomitant with the main tenets of natural selection, we allow organisms to reproduce, mutate, and die within a neutral phenotype space. Previous work has shown phase transition behavior in an assortative mating model with variable fitness landscapes as the maximum mutation size (μ) was varied (Dees and Bahar, 2010). Similarly, this behavior was recently presented in the work of Scott et al. (2013), even on a completely neutral landscape, for bacterial-like fission as well as for assortative mating. Here we present another neutral model to investigate the `critical' phase transition behavior of three mating types - assortative, bacterial, and random - in a phenotype space as a function of the percentage of random death. Results show two types of phase transitions occurring for the parameters of the population size and the number of clusters (an analogue of species), indicating different evolutionary dynamics for system survival and clustering. This research was supported by funding from: University of Missouri Research Board and James S. McDonnell Foundation.

Autophagy selectivity through receptor clustering

NASA Astrophysics Data System (ADS)

Rutenberg, Andrew; Brown, Aidan

Substrate selectivity in autophagy requires an all-or-none cellular response. We focus on peroxisomes, for which autophagy receptor proteins NBR1 and p62 are well characterized. Using computational models, we explore the hypothesis that physical clustering of autophagy receptor proteins on the peroxisome surface provides an appropriate all-or-none response. We find that larger peroxisomes nucleate NBR1 clusters first, and lose them due to competitive coarsening last, resulting in significant size-selectivity. We then consider a secondary hypothesis that p62 inhibits NBR1 cluster formation. We find that p62 inhibition enhances size-selectivity enough that, even if there is no change of the pexophagy rate, the volume of remaining peroxisomes can significantly decrease. We find that enhanced ubiquitin levels suppress size-selectivity, and that this effect is more pronounced for individual peroxisomes. Sufficient ubiquitin allows receptor clusters to form on even the smallest peroxisomes. We conclude that NBR1 cluster formation provides a viable physical mechanism for all-or-none substrate selectivity in pexophagy. We predict that cluster formation is associated with significant size-selectivity. Now at Simon Fraser University.

Transportability of an Evidence-Based Early Childhood Intervention in a Low-Income African Country: Results of a Cluster Randomized Controlled Study.

PubMed

Huang, Keng-Yen; Nakigudde, Janet; Rhule, Dana; Gumikiriza-Onoria, Joy Louise; Abura, Gloria; Kolawole, Bukky; Ndyanabangi, Sheila; Kim, Sharon; Seidman, Edward; Ogedegbe, Gbenga; Brotman, Laurie Miller

2017-11-01

Children in Sub-Saharan Africa (SSA) are burdened by significant unmet mental health needs. Despite the successes of numerous school-based interventions for promoting child mental health, most evidence-based interventions (EBIs) are not available in SSA. This study investigated the implementation quality and effectiveness of one component of an EBI from a developed country (USA) in a SSA country (Uganda). The EBI component, Professional Development, was provided by trained Ugandan mental health professionals to Ugandan primary school teachers. It included large-group experiential training and small-group coaching to introduce and support a range of evidence-based practices (EBPs) to create nurturing and predictable classroom experiences. The study was guided by the Consolidated Framework for Implementation Research, the Teacher Training Implementation Model, and the RE-AIM evaluation framework. Effectiveness outcomes were studied using a cluster randomized design, in which 10 schools were randomized to intervention and wait-list control conditions. A total of 79 early childhood teachers participated. Teacher knowledge and the use of EBPs were assessed at baseline and immediately post-intervention (4-5 months later). A sample of 154 parents was randomly selected to report on child behavior at baseline and post-intervention. Linear mixed effect modeling was applied to examine effectiveness outcomes. Findings support the feasibility of training Ugandan mental health professionals to provide Professional Development for Ugandan teachers. Professional Development was delivered with high levels of fidelity and resulted in improved teacher EBP knowledge and the use of EBPs in the classroom, and child social competence.

Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods

NASA Astrophysics Data System (ADS)

Erener, Arzu; Sivas, A. Abdullah; Selcuk-Kestel, A. Sevtap; Düzgün, H. Sebnem

2017-07-01

All of the quantitative landslide susceptibility mapping (QLSM) methods requires two basic data types, namely, landslide inventory and factors that influence landslide occurrence (landslide influencing factors, LIF). Depending on type of landslides, nature of triggers and LIF, accuracy of the QLSM methods differs. Moreover, how to balance the number of 0 (nonoccurrence) and 1 (occurrence) in the training set obtained from the landslide inventory and how to select which one of the 1's and 0's to be included in QLSM models play critical role in the accuracy of the QLSM. Although performance of various QLSM methods is largely investigated in the literature, the challenge of training set construction is not adequately investigated for the QLSM methods. In order to tackle this challenge, in this study three different training set selection strategies along with the original data set is used for testing the performance of three different regression methods namely Logistic Regression (LR), Bayesian Logistic Regression (BLR) and Fuzzy Logistic Regression (FLR). The first sampling strategy is proportional random sampling (PRS), which takes into account a weighted selection of landslide occurrences in the sample set. The second method, namely non-selective nearby sampling (NNS), includes randomly selected sites and their surrounding neighboring points at certain preselected distances to include the impact of clustering. Selective nearby sampling (SNS) is the third method, which concentrates on the group of 1's and their surrounding neighborhood. A randomly selected group of landslide sites and their neighborhood are considered in the analyses similar to NNS parameters. It is found that LR-PRS, FLR-PRS and BLR-Whole Data set-ups, with order, yield the best fits among the other alternatives. The results indicate that in QLSM based on regression models, avoidance of spatial correlation in the data set is critical for the model's performance.

Extinction from a paleontological perspective

NASA Technical Reports Server (NTRS)

Raup, D. M.

1993-01-01

Extinction of widespread species is common in evolutionary time (millions of years) but rare in ecological time (hundreds or thousands of years). In the fossil record, there appears to be a smooth continuum between background and mass extinction; and the clustering of extinctions at mass extinctions cannot be explained by the chance coincidence of independent events. Although some extinction is selective, much is apparently random in that survivors have no recognizable superiority over victims. Extinction certainly plays an important role in evolution, but whether it is constructive or destructive has not yet been determined.

Clustering and phase transitions on a neutral landscape

NASA Astrophysics Data System (ADS)

Scott, Adam D.; King, Dawn M.; Marić, Nevena; Bahar, Sonya

2013-06-01

Recent computational studies have shown that speciation can occur under neutral conditions, i.e., when the simulated organisms all have identical fitness. These works bear comparison with mathematical studies of clustering on neutral landscapes in the context of branching and coalescing random walks. Here, we show that sympatric clustering/speciation can occur on a neutral landscape whose dimensions specify only the simulated organisms’ phenotypes. We demonstrate that clustering occurs not only in the case of assortative mating, but also in the case of asexual fission; it is not observed in the control case of random mating. We find that the population size and the number of clusters undergo a second-order non-equilibrium phase transition as the maximum mutation size is varied.

Spitzer Imaging of Planck-Herschel Dusty Proto-Clusters at z=2-3

NASA Astrophysics Data System (ADS)

Cooray, Asantha; Ma, Jingzhe; Greenslade, Joshua; Kubo, Mariko; Nayyeri, Hooshang; Clements, David; Cheng, Tai-An

2018-05-01

We have recently introduced a new proto-cluster selection technique by combing Herschel/SPIRE imaging data and Planck/HFIk all-sky survey point source catalog. These sources are identified as Planck point sources with clumps of Herschel source over-densities with far-IR colors comparable to z=0 ULIRGS redshifted to z=2 to 3. The selection is sensitive to dusty starbursts and obscured QSOs and we have recovered couple of the known proto-clusters and close to 30 new proto-clusters. The candidate proto-clusters selected from this technique have far-IR flux densities several times higher than those that are optically selected, such as using LBG selection, implying that the member galaxies are in a special phase of heightened dusty starburst and dusty QSO activity. This far-IR luminous phase may be short but likely to be necessary piece to understand the whole stellar mass assembly history of clusters. Moreover, our photo-clusters are missed in optical selections, suggesting that optically selected proto-clusters alone do not provide adequate statistics and a comparison of the far-IR and optical selected clusters may reveal the importance of the dusty stellar mass assembly. Here, we propose IRAC observations of six of the highest priority new proto-clusters, to establish the validity of the technique and to determine the total stellar mass through SED models. For a modest observing time the science program will have a substantial impact on an upcoming science topic in cosmology with implications for observations with JWST and WFIRST to understand the mass assembly in the universe.

Efficacy of community-based physiotherapy networks for patients with Parkinson's disease: a cluster-randomised trial.

PubMed

Munneke, Marten; Nijkrake, Maarten J; Keus, Samyra Hj; Kwakkel, Gert; Berendse, Henk W; Roos, Raymund Ac; Borm, George F; Adang, Eddy M; Overeem, Sebastiaan; Bloem, Bastiaan R

2010-01-01

Many patients with Parkinson's disease are treated with physiotherapy. We have developed a community-based professional network (ParkinsonNet) that involves training of a selected number of expert physiotherapists to work according to evidence-based recommendations, and structured referrals to these trained physiotherapists to increase the numbers of patients they treat. We aimed to assess the efficacy of this approach for improving health-care outcomes. Between February, 2005, and August, 2007, we did a cluster-randomised trial with 16 clusters (defined as community hospitals and their catchment area). Clusters were randomly allocated by use of a variance minimisation algorithm to ParkinsonNet care (n=8) or usual care (n=8). Patients were assessed at baseline and at 8, 16, and 24 weeks of follow-up. The primary outcome was a patient preference disability score, the patient-specific index score, at 16 weeks. Health secondary outcomes were functional mobility, mobility-related quality of life, and total societal costs over 24 weeks. Analysis was by intention to treat. This trial is registered, number NCT00330694. We included 699 patients. Baseline characteristics of the patients were comparable between the ParkinsonNet clusters (n=358) and usual-care clusters (n=341). The primary endpoint was similar for patients within the ParkinsonNet clusters (mean 47.7, SD 21.9) and control clusters (48.3, 22.4). Health secondary endpoints were also similar for patients in both study groups. Total costs over 24 weeks were lower in ParkinsonNet clusters compared with usual-care clusters (difference euro727; 95% CI 56-1399). Implementation of ParkinsonNet networks did not change health outcomes for patients living in ParkinsonNet clusters. However, health-care costs were reduced in ParkinsonNet clusters compared with usual-care clusters. ZonMw; Netherlands Organisation for Scientific Research; Dutch Parkinson's Disease Society; National Parkinson Foundation; Stichting Robuust. Copyright 2010 Elsevier Ltd. All rights reserved.

Reporting and methodological quality of sample size calculations in cluster randomized trials could be improved: a review.

PubMed

Rutterford, Clare; Taljaard, Monica; Dixon, Stephanie; Copas, Andrew; Eldridge, Sandra

2015-06-01

To assess the quality of reporting and accuracy of a priori estimates used in sample size calculations for cluster randomized trials (CRTs). We reviewed 300 CRTs published between 2000 and 2008. The prevalence of reporting sample size elements from the 2004 CONSORT recommendations was evaluated and a priori estimates compared with those observed in the trial. Of the 300 trials, 166 (55%) reported a sample size calculation. Only 36 of 166 (22%) reported all recommended descriptive elements. Elements specific to CRTs were the worst reported: a measure of within-cluster correlation was specified in only 58 of 166 (35%). Only 18 of 166 articles (11%) reported both a priori and observed within-cluster correlation values. Except in two cases, observed within-cluster correlation values were either close to or less than a priori values. Even with the CONSORT extension for cluster randomization, the reporting of sample size elements specific to these trials remains below that necessary for transparent reporting. Journal editors and peer reviewers should implement stricter requirements for authors to follow CONSORT recommendations. Authors should report observed and a priori within-cluster correlation values to enable comparisons between these over a wider range of trials. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

[Analysis of Time-to-onset of Interstitial Lung Disease after the Administration of Small Molecule Molecularly-targeted Drugs].

PubMed

Komada, Fusao

2018-01-01

　The aim of this study was to investigate the time-to-onset of drug-induced interstitial lung disease (DILD) following the administration of small molecule molecularly-targeted drugs via the use of the spontaneous adverse reaction reporting system of the Japanese Adverse Drug Event Report database. DILD datasets for afatinib, alectinib, bortezomib, crizotinib, dasatinib, erlotinib, everolimus, gefitinib, imatinib, lapatinib, nilotinib, osimertinib, sorafenib, sunitinib, temsirolimus, and tofacitinib were used to calculate the median onset times of DILD and the Weibull distribution parameters, and to perform the hierarchical cluster analysis. The median onset times of DILD for afatinib, bortezomib, crizotinib, erlotinib, gefitinib, and nilotinib were within one month. The median onset times of DILD for dasatinib, everolimus, lapatinib, osimertinib, and temsirolimus ranged from 1 to 2 months. The median onset times of the DILD for alectinib, imatinib, and tofacitinib ranged from 2 to 3 months. The median onset times of the DILD for sunitinib and sorafenib ranged from 8 to 9 months. Weibull distributions for these drugs when using the cluster analysis showed that there were 4 clusters. Cluster 1 described a subgroup with early to later onset DILD and early failure type profiles or a random failure type profile. Cluster 2 exhibited early failure type profiles or a random failure type profile with early onset DILD. Cluster 3 exhibited a random failure type profile or wear out failure type profiles with later onset DILD. Cluster 4 exhibited an early failure type profile or a random failure type profile with the latest onset DILD.

Hyperparameterization of soil moisture statistical models for North America with Ensemble Learning Models (Elm)

NASA Astrophysics Data System (ADS)

Steinberg, P. D.; Brener, G.; Duffy, D.; Nearing, G. S.; Pelissier, C.

2017-12-01

Hyperparameterization, of statistical models, i.e. automated model scoring and selection, such as evolutionary algorithms, grid searches, and randomized searches, can improve forecast model skill by reducing errors associated with model parameterization, model structure, and statistical properties of training data. Ensemble Learning Models (Elm), and the related Earthio package, provide a flexible interface for automating the selection of parameters and model structure for machine learning models common in climate science and land cover classification, offering convenient tools for loading NetCDF, HDF, Grib, or GeoTiff files, decomposition methods like PCA and manifold learning, and parallel training and prediction with unsupervised and supervised classification, clustering, and regression estimators. Continuum Analytics is using Elm to experiment with statistical soil moisture forecasting based on meteorological forcing data from NASA's North American Land Data Assimilation System (NLDAS). There Elm is using the NSGA-2 multiobjective optimization algorithm for optimizing statistical preprocessing of forcing data to improve goodness-of-fit for statistical models (i.e. feature engineering). This presentation will discuss Elm and its components, including dask (distributed task scheduling), xarray (data structures for n-dimensional arrays), and scikit-learn (statistical preprocessing, clustering, classification, regression), and it will show how NSGA-2 is being used for automate selection of soil moisture forecast statistical models for North America.

A null model for microbial diversification

PubMed Central

Straub, Timothy J.

2017-01-01

Whether prokaryotes (Bacteria and Archaea) are naturally organized into phenotypically and genetically cohesive units comparable to animal or plant species remains contested, frustrating attempts to estimate how many such units there might be, or to identify the ecological roles they play. Analyses of gene sequences in various closely related prokaryotic groups reveal that sequence diversity is typically organized into distinct clusters, and processes such as periodic selection and extensive recombination are understood to be drivers of cluster formation (“speciation”). However, observed patterns are rarely compared with those obtainable with simple null models of diversification under stochastic lineage birth and death and random genetic drift. Via a combination of simulations and analyses of core and phylogenetic marker genes, we show that patterns of diversity for the genera Escherichia, Neisseria, and Borrelia are generally indistinguishable from patterns arising under a null model. We suggest that caution should thus be taken in interpreting observed clustering as a result of selective evolutionary forces. Unknown forces do, however, appear to play a role in Helicobacter pylori, and some individual genes in all groups fail to conform to the null model. Taken together, we recommend the presented birth−death model as a null hypothesis in prokaryotic speciation studies. It is only when the real data are statistically different from the expectations under the null model that some speciation process should be invoked. PMID:28630293

Cluster-cluster correlations and constraints on the correlation hierarchy

NASA Technical Reports Server (NTRS)

Hamilton, A. J. S.; Gott, J. R., III

1988-01-01

The hypothesis that galaxies cluster around clusters at least as strongly as they cluster around galaxies imposes constraints on the hierarchy of correlation amplitudes in hierachical clustering models. The distributions which saturate these constraints are the Rayleigh-Levy random walk fractals proposed by Mandelbrot; for these fractal distributions cluster-cluster correlations are all identically equal to galaxy-galaxy correlations. If correlation amplitudes exceed the constraints, as is observed, then cluster-cluster correlations must exceed galaxy-galaxy correlations, as is observed.

Comparing the performance of cluster random sampling and integrated threshold mapping for targeting trachoma control, using computer simulation.

PubMed

Smith, Jennifer L; Sturrock, Hugh J W; Olives, Casey; Solomon, Anthony W; Brooker, Simon J

2013-01-01

Implementation of trachoma control strategies requires reliable district-level estimates of trachomatous inflammation-follicular (TF), generally collected using the recommended gold-standard cluster randomized surveys (CRS). Integrated Threshold Mapping (ITM) has been proposed as an integrated and cost-effective means of rapidly surveying trachoma in order to classify districts according to treatment thresholds. ITM differs from CRS in a number of important ways, including the use of a school-based sampling platform for children aged 1-9 and a different age distribution of participants. This study uses computerised sampling simulations to compare the performance of these survey designs and evaluate the impact of varying key parameters. Realistic pseudo gold standard data for 100 districts were generated that maintained the relative risk of disease between important sub-groups and incorporated empirical estimates of disease clustering at the household, village and district level. To simulate the different sampling approaches, 20 clusters were selected from each district, with individuals sampled according to the protocol for ITM and CRS. Results showed that ITM generally under-estimated the true prevalence of TF over a range of epidemiological settings and introduced more district misclassification according to treatment thresholds than did CRS. However, the extent of underestimation and resulting misclassification was found to be dependent on three main factors: (i) the district prevalence of TF; (ii) the relative risk of TF between enrolled and non-enrolled children within clusters; and (iii) the enrollment rate in schools. Although in some contexts the two methodologies may be equivalent, ITM can introduce a bias-dependent shift as prevalence of TF increases, resulting in a greater risk of misclassification around treatment thresholds. In addition to strengthening the evidence base around choice of trachoma survey methodologies, this study illustrates the use of a simulated approach in addressing operational research questions for trachoma but also other NTDs.

Multimorbidity and patterns of chronic conditions in a primary care population in Switzerland: a cross-sectional study

PubMed Central

Déruaz-Luyet, Anouk; N'Goran, A Alexandra; Senn, Nicolas; Bodenmann, Patrick; Pasquier, Jérôme; Widmer, Daniel; Tandjung, Ryan; Rosemann, Thomas; Frey, Peter; Streit, Sven; Zeller, Andreas; Excoffier, Sophie; Burnand, Bernard; Herzig, Lilli

2017-01-01

Objective To characterise in details a random sample of multimorbid patients in Switzerland and to evaluate the clustering of chronic conditions in that sample. Methods 100 general practitioners (GPs) each enrolled 10 randomly selected multimorbid patients aged ≥18 years old and suffering from at least three chronic conditions. The prevalence of 75 separate chronic conditions from the International Classification of Primary Care-2 (ICPC-2) was evaluated in these patients. Clusters of chronic conditions were studied in parallel. Results The final database included 888 patients. Mean (SD) patient age was 73.0 (12.0) years old. They suffered from 5.5 (2.2) chronic conditions and were prescribed 7.7 (3.5) drugs; 25.7% suffered from depression. Psychological conditions were more prevalent among younger individuals (≤66 years old). Cluster analysis of chronic conditions with a prevalence ≥5% in the sample revealed four main groups of conditions: (1) cardiovascular risk factors and conditions, (2) general age-related and metabolic conditions, (3) tobacco and alcohol dependencies, and (4) pain, musculoskeletal and psychological conditions. Conclusion Given the emerging epidemic of multimorbidity in industrialised countries, accurately depicting the multiple expressions of multimorbidity in family practices’ patients is a high priority. Indeed, even in a setting where patients have direct access to medical specialists, GPs nevertheless retain a key role as coordinators and often as the sole medical reference for multimorbid patients. PMID:28674127

Improved dengue fever prevention through innovative intervention methods in the city of Salto, Uruguay.

PubMed

Basso, César; García da Rosa, Elsa; Romero, Sonnia; González, Cristina; Lairihoy, Rosario; Roche, Ingrid; Caffera, Ruben M; da Rosa, Ricardo; Calfani, Marisel; Alfonso-Sierra, Eduardo; Petzold, Max; Kroeger, Axel; Sommerfeld, Johannes

2015-02-01

Uruguay is located at the southern border of Aedes aegypti distribution on the South American sub-continent. The reported dengue cases in the country are all imported from surrounding countries. One of the cities at higher risk of local dengue transmission is Salto, a border city with heavy traffic from dengue endemic areas. We completed an intervention study using a cluster randomized trial design in 20 randomly selected 'clusters' in Salto. The clusters were located in neighborhoods of differing geography and economic, cultural and social aspects. Entomological surveys were carried out to measure the impact of the intervention on vector densities. Through participatory processes of all stakeholders, an appropriate ecosystem management intervention was defined. Residents collected the abundant small water holding containers and the Ministry of Public Health and the Municipality of Salto were responsible for collecting and eliminating them. Additional vector breeding places were large water tanks; they were either altered so that they could not hold water any more or covered so that oviposition by mosquitoes could not take place. The response from the community and national programme managers was encouraging. The intervention evidenced opportunities for cost savings and reducing dengue vector densities (although not to statistically significant levels). The observed low vector density limits the potential reduction due to the intervention. A larger sample size is needed to obtain a statistically significant difference. © The author 2015. The World Health Organization has granted Oxford University Press permission for the reproduction of this article.

Sample Size Estimation in Cluster Randomized Educational Trials: An Empirical Bayes Approach

ERIC Educational Resources Information Center

Rotondi, Michael A.; Donner, Allan

2009-01-01

The educational field has now accumulated an extensive literature reporting on values of the intraclass correlation coefficient, a parameter essential to determining the required size of a planned cluster randomized trial. We propose here a simple simulation-based approach including all relevant information that can facilitate this task. An…

The Effectiveness of Healthy Start Home Visit Program: Cluster Randomized Controlled Trial

ERIC Educational Resources Information Center

Leung, Cynthia; Tsang, Sandra; Heung, Kitty

2015-01-01

Purpose: The study reported the effectiveness of a home visit program for disadvantaged Chinese parents with preschool children, using cluster randomized controlled trial design. Method: Participants included 191 parents and their children from 24 preschools, with 84 dyads (12 preschools) in the intervention group and 107 dyads (12 preschools) in…

Standardized Effect Size Measures for Mediation Analysis in Cluster-Randomized Trials

ERIC Educational Resources Information Center

Stapleton, Laura M.; Pituch, Keenan A.; Dion, Eric

2015-01-01

This article presents 3 standardized effect size measures to use when sharing results of an analysis of mediation of treatment effects for cluster-randomized trials. The authors discuss 3 examples of mediation analysis (upper-level mediation, cross-level mediation, and cross-level mediation with a contextual effect) with demonstration of the…

Intraclass Correlations and Covariate Outcome Correlations for Planning Two-and Three-Level Cluster-Randomized Experiments in Education

ERIC Educational Resources Information Center

Hedges, Larry V.; Hedberg, E. C.

2013-01-01

Background: Cluster-randomized experiments that assign intact groups such as schools or school districts to treatment conditions are increasingly common in educational research. Such experiments are inherently multilevel designs whose sensitivity (statistical power and precision of estimates) depends on the variance decomposition across levels.…

Fit 5 Kids TV reduction program for Latino preschoolers: A cluster randomized controlled trial

USDA-ARS?s Scientific Manuscript database

Reducing Latino preschoolers' TV viewing is needed to reduce their risk of obesity and other chronic diseases. This study's objective was to evaluate the Fit 5 Kids (F5K) TV reduction program's impact on Latino preschooler's TV viewing. The study design was a cluster randomized controlled trial (RCT...

Random phase approximation and cluster mean field studies of hard core Bose Hubbard model

NASA Astrophysics Data System (ADS)

Alavani, Bhargav K.; Gaude, Pallavi P.; Pai, Ramesh V.

2018-04-01

We investigate zero temperature and finite temperature properties of the Bose Hubbard Model in the hard core limit using Random Phase Approximation (RPA) and Cluster Mean Field Theory (CMFT). We show that our RPA calculations are able to capture quantum and thermal fluctuations significantly better than CMFT.

Intraclass Correlations and Covariate Outcome Correlations for Planning 2 and 3 Level Cluster Randomized Experiments in Education

ERIC Educational Resources Information Center

Hedges, Larry V.; Hedberg, Eric C.

2013-01-01

Background: Cluster randomized experiments that assign intact groups such as schools or school districts to treatment conditions are increasingly common in educational research. Such experiments are inherently multilevel designs whose sensitivity (statistical power and precision of estimates) depends on the variance decomposition across levels.…

Study protocol of Prednisone in episodic Cluster Headache (PredCH): a randomized, double-blind, placebo-controlled parallel group trial to evaluate the efficacy and safety of oral prednisone as an add-on therapy in the prophylactic treatment of episodic cluster headache with verapamil

PubMed Central

2013-01-01

Background Episodic cluster headache (ECH) is a primary headache disorder that severely impairs patient’s quality of life. First-line therapy in the initiation of a prophylactic treatment is verapamil. Due to its delayed onset of efficacy and the necessary slow titration of dosage for tolerability reasons prednisone is frequently added by clinicians to the initial prophylactic treatment of a cluster episode. This treatment strategy is thought to effectively reduce the number and intensity of cluster attacks in the beginning of a cluster episode (before verapamil is effective). This study will assess the efficacy and safety of oral prednisone as an add-on therapy to verapamil and compare it to a monotherapy with verapamil in the initial prophylactic treatment of a cluster episode. Methods and design PredCH is a prospective, randomized, double-blind, placebo-controlled trial with parallel study arms. Eligible patients with episodic cluster headache will be randomized to a treatment intervention with prednisone or a placebo arm. The multi-center trial will be conducted in eight German headache clinics that specialize in the treatment of ECH. Discussion PredCH is designed to assess whether oral prednisone added to first-line agent verapamil helps reduce the number and intensity of cluster attacks in the beginning of a cluster episode as compared to monotherapy with verapamil. Trial registration German Clinical Trials Register DRKS00004716 PMID:23889923

Confidence intervals for a difference between lognormal means in cluster randomization trials.

PubMed

Poirier, Julia; Zou, G Y; Koval, John

2017-04-01

Cluster randomization trials, in which intact social units are randomized to different interventions, have become popular in the last 25 years. Outcomes from these trials in many cases are positively skewed, following approximately lognormal distributions. When inference is focused on the difference between treatment arm arithmetic means, existent confidence interval procedures either make restricting assumptions or are complex to implement. We approach this problem by assuming log-transformed outcomes from each treatment arm follow a one-way random effects model. The treatment arm means are functions of multiple parameters for which separate confidence intervals are readily available, suggesting that the method of variance estimates recovery may be applied to obtain closed-form confidence intervals. A simulation study showed that this simple approach performs well in small sample sizes in terms of empirical coverage, relatively balanced tail errors, and interval widths as compared to existing methods. The methods are illustrated using data arising from a cluster randomization trial investigating a critical pathway for the treatment of community acquired pneumonia.

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

PubMed Central

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease. PMID:27977767

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

PubMed

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.

Precision of systematic and random sampling in clustered populations: habitat patches and aggregating organisms.

PubMed

McGarvey, Richard; Burch, Paul; Matthews, Janet M

2016-01-01

Natural populations of plants and animals spatially cluster because (1) suitable habitat is patchy, and (2) within suitable habitat, individuals aggregate further into clusters of higher density. We compare the precision of random and systematic field sampling survey designs under these two processes of species clustering. Second, we evaluate the performance of 13 estimators for the variance of the sample mean from a systematic survey. Replicated simulated surveys, as counts from 100 transects, allocated either randomly or systematically within the study region, were used to estimate population density in six spatial point populations including habitat patches and Matérn circular clustered aggregations of organisms, together and in combination. The standard one-start aligned systematic survey design, a uniform 10 x 10 grid of transects, was much more precise. Variances of the 10 000 replicated systematic survey mean densities were one-third to one-fifth of those from randomly allocated transects, implying transect sample sizes giving equivalent precision by random survey would need to be three to five times larger. Organisms being restricted to patches of habitat was alone sufficient to yield this precision advantage for the systematic design. But this improved precision for systematic sampling in clustered populations is underestimated by standard variance estimators used to compute confidence intervals. True variance for the survey sample mean was computed from the variance of 10 000 simulated survey mean estimates. Testing 10 published and three newly proposed variance estimators, the two variance estimators (v) that corrected for inter-transect correlation (ν₈ and ν(W)) were the most accurate and also the most precise in clustered populations. These greatly outperformed the two "post-stratification" variance estimators (ν₂ and ν₃) that are now more commonly applied in systematic surveys. Similar variance estimator performance rankings were found with a second differently generated set of spatial point populations, ν₈ and ν(W) again being the best performers in the longer-range autocorrelated populations. However, no systematic variance estimators tested were free from bias. On balance, systematic designs bring more narrow confidence intervals in clustered populations, while random designs permit unbiased estimates of (often wider) confidence interval. The search continues for better estimators of sampling variance for the systematic survey mean.

A clinical carepath for obese pregnant women: A pragmatic pilot cluster randomized controlled trial.

PubMed

McDonald, Sarah D; Viaje, Kristen A; Rooney, Rebecca A; Jarde, Alexander; Giglia, Lucia; Maxwell, Cynthia V; Small, David; Kelly, Tracy Pearce; Midwifery, B H Sc; Sabatino, Lisa; Thabane, Lehana

2018-05-17

Obese women are at increased risks for complications during pregnancy, birth and in their infants. Although guidelines have been established for the clinical care of obese pregnant women, management is sometimes suboptimal. Our goal was to determine the feasibility of implementing and testing a clinical carepath for obese pregnant women compared to standard care, in a pilot cluster randomized controlled trial (RCT). A pragmatic pilot cluster RCT was conducted, randomly allocating eight clinics to the carepath or standard care for obese pregnant women. Women were eligible if they had a prepregnancy body mass index of ≥ 30 kg/m 2 and a viable singleton < 21 weeks. The primary outcomes were the feasibility of conducting a full-scale cluster RCT (defined as > 80%: randomization of clinics, use in eligible women, and completeness of follow-up) and of the intervention (defined as > 80%: compliance with each step in the carepath, and recommendation of the carepath by clinicians to a colleague). All eight approached clinics agreed to participate and were randomized. Half of the intervention clinics used the carepath, resulting in < 80% uptake of eligible women. High follow-up (99.5%) was achieved, in 188 of 189 women. The carepath was feasible for numerous guideline-directed recommendations for screening, but less so for counselling topics. When the carepath was used in the majority of women, all clinicians, most of whom were midwives, reported they would recommend it to a colleague. The intervention group had significantly higher overall adherence to the guideline recommendations compared to control (relative risk 1.71, 95% confidence interval 1.57-1.87). In this pragmatic pilot cluster RCT, a guideline-directed clinical carepath improved some aspects of care of obese pregnant women and was recommended by clinicians, particularly midwives. A cluster RCT may not be feasible in a mix of obstetric and midwifery clinics, but may be feasible in midwifery clinics. This pragmatic pilot cluster RCT was registered on clinicaltrials.gov (identifier: NCT02534051 ).

Descriptive epidemiology of typhoid fever during an epidemic in Harare, Zimbabwe, 2012.

PubMed

Polonsky, Jonathan A; Martínez-Pino, Isabel; Nackers, Fabienne; Chonzi, Prosper; Manangazira, Portia; Van Herp, Michel; Maes, Peter; Porten, Klaudia; Luquero, Francisco J

2014-01-01

Typhoid fever remains a significant public health problem in developing countries. In October 2011, a typhoid fever epidemic was declared in Harare, Zimbabwe - the fourth enteric infection epidemic since 2008. To orient control activities, we described the epidemiology and spatiotemporal clustering of the epidemic in Dzivaresekwa and Kuwadzana, the two most affected suburbs of Harare. A typhoid fever case-patient register was analysed to describe the epidemic. To explore clustering, we constructed a dataset comprising GPS coordinates of case-patient residences and randomly sampled residential locations (spatial controls). The scale and significance of clustering was explored with Ripley K functions. Cluster locations were determined by a random labelling technique and confirmed using Kulldorff's spatial scan statistic. We analysed data from 2570 confirmed and suspected case-patients, and found significant spatiotemporal clustering of typhoid fever in two non-overlapping areas, which appeared to be linked to environmental sources. Peak relative risk was more than six times greater than in areas lying outside the cluster ranges. Clusters were identified in similar geographical ranges by both random labelling and Kulldorff's spatial scan statistic. The spatial scale at which typhoid fever clustered was highly localised, with significant clustering at distances up to 4.5 km and peak levels at approximately 3.5 km. The epicentre of infection transmission shifted from one cluster to the other during the course of the epidemic. This study demonstrated highly localised clustering of typhoid fever during an epidemic in an urban African setting, and highlights the importance of spatiotemporal analysis for making timely decisions about targetting prevention and control activities and reinforcing treatment during epidemics. This approach should be integrated into existing surveillance systems to facilitate early detection of epidemics and identify their spatial range.

Descriptive Epidemiology of Typhoid Fever during an Epidemic in Harare, Zimbabwe, 2012

PubMed Central

Polonsky, Jonathan A.; Martínez-Pino, Isabel; Nackers, Fabienne; Chonzi, Prosper; Manangazira, Portia; Van Herp, Michel; Maes, Peter; Porten, Klaudia; Luquero, Francisco J.

2014-01-01

Background Typhoid fever remains a significant public health problem in developing countries. In October 2011, a typhoid fever epidemic was declared in Harare, Zimbabwe - the fourth enteric infection epidemic since 2008. To orient control activities, we described the epidemiology and spatiotemporal clustering of the epidemic in Dzivaresekwa and Kuwadzana, the two most affected suburbs of Harare. Methods A typhoid fever case-patient register was analysed to describe the epidemic. To explore clustering, we constructed a dataset comprising GPS coordinates of case-patient residences and randomly sampled residential locations (spatial controls). The scale and significance of clustering was explored with Ripley K functions. Cluster locations were determined by a random labelling technique and confirmed using Kulldorff's spatial scan statistic. Principal Findings We analysed data from 2570 confirmed and suspected case-patients, and found significant spatiotemporal clustering of typhoid fever in two non-overlapping areas, which appeared to be linked to environmental sources. Peak relative risk was more than six times greater than in areas lying outside the cluster ranges. Clusters were identified in similar geographical ranges by both random labelling and Kulldorff's spatial scan statistic. The spatial scale at which typhoid fever clustered was highly localised, with significant clustering at distances up to 4.5 km and peak levels at approximately 3.5 km. The epicentre of infection transmission shifted from one cluster to the other during the course of the epidemic. Conclusions This study demonstrated highly localised clustering of typhoid fever during an epidemic in an urban African setting, and highlights the importance of spatiotemporal analysis for making timely decisions about targetting prevention and control activities and reinforcing treatment during epidemics. This approach should be integrated into existing surveillance systems to facilitate early detection of epidemics and identify their spatial range. PMID:25486292

Developing Appropriate Methods for Cost-Effectiveness Analysis of Cluster Randomized Trials

PubMed Central

Gomes, Manuel; Ng, Edmond S.-W.; Nixon, Richard; Carpenter, James; Thompson, Simon G.

2012-01-01

Aim. Cost-effectiveness analyses (CEAs) may use data from cluster randomized trials (CRTs), where the unit of randomization is the cluster, not the individual. However, most studies use analytical methods that ignore clustering. This article compares alternative statistical methods for accommodating clustering in CEAs of CRTs. Methods. Our simulation study compared the performance of statistical methods for CEAs of CRTs with 2 treatment arms. The study considered a method that ignored clustering—seemingly unrelated regression (SUR) without a robust standard error (SE)—and 4 methods that recognized clustering—SUR and generalized estimating equations (GEEs), both with robust SE, a “2-stage” nonparametric bootstrap (TSB) with shrinkage correction, and a multilevel model (MLM). The base case assumed CRTs with moderate numbers of balanced clusters (20 per arm) and normally distributed costs. Other scenarios included CRTs with few clusters, imbalanced cluster sizes, and skewed costs. Performance was reported as bias, root mean squared error (rMSE), and confidence interval (CI) coverage for estimating incremental net benefits (INBs). We also compared the methods in a case study. Results. Each method reported low levels of bias. Without the robust SE, SUR gave poor CI coverage (base case: 0.89 v. nominal level: 0.95). The MLM and TSB performed well in each scenario (CI coverage, 0.92–0.95). With few clusters, the GEE and SUR (with robust SE) had coverage below 0.90. In the case study, the mean INBs were similar across all methods, but ignoring clustering underestimated statistical uncertainty and the value of further research. Conclusions. MLMs and the TSB are appropriate analytical methods for CEAs of CRTs with the characteristics described. SUR and GEE are not recommended for studies with few clusters. PMID:22016450

Review of Recent Methodological Developments in Group-Randomized Trials: Part 1—Design

PubMed Central

Li, Fan; Gallis, John A.; Prague, Melanie; Murray, David M.

2017-01-01

In 2004, Murray et al. reviewed methodological developments in the design and analysis of group-randomized trials (GRTs). We have highlighted the developments of the past 13 years in design with a companion article to focus on developments in analysis. As a pair, these articles update the 2004 review. We have discussed developments in the topics of the earlier review (e.g., clustering, matching, and individually randomized group-treatment trials) and in new topics, including constrained randomization and a range of randomized designs that are alternatives to the standard parallel-arm GRT. These include the stepped-wedge GRT, the pseudocluster randomized trial, and the network-randomized GRT, which, like the parallel-arm GRT, require clustering to be accounted for in both their design and analysis. PMID:28426295

Review of Recent Methodological Developments in Group-Randomized Trials: Part 1-Design.

PubMed

Turner, Elizabeth L; Li, Fan; Gallis, John A; Prague, Melanie; Murray, David M

2017-06-01

In 2004, Murray et al. reviewed methodological developments in the design and analysis of group-randomized trials (GRTs). We have highlighted the developments of the past 13 years in design with a companion article to focus on developments in analysis. As a pair, these articles update the 2004 review. We have discussed developments in the topics of the earlier review (e.g., clustering, matching, and individually randomized group-treatment trials) and in new topics, including constrained randomization and a range of randomized designs that are alternatives to the standard parallel-arm GRT. These include the stepped-wedge GRT, the pseudocluster randomized trial, and the network-randomized GRT, which, like the parallel-arm GRT, require clustering to be accounted for in both their design and analysis.

Applying the Anderson-Darling test to suicide clusters: evidence of contagion at U. S. universities?

PubMed

MacKenzie, Donald W

2013-01-01

Suicide clusters at Cornell University and the Massachusetts Institute of Technology (MIT) prompted popular and expert speculation of suicide contagion. However, some clustering is to be expected in any random process. This work tested whether suicide clusters at these two universities differed significantly from those expected under a homogeneous Poisson process, in which suicides occur randomly and independently of one another. Suicide dates were collected for MIT and Cornell for 1990-2012. The Anderson-Darling statistic was used to test the goodness-of-fit of the intervals between suicides to distribution expected under the Poisson process. Suicides at MIT were consistent with the homogeneous Poisson process, while those at Cornell showed clustering inconsistent with such a process (p = .05). The Anderson-Darling test provides a statistically powerful means to identify suicide clustering in small samples. Practitioners can use this method to test for clustering in relevant communities. The difference in clustering behavior between the two institutions suggests that more institutions should be studied to determine the prevalence of suicide clustering in universities and its causes.

Cluster mass inference via random field theory.

PubMed

Zhang, Hui; Nichols, Thomas E; Johnson, Timothy D

2009-01-01

Cluster extent and voxel intensity are two widely used statistics in neuroimaging inference. Cluster extent is sensitive to spatially extended signals while voxel intensity is better for intense but focal signals. In order to leverage strength from both statistics, several nonparametric permutation methods have been proposed to combine the two methods. Simulation studies have shown that of the different cluster permutation methods, the cluster mass statistic is generally the best. However, to date, there is no parametric cluster mass inference available. In this paper, we propose a cluster mass inference method based on random field theory (RFT). We develop this method for Gaussian images, evaluate it on Gaussian and Gaussianized t-statistic images and investigate its statistical properties via simulation studies and real data. Simulation results show that the method is valid under the null hypothesis and demonstrate that it can be more powerful than the cluster extent inference method. Further, analyses with a single subject and a group fMRI dataset demonstrate better power than traditional cluster size inference, and good accuracy relative to a gold-standard permutation test.

Using Geographic Information Systems and Spatial Analysis Methods to Assess Household Water Access and Sanitation Coverage in the SHINE Trial.

PubMed

Ntozini, Robert; Marks, Sara J; Mangwadu, Goldberg; Mbuya, Mduduzi N N; Gerema, Grace; Mutasa, Batsirai; Julian, Timothy R; Schwab, Kellogg J; Humphrey, Jean H; Zungu, Lindiwe I

2015-12-15

Access to water and sanitation are important determinants of behavioral responses to hygiene and sanitation interventions. We estimated cluster-specific water access and sanitation coverage to inform a constrained randomization technique in the SHINE trial. Technicians and engineers inspected all public access water sources to ascertain seasonality, function, and geospatial coordinates. Households and water sources were mapped using open-source geospatial software. The distance from each household to the nearest perennial, functional, protected water source was calculated, and for each cluster, the median distance and the proportion of households within <500 m and >1500 m of such a water source. Cluster-specific sanitation coverage was ascertained using a random sample of 13 households per cluster. These parameters were included as covariates in randomization to optimize balance in water and sanitation access across treatment arms at the start of the trial. The observed high variability between clusters in both parameters suggests that constraining on these factors was needed to reduce risk of bias. © The Author 2015. Published by Oxford University Press for the Infectious Diseases Society of America.

Cluster-randomized controlled trial of the effects of free glasses on purchase of children's glasses in China: The PRICE (Potentiating Rural Investment in Children's Eyecare) study

PubMed Central

Wang, Xiuqin; Ma, Yue; Hu, Min; Zhou, Yuan; Liao, Weiqi; Jin, Ling; Xiao, Baixiang; Wu, Xiaoyi; Ni, Ming; Yi, Hongmei; Huang, Yiwen; Varga, Beatrice; Zhang, Hong; Cun, Yongkang; Li, Xianshun; Yang, Luhua; Liang, Chaoguang; Huang, Wan; Rozelle, Scott; Ma, Xiaochen

2017-01-01

Background Offering free glasses can be important to increase children’s wear. We sought to assess whether “Upgrade glasses” could avoid reduced glasses sales when offering free glasses to children in China. Methods In this cluster-randomized, controlled trial, children with uncorrected visual acuity (VA)< = 6/12 in either eye correctable to >6/12 in both eyes at 138 randomly-selected primary schools in 9 counties in Guangdong and Yunnan provinces, China, were randomized by school to one of four groups: glasses prescription only (Control); Free Glasses; Free Glasses + offer of $15 Upgrade Glasses; Free Glasses + offer of $30 Upgrade Glasses. Spectacle purchase (main outcome) was assessed 6 months after randomization. Results Among 10,234 children screened, 882 (8.62%, mean age 10.6 years, 45.5% boys) were eligible and randomized: 257 (29.1%) at 37 schools to Control; 253 (28.7%) at 32 schools to Free Glasses; 187 (21.2%) at 31 schools to Free Glasses + $15 Upgrade; and 185 (21.0%) at 27 schools to Free Glasses +$30 Upgrade. Baseline ownership among these children needing glasses was 11.8% (104/882), and 867 (98.3%) children completed follow-up. Glasses purchase was significantly less likely when free glasses were given: Control: 59/250 = 23.6%; Free glasses: 32/252 = 12.7%, P = 0.010. Offering Upgrade Glasses eliminated this difference: Free + $15 Upgrade: 39/183 = 21.3%, multiple regression relative risk (RR) 0.90 (0.56–1.43), P = 0.65; Free + $30 Upgrade: 38/182 = 20.9%, RR 0.91 (0.59, 1.42), P = 0.69. Conclusions Upgrade glasses can prevent reductions in glasses purchase when free spectacles are provided, providing important program income. Trial registration ClinicalTrials.gov Identifier: NCT02231606. Registered on 31 August 2014. PMID:29161286

The Effect of Single or Repeated Home Visits on the Hanging and Use of Insecticide-Treated Mosquito Nets following a Mass Distribution Campaign - A Cluster Randomized, Controlled Trial

PubMed Central

Kilian, Albert; Balayo, Connie; Feldman, Mitra; Koenker, Hannah; Lokko, Kojo; Ashton, Ruth A.; Bruce, Jane; Lynch, Matthew; Boulay, Marc

2015-01-01

Background Study objective was to evaluate the effectiveness of commonly used post-campaign hang-up visits on the hanging and use of campaign nets. Methods A cluster-randomized trial was carried out in Uganda following an ITN distribution campaign. Five clusters (parishes, consisting of ∼11 villages each) were randomly selected for each of the three study arms with between 7,534 and 9,401 households per arm. Arm 1 received one hang-up visit, while Arm 2 received two visits by volunteers four and seven months after the campaign. Visits consisted of assistance hanging the net and education on net use. The control arm was only exposed to messages during the campaign itself. Three cross-sectional surveys with a two-stage cluster sampling design, representative of the study populations, were carried out to capture the two key outcome variables of net hanging and ITN use. Sample size was calculated to detect at least a 15 percentage-points change in net use, and was 1811 at endline. The analysis used an intention-to-treat approach. Findings Both hanging and use of ITN increased during follow-up in a similar way in all three study arms. The proportion of the population using an ITN the previous night was 64.0% (95% CI 60.8, 67.2), for one additional visit, 68.2% (63.8, 72.2) for two visits and 64.0% (59.4, 68.5) for the control. The proportion of households with all campaign nets hanging increased from 55.7% to 72.5% at endline (p<0.0005 for trend), with no difference between study arms. Financial cost per household visited was estimated as USD 2.33 for the first visit and USD 2.24 for the second. Conclusions Behavior change communication provided during the campaign or through other channels was sufficient to induce high levels of net hanging and use and additional “hang-up” activities were not cost-effective. PMID:25774676

Evaluation of genetic diversity in Chinese kale (Brassica oleracea L. var. alboglabra Bailey) by using rapid amplified polymorphic DNA and sequence-related amplified polymorphism markers.

PubMed

Zhang, J; Zhang, L G

2014-02-14

Chinese kale is an original Chinese vegetable of the Cruciferae family. To select suitable parents for hybrid breeding, we thoroughly analyzed the genetic diversity of Chinese kale. Random amplified polymorphic DNA (RAPD) and sequence-related amplified polymorphism (SRAP) molecular markers were used to evaluate the genetic diversity across 21 Chinese kale accessions from AVRDC and Guangzhou in China. A total of 104 bands were detected by 11 RAPD primers, of which 66 (63.5%) were polymorphic, and 229 polymorphic bands (68.4%) were observed in 335 bands amplified by 17 SRAP primer combinations. The dendrogram showed the grouping of the 21 accessions into 4 main clusters based on RAPD data, and into 6 clusters based on SRAP and combined data (RAPD + SRAP). The clustering of accessions based on SRAP data was consistent with petal colors. The Mantel test indicated a poor fit for the RAPD and SRAP data (r = 0.16). These results have an important implication for Chinese kale germplasm characterization and improvement.

Ternary alloy material prediction using genetic algorithm and cluster expansion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Chong

2015-12-01

This thesis summarizes our study on the crystal structures prediction of Fe-V-Si system using genetic algorithm and cluster expansion. Our goal is to explore and look for new stable compounds. We started from the current ten known experimental phases, and calculated formation energies of those compounds using density functional theory (DFT) package, namely, VASP. The convex hull was generated based on the DFT calculations of the experimental known phases. Then we did random search on some metal rich (Fe and V) compositions and found that the lowest energy structures were body centered cube (bcc) underlying lattice, under which we didmore » our computational systematic searches using genetic algorithm and cluster expansion. Among hundreds of the searched compositions, thirteen were selected and DFT formation energies were obtained by VASP. The stability checking of those thirteen compounds was done in reference to the experimental convex hull. We found that the composition, 24-8-16, i.e., Fe 3VSi 2 is a new stable phase and it can be very inspiring to the future experiments.« less

An insight into the distribution, genetic diversity, and mycotoxin production of Aspergillus section Flavi in soils of pistachio orchards.

PubMed

Jamali, Mojdeh; Ebrahimi, Mohammad-Ali; Karimipour, Morteza; Shams-Ghahfarokhi, Masoomeh; Dinparast-Djadid, Navid; Kalantari, Sanaz; Pilehvar-Soltanahmadi, Yones; Amani, Akram; Razzaghi-Abyaneh, Mehdi

2012-01-01

In the present study, 193 Aspergillus strains were isolated from a total of 100 soil samples of pistachio orchards, which all of them were identified as Aspergillus flavus as the most abundant species of Aspergillus section Flavi existing in the environment. Approximately 59%, 81%, and 61% of the isolates were capable of producing aflatoxins (AFs), cyclopiazonic acid (CPA), and sclerotia, respectively. The isolates were classified into four chemotypes (I to IV) based on the ability to produce AFs and CPA. The resulting dendrogram of random amplified polymorphic DNA (RAPD) analysis of 24 selected A. flavus isolates demonstrated the formation of two separate clusters. Cluster 1 contained both aflatoxigenic and non-aflatoxigenic isolates (17 isolates), whereas cluster 2 comprised only aflatoxigenic isolates (7 isolates). All the isolates of cluster 2 produced significantly higher levels of AFs than those of cluster 1 and the isolates that produced both AFB(1) and AFB(2) were found only in cluster 2. RAPD genotyping allowed the differentiation of A. flavus from Aspergillus parasiticus as a closely related species within section Flavi. The present study has provided for the first time the relevant information on distribution and genetic diversity of different A. flavus populations from nontoxigenic to highly toxigenic enable to produce hazardous amounts of AFB(1) and CPA in soils of pistachio orchards. These fungi, either toxigenic or not-toxigenic, should be considered as potential threats for agriculture and public health.

Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra

NASA Astrophysics Data System (ADS)

Shan, Jiajia; Wang, Xue; Zhou, Hao; Han, Shuqing; Riza, Dimas Firmanda Al; Kondo, Naoshi

2018-04-01

Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models’ performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra.

PubMed

Shan, Jiajia; Wang, Xue; Zhou, Hao; Han, Shuqing; Riza, Dimas Firmanda Al; Kondo, Naoshi

2018-03-13

Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models' performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

Estimating overall exposure effects for the clustered and censored outcome using random effect Tobit regression models.

PubMed

Wang, Wei; Griswold, Michael E

2016-11-30

The random effect Tobit model is a regression model that accommodates both left- and/or right-censoring and within-cluster dependence of the outcome variable. Regression coefficients of random effect Tobit models have conditional interpretations on a constructed latent dependent variable and do not provide inference of overall exposure effects on the original outcome scale. Marginalized random effects model (MREM) permits likelihood-based estimation of marginal mean parameters for the clustered data. For random effect Tobit models, we extend the MREM to marginalize over both the random effects and the normal space and boundary components of the censored response to estimate overall exposure effects at population level. We also extend the 'Average Predicted Value' method to estimate the model-predicted marginal means for each person under different exposure status in a designated reference group by integrating over the random effects and then use the calculated difference to assess the overall exposure effect. The maximum likelihood estimation is proposed utilizing a quasi-Newton optimization algorithm with Gauss-Hermite quadrature to approximate the integration of the random effects. We use these methods to carefully analyze two real datasets. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Use of LANDSAT imagery for wildlife habitat mapping in northeast and eastcentral Alaska

NASA Technical Reports Server (NTRS)

Lent, P. C. (Principal Investigator)

1976-01-01

The author has identified the following significant results. There is strong indication that spatially rare feature classes may be missed in clustering classifications based on 2% random sampling. Therefore, it seems advisable to augment random sampling for cluster analysis with directed sampling of any spatially rare features which are relevant to the analysis.

Efficacy of a Universal Parent Training Program (HOPE-20): Cluster Randomized Controlled Trial

ERIC Educational Resources Information Center

Leung, Cynthia; Tsang, Sandra; Kwan, H. W.

2017-01-01

Objective: This study examined the efficacy of Hands-On Parent Empowerment-20 (HOPE-20) program. Methods: Eligible participants were parents residing in Hong Kong with target children aged 2 years attending nursery schools. Cluster randomized control trial was adopted, with 10 schools (110 participants) assigned to intervention group and 8 schools…

Motivational Pathways to Leisure-Time Physical Activity Participation in Urban Physical Education: A Cluster-Randomized Trial

ERIC Educational Resources Information Center

Yli-Piipari, Sami; Layne, Todd; Hinson, Janet; Irwin, Carol

2018-01-01

Purpose: Grounded in the trans-contextual model of motivation framework, this cluster-randomized trial examined the effectiveness of an autonomy supportive physical education (PE) instruction on student motivation and physical activity (PA). Method: The study comprised six middle schools and 408 students (M[subscript age] = 12.29), with primary…

A Multisite Cluster Randomized Field Trial of Open Court Reading

ERIC Educational Resources Information Center

Borman, Geoffrey D.; Dowling, N. Maritza; Schneck, Carrie

2008-01-01

In this article, the authors report achievement outcomes of a multisite cluster randomized field trial of Open Court Reading 2005 (OCR), a K-6 literacy curriculum published by SRA/McGraw-Hill. The participants are 49 first-grade through fifth-grade classrooms from predominantly minority and poor contexts across the nation. Blocking by grade level…

"A Richness Study of 14 Distant X-Ray Clusters from the 160 Square Degree Survey"

NASA Technical Reports Server (NTRS)

Jones, Christine; West, Donald (Technical Monitor)

2001-01-01

We have measured the surface density of galaxies toward 14 X-ray-selected cluster candidates at redshifts z(sub i) 0.46, and we show that they are associated with rich galaxy concentrations. These clusters, having X-ray luminosities of Lx(0.5-2 keV) approx. (0.5 - 2.6) x 10(exp 44) ergs/ sec are among the most distant and luminous in our 160 deg(exp 2) ROSAT Position Sensitive Proportional Counter cluster survey. We find that the clusters range between Abell richness classes 0 and 2 and have a most probable richness class of 1. We compare the richness distribution of our distant clusters to those for three samples of nearby clusters with similar X-ray luminosities. We find that the nearby and distant samples have similar richness distributions, which shows that clusters have apparently not evolved substantially in richness since redshift z=0.5. There is, however, a marginal tendency for the distant clusters to be slightly poorer than nearby clusters, although deeper multicolor data for a large sample would be required to confirm this trend. We compare the distribution of distant X-ray clusters in the L(sub X)-richness plane to the distribution of optically selected clusters from the Palomar Distant Cluster Survey. The optically selected clusters appear overly rich for their X-ray luminosities, when compared to X-ray-selected clusters. Apparently, X-ray and optical surveys do not necessarily sample identical mass concentrations at large redshifts. This may indicate the existence of a population of optically rich clusters with anomalously low X-ray emission, More likely, however, it reflects the tendency for optical surveys to select unvirialized mass concentrations, as might be expected when peering along large-scale filaments.

VizieR Online Data Catalog: Properties of giant arcs behind CLASH clusters (Xu+, 2016)

NASA Astrophysics Data System (ADS)

Xu, B.; Postman, M.; Meneghetti, M.; Seitz, S.; Zitrin, A.; Merten, J.; Maoz, D.; Frye, B.; Umetsu, K.; Zheng, W.; Bradley, L.; Vega, J.; Koekemoer, A.

2018-01-01

Giant arcs are found in the CLASH images and in simulated images that mimic the CLASH data, using an efficient automated arc-finding algorithm whose selection function has been carefully quantified. CLASH is a 524-orbit multicycle treasury program that targeted 25 massive clusters with 0.18=6.5 in 20 X-ray-selected CLASH clusters. After applying our minimum arc length criterion l>=6", the arc count drops to 81 giant arcs selected from the 20 X-ray-selected CLASH clusters. (2 data files).

Tools for Material Design and Selection

NASA Astrophysics Data System (ADS)

Wehage, Kristopher

The present thesis focuses on applications of numerical methods to create tools for material characterization, design and selection. The tools generated in this work incorporate a variety of programming concepts, from digital image analysis, geometry, optimization, and parallel programming to data-mining, databases and web design. The first portion of the thesis focuses on methods for characterizing clustering in bimodal 5083 Aluminum alloys created by cryomilling and powder metallurgy. The bimodal samples analyzed in the present work contain a mixture of a coarse grain phase, with a grain size on the order of several microns, and an ultra-fine grain phase, with a grain size on the order of 200 nm. The mixing of the two phases is not homogeneous and clustering is observed. To investigate clustering in these bimodal materials, various microstructures were created experimentally by conventional cryomilling, Hot Isostatic Pressing (HIP), Extrusion, Dual-Mode Dynamic Forging (DMDF) and a new 'Gradient' cryomilling process. Two techniques for quantitative clustering analysis are presented, formulated and implemented. The first technique, the Area Disorder function, provides a metric of the quality of coarse grain dispersion in an ultra-fine grain matrix and the second technique, the Two-Point Correlation function, provides a metric of long and short range spatial arrangements of the two phases, as well as an indication of the mean feature size in any direction. The two techniques are implemented on digital images created by Scanning Electron Microscopy (SEM) and Electron Backscatter Detection (EBSD) of the microstructures. To investigate structure--property relationships through modeling and simulation, strategies for generating synthetic microstructures are discussed and a computer program that generates randomized microstructures with desired configurations of clustering described by the Area Disorder Function is formulated and presented. In the computer program, two-dimensional microstructures are generated by Random Sequential Adsorption (RSA) of voxelized ellipses representing the coarse grain phase. A simulated annealing algorithm is used to geometrically optimize the placement of the ellipses in the model to achieve varying user-defined configurations of spatial arrangement of the coarse grains. During the simulated annealing process, the ellipses are allowed to overlap up to a specified threshold, allowing triple junctions to form in the model. Once the simulated annealing process is complete, the remaining space is populated by smaller ellipses representing the ultra-fine grain phase. Uniform random orientations are assigned to the grains. The program generates text files that can be imported in to Crystal Plasticity Finite Element Analysis Software for stress analysis. Finally, numerical methods and programming are applied to current issues in green engineering and hazard assessment. To understand hazards associated with materials and select safer alternatives, engineers and designers need access to up-to-date hazard information. However, hazard information comes from many disparate sources and aggregating, interpreting and taking action on the wealth of data is not trivial. In light of these challenges, a Framework for Automated Hazard Assessment based on the GreenScreen list translator is presented. The framework consists of a computer program that automatically extracts data from the GHS-Japan hazard database, loads the data into a machine-readable JSON format, transforms the JSON document in to a GreenScreen JSON document using the GreenScreen List Translator v1.2 and performs GreenScreen Benchmark scoring on the material. The GreenScreen JSON documents are then uploaded to a document storage system to allow human operators to search for, modify or add additional hazard information via a web interface.

Community involvement in dengue vector control: cluster randomised trial.

PubMed

Vanlerberghe, V; Toledo, M E; Rodríguez, M; Gómez, D; Baly, A; Benítez, J R; Van der Stuyft, P

2010-01-01

To assess the effectiveness of an integrated community based environmental management strategy to control Aedes aegypti, the vector of dengue, compared with a routine strategy. Design Cluster randomised trial. Setting Guantanamo, Cuba. Participants 32 circumscriptions (around 2000 inhabitants each). Interventions The circumscriptions were randomly allocated to control clusters (n=16) comprising routine Aedes control programme (entomological surveillance, source reduction, selective adulticiding, and health education) and to intervention clusters (n=16) comprising the routine Aedes control programme combined with a community based environmental management approach. The primary outcome was levels of Aedes infestation: house index (number of houses positive for at least one container with immature stages of Ae aegypti per 100 inspected houses), Breteau index (number of containers positive for immature stages of Ae aegypti per 100 inspected houses), and the pupae per inhabitant statistic (number of Ae aegypti pupae per inhabitant). All clusters were subjected to the intended intervention; all completed the study protocol up to February 2006 and all were included in the analysis. At baseline the Aedes infestation levels were comparable between intervention and control clusters: house index 0.25% v 0.20%, pupae per inhabitant 0.44 x 10(-3) v 0.29 x 10(-3). At the end of the intervention these indices were significantly lower in the intervention clusters: rate ratio for house indices 0.49 (95% confidence interval 0.27 to 0.88) and rate ratio for pupae per inhabitant 0.27 (0.09 to 0.76). A community based environmental management embedded in a routine control programme was effective at reducing levels of Aedes infestation. Trial Registration Current Controlled Trials ISRCTN88405796.

Community involvement in dengue vector control: cluster randomised trial.

PubMed

Vanlerberghe, V; Toledo, M E; Rodríguez, M; Gomez, D; Baly, A; Benitez, J R; Van der Stuyft, P

2009-06-09

To assess the effectiveness of an integrated community based environmental management strategy to control Aedes aegypti, the vector of dengue, compared with a routine strategy. Cluster randomised trial. Guantanamo, Cuba. 32 circumscriptions (around 2000 inhabitants each). The circumscriptions were randomly allocated to control clusters (n=16) comprising routine Aedes control programme (entomological surveillance, source reduction, selective adulticiding, and health education) and to intervention clusters (n=16) comprising the routine Aedes control programme combined with a community based environmental management approach. The primary outcome was levels of Aedes infestation: house index (number of houses positive for at least one container with immature stages of Ae aegypti per 100 inspected houses), Breteau index (number of containers positive for immature stages of Ae aegypti per 100 inspected houses), and the pupae per inhabitant statistic (number of Ae aegypti pupae per inhabitant). All clusters were subjected to the intended intervention; all completed the study protocol up to February 2006 and all were included in the analysis. At baseline the Aedes infestation levels were comparable between intervention and control clusters: house index 0.25% v 0.20%, pupae per inhabitant 0.44x10(-3) v 0.29x10(-3). At the end of the intervention these indices were significantly lower in the intervention clusters: rate ratio for house indices 0.49 (95% confidence interval 0.27 to 0.88) and rate ratio for pupae per inhabitant 0.27 (0.09 to 0.76). A community based environmental management embedded in a routine control programme was effective at reducing levels of Aedes infestation. Current Controlled Trials ISRCTN88405796.

Refractive error and visual impairment in private school children in Ghana.

PubMed

Kumah, Ben D; Ebri, Anne; Abdul-Kabir, Mohammed; Ahmed, Abdul-Sadik; Koomson, Nana Ya; Aikins, Samual; Aikins, Amos; Amedo, Angela; Lartey, Seth; Naidoo, Kovin

2013-12-01

To assess the prevalence of refractive error and visual impairment in private school children in Ghana. A random selection of geographically defined classes in clusters was used to identify a sample of school children aged 12 to 15 years in the Ashanti Region. Children in 60 clusters were enumerated and examined in classrooms. The examination included visual acuity, retinoscopy, autorefraction under cycloplegia, and examination of anterior segment, media, and fundus. For quality assurance, a random sample of children with reduced and normal vision were selected and re-examined independently. A total of 2454 children attending 53 private schools were enumerated, and of these, 2435 (99.2%) were examined. Prevalence of uncorrected, presenting, and best visual acuity of 20/40 or worse in the better eye was 3.7, 3.5, and 0.4%, respectively. Refractive error was the cause of reduced vision in 71.7% of 152 eyes, amblyopia in 9.9%, retinal disorders in 5.9%, and corneal opacity in 4.6%. Exterior and anterior segment abnormalities occurred in 43 (1.8%) children. Myopia (at least -0.50 D) in one or both eyes was present in 3.2% of children when measured with retinoscopy and in 3.4% measured with autorefraction. Myopia was not significantly associated with gender (P = 0.82). Hyperopia (+2.00 D or more) in at least one eye was present in 0.3% of children with retinoscopy and autorefraction. The prevalence of reduced vision in Ghanaian private school children due to uncorrected refractive error was low. However, the prevalence of amblyopia, retinal disorders, and corneal opacities indicate the need for early interventions.

Method of precisely modifying predetermined surface layers of a workpiece by cluster ion impact therewith

DOEpatents

Friedman, L.; Beuhler, R.J.; Matthew, M.W.; Ledbetter, M.

1984-06-25

A method of precisely modifying a selected area of a workpiece by producing a beam of charged cluster ions that is narrowly mass selected to a predetermined mean size of cluster ions within a range of 25 to 10/sup 6/ atoms per cluster ion, and accelerated in a beam to a critical velocity. The accelerated beam is used to impact a selected area of an outer surface of the workpiece at a preselected rate of impacts of cluster ions/cm/sup 2//sec in order to effect a precise modification in that selected area of the workpiece.

Method of precisely modifying predetermined surface layers of a workpiece by cluster ion impact therewith

DOEpatents

Friedman, Lewis; Buehler, Robert J.; Matthew, Michael W.; Ledbetter, Myron

1985-01-01

A method of precisely modifying a selected area of a workpiece by producing a beam of charged cluster ions that is narrowly mass selected to a predetermined mean size of cluster ions within a range of 25 to 10.sup.6 atoms per cluster ion, and accelerated in a beam to a critical velocity. The accelerated beam is used to impact a selected area of an outer surface of the workpiece at a preselected rate of impacts of cluster ions/cm.sup.2 /sec. in order to effect a precise modification in that selected area of the workpiece.

Effects of multiple spreaders in community networks

NASA Astrophysics Data System (ADS)

Hu, Zhao-Long; Ren, Zhuo-Ming; Yang, Guang-Yong; Liu, Jian-Guo

2014-12-01

Human contact networks exhibit the community structure. Understanding how such community structure affects the epidemic spreading could provide insights for preventing the spreading of epidemics between communities. In this paper, we explore the spreading of multiple spreaders in community networks. A network based on the clustering preferential mechanism is evolved, whose communities are detected by the Girvan-Newman (GN) algorithm. We investigate the spreading effectiveness by selecting the nodes as spreaders in the following ways: nodes with the largest degree in each community (community hubs), the same number of nodes with the largest degree from the global network (global large-degree) and randomly selected one node within each community (community random). The experimental results on the SIR model show that the spreading effectiveness based on the global large-degree and community hubs methods is the same in the early stage of the infection and the method of community random is the worst. However, when the infection rate exceeds the critical value, the global large-degree method embodies the worst spreading effectiveness. Furthermore, the discrepancy of effectiveness for the three methods will decrease as the infection rate increases. Therefore, we should immunize the hubs in each community rather than those hubs in the global network to prevent the outbreak of epidemics.

Identification and analysis of evolutionary selection pressures acting at the molecular level in five forkhead subfamilies.

PubMed

Fetterman, Christina D; Rannala, Bruce; Walter, Michael A

2008-09-24

Members of the forkhead gene family act as transcription regulators in biological processes including development and metabolism. The evolution of forkhead genes has not been widely examined and selection pressures at the molecular level influencing subfamily evolution and differentiation have not been explored. Here, in silico methods were used to examine selection pressures acting on the coding sequence of five multi-species FOX protein subfamily clusters; FoxA, FoxD, FoxI, FoxO and FoxP. Application of site models, which estimate overall selection pressures on individual codons throughout the phylogeny, showed that the amino acid changes observed were either neutral or under negative selection. Branch-site models, which allow estimated selection pressures along specified lineages to vary as compared to the remaining phylogeny, identified positive selection along branches leading to the FoxA3 and Protostomia clades in the FoxA cluster and the branch leading to the FoxO3 clade in the FoxO cluster. Residues that may differentiate paralogs were identified in the FoxA and FoxO clusters and residues that differentiate orthologs were identified in the FoxA cluster. Neutral amino acid changes were identified in the forkhead domain of the FoxA, FoxD and FoxP clusters while positive selection was identified in the forkhead domain of the Protostomia lineage of the FoxA cluster. A series of residues under strong negative selection adjacent to the N- and C-termini of the forkhead domain were identified in all clusters analyzed suggesting a new method for refinement of domain boundaries. Extrapolation of domains among cluster members in conjunction with selection pressure information allowed prediction of residue function in the FoxA, FoxO and FoxP clusters and exclusion of known domain function in residues of the FoxA and FoxI clusters. Consideration of selection pressures observed in conjunction with known functional information allowed prediction of residue function and refinement of domain boundaries. Identification of residues that differentiate orthologs and paralogs provided insight into the development and functional consequences of paralogs and forkhead subfamily composition differences among species. Overall we found that after gene duplication of forkhead family members, rapid differentiation and subsequent fixation of amino acid changes through negative selection has occurred.

Efficient design of cluster randomized trials with treatment-dependent costs and treatment-dependent unknown variances.

PubMed

van Breukelen, Gerard J P; Candel, Math J J M

2018-06-10

Cluster randomized trials evaluate the effect of a treatment on persons nested within clusters, where treatment is randomly assigned to clusters. Current equations for the optimal sample size at the cluster and person level assume that the outcome variances and/or the study costs are known and homogeneous between treatment arms. This paper presents efficient yet robust designs for cluster randomized trials with treatment-dependent costs and treatment-dependent unknown variances, and compares these with 2 practical designs. First, the maximin design (MMD) is derived, which maximizes the minimum efficiency (minimizes the maximum sampling variance) of the treatment effect estimator over a range of treatment-to-control variance ratios. The MMD is then compared with the optimal design for homogeneous variances and costs (balanced design), and with that for homogeneous variances and treatment-dependent costs (cost-considered design). The results show that the balanced design is the MMD if the treatment-to control cost ratio is the same at both design levels (cluster, person) and within the range for the treatment-to-control variance ratio. It still is highly efficient and better than the cost-considered design if the cost ratio is within the range for the squared variance ratio. Outside that range, the cost-considered design is better and highly efficient, but it is not the MMD. An example shows sample size calculation for the MMD, and the computer code (SPSS and R) is provided as supplementary material. The MMD is recommended for trial planning if the study costs are treatment-dependent and homogeneity of variances cannot be assumed. © 2018 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Novel approaches to pin cluster synchronization on complex dynamical networks in Lur'e forms

NASA Astrophysics Data System (ADS)

Tang, Ze; Park, Ju H.; Feng, Jianwen

2018-04-01

This paper investigates the cluster synchronization of complex dynamical networks consisted of identical or nonidentical Lur'e systems. Due to the special topology structure of the complex networks and the existence of stochastic perturbations, a kind of randomly occurring pinning controller is designed which not only synchronizes all Lur'e systems in the same cluster but also decreases the negative influence among different clusters. Firstly, based on an extended integral inequality, the convex combination theorem and S-procedure, the conditions for cluster synchronization of identical Lur'e networks are derived in a convex domain. Secondly, randomly occurring adaptive pinning controllers with two independent Bernoulli stochastic variables are designed and then sufficient conditions are obtained for the cluster synchronization on complex networks consisted of nonidentical Lur'e systems. In addition, suitable control gains for successful cluster synchronization of nonidentical Lur'e networks are acquired by designing some adaptive updating laws. Finally, we present two numerical examples to demonstrate the validity of the control scheme and the theoretical analysis.

Power and money in cluster randomized trials: when is it worth measuring a covariate?

PubMed

Moerbeek, Mirjam

2006-08-15

The power to detect a treatment effect in cluster randomized trials can be increased by increasing the number of clusters. An alternative is to include covariates into the regression model that relates treatment condition to outcome. In this paper, formulae are derived in order to evaluate both strategies on basis of their costs. It is shown that the strategy that uses covariates is more cost-efficient in detecting a treatment effect when the costs to measure these covariates are small and the correlation between the covariates and outcome is sufficiently large. The minimum required correlation depends on the cluster size, and the costs to recruit a cluster and to measure the covariate, relative to the costs to recruit a person. Measuring a covariate that varies at the person level only is recommended when cluster sizes are small and the costs to recruit and measure a cluster are large. Measuring a cluster level covariate is recommended when cluster sizes are large and the costs to recruit and measure a cluster are small. An illustrative example shows the use of the formulae in a practical setting. Copyright 2006 John Wiley & Sons, Ltd.

Evaluation of the procedure 1A component of the 1980 US/Canada wheat and barley exploratory experiment

NASA Technical Reports Server (NTRS)

Chapman, G. M. (Principal Investigator); Carnes, J. G.

1981-01-01

Several techniques which use clusters generated by a new clustering algorithm, CLASSY, are proposed as alternatives to random sampling to obtain greater precision in crop proportion estimation: (1) Proportional Allocation/relative count estimator (PA/RCE) uses proportional allocation of dots to clusters on the basis of cluster size and a relative count cluster level estimate; (2) Proportional Allocation/Bayes Estimator (PA/BE) uses proportional allocation of dots to clusters and a Bayesian cluster-level estimate; and (3) Bayes Sequential Allocation/Bayesian Estimator (BSA/BE) uses sequential allocation of dots to clusters and a Bayesian cluster level estimate. Clustering in an effective method in making proportion estimates. It is estimated that, to obtain the same precision with random sampling as obtained by the proportional sampling of 50 dots with an unbiased estimator, samples of 85 or 166 would need to be taken if dot sets with AI labels (integrated procedure) or ground truth labels, respectively were input. Dot reallocation provides dot sets that are unbiased. It is recommended that these proportion estimation techniques are maintained, particularly the PA/BE because it provides the greatest precision.

Geographic clustering of elderly people with above-norm anthropometric measurements and blood chemistry.

PubMed

Mena, Carlos; Fuentes, Eduardo; Ormazábal, Yony; Palomo, Iván

2017-05-11

The global percentage of people over 60 is strongly increasing and estimated to exceed 20% by 20,150, which means that there will be an increase in many pathological conditions related to aging. Mapping of the location of aging people and identification of their needs can be extremely valuable from a social-economic point of view. Participants in this study were 148 randomly selected adults from Talca City, Chile aged 60-74 at baseline. Geographic information systems (GIS) analyses were performed using ArcGIS software through its module Spatial Autocorrelation. In this study, we demonstrated that elderly people show geographic clustering according to above-norm results of anthropometric measurements and blood chemistry. The spatial identifications found would facilitate exploring the impact of treatment programmes in communities where many aging people live, thereby improving their quality of life as well as reducing overall costs.

Design and simulation study of the immunization Data Quality Audit (DQA).

PubMed

Woodard, Stacy; Archer, Linda; Zell, Elizabeth; Ronveaux, Olivier; Birmingham, Maureen

2007-08-01

The goal of the Data Quality Audit (DQA) is to assess whether the Global Alliance for Vaccines and Immunization-funded countries are adequately reporting the number of diphtheria-tetanus-pertussis immunizations given, on which the "shares" are awarded. Given that this sampling design is a modified two-stage cluster sample (modified because a stratified, rather than a simple, random sample of health facilities is obtained from the selected clusters); the formula for the calculation of the standard error for the estimate is unknown. An approximated standard error has been proposed, and the first goal of this simulation is to assess the accuracy of the standard error. Results from the simulations based on hypothetical populations were found not to be representative of the actual DQAs that were conducted. Additional simulations were then conducted on the actual DQA data to better access the precision of the DQ with both the original and the increased sample sizes.

Clustering and group selection of multiple criteria alternatives with application to space-based networks.

PubMed

Malakooti, Behnam; Yang, Ziyong

2004-02-01

In many real-world problems, the range of consequences of different alternatives are considerably different. In addition, sometimes, selection of a group of alternatives (instead of only one best alternative) is necessary. Traditional decision making approaches treat the set of alternatives with the same method of analysis and selection. In this paper, we propose clustering alternatives into different groups so that different methods of analysis, selection, and implementation for each group can be applied. As an example, consider the selection of a group of functions (or tasks) to be processed by a group of processors. The set of tasks can be grouped according to their similar criteria, and hence, each cluster of tasks to be processed by a processor. The selection of the best alternative for each clustered group can be performed using existing methods; however, the process of selecting groups is different than the process of selecting alternatives within a group. We develop theories and procedures for clustering discrete multiple criteria alternatives. We also demonstrate how the set of alternatives is clustered into mutually exclusive groups based on 1) similar features among alternatives; 2) ideal (or most representative) alternatives given by the decision maker; and 3) other preferential information of the decision maker. The clustering of multiple criteria alternatives also has the following advantages. 1) It decreases the set of alternatives to be considered by the decision maker (for example, different decision makers are assigned to different groups of alternatives). 2) It decreases the number of criteria. 3) It may provide a different approach for analyzing multiple decision makers problems. Each decision maker may cluster alternatives differently, and hence, clustering of alternatives may provide a basis for negotiation. The developed approach is applicable for solving a class of telecommunication networks problems where a set of objects (such as routers, processors, or intelligent autonomous vehicles) are to be clustered into similar groups. Objects are clustered based on several criteria and the decision maker's preferences.

Complex network structure of musical compositions: Algorithmic generation of appealing music

NASA Astrophysics Data System (ADS)

Liu, Xiao Fan; Tse, Chi K.; Small, Michael

2010-01-01

In this paper we construct networks for music and attempt to compose music artificially. Networks are constructed with nodes and edges corresponding to musical notes and their co-occurring connections. We analyze classical music from Bach, Mozart, Chopin, as well as other types of music such as Chinese pop music. We observe remarkably similar properties in all networks constructed from the selected compositions. We conjecture that preserving the universal network properties is a necessary step in artificial composition of music. Power-law exponents of node degree, node strength and/or edge weight distributions, mean degrees, clustering coefficients, mean geodesic distances, etc. are reported. With the network constructed, music can be composed artificially using a controlled random walk algorithm, which begins with a randomly chosen note and selects the subsequent notes according to a simple set of rules that compares the weights of the edges, weights of the nodes, and/or the degrees of nodes. By generating a large number of compositions, we find that this algorithm generates music which has the necessary qualities to be subjectively judged as appealing.

[Plaque segmentation of intracoronary optical coherence tomography images based on K-means and improved random walk algorithm].

PubMed

Wang, Guanglei; Wang, Pengyu; Han, Yechen; Liu, Xiuling; Li, Yan; Lu, Qian

2017-06-01

In recent years, optical coherence tomography (OCT) has developed into a popular coronary imaging technology at home and abroad. The segmentation of plaque regions in coronary OCT images has great significance for vulnerable plaque recognition and research. In this paper, a new algorithm based on K -means clustering and improved random walk is proposed and Semi-automated segmentation of calcified plaque, fibrotic plaque and lipid pool was achieved. And the weight function of random walk is improved. The distance between the edges of pixels in the image and the seed points is added to the definition of the weight function. It increases the weak edge weights and prevent over-segmentation. Based on the above methods, the OCT images of 9 coronary atherosclerotic patients were selected for plaque segmentation. By contrasting the doctor's manual segmentation results with this method, it was proved that this method had good robustness and accuracy. It is hoped that this method can be helpful for the clinical diagnosis of coronary heart disease.

Effect of Deploying Trained Community Based Reproductive Health Nurses (CORN) on Long-Acting Reversible Contraception (LARC) Use in Rural Ethiopia: A Cluster Randomized Community Trial.

PubMed

Zerfu, Taddese Alemu; Ayele, Henok Taddese; Bogale, Tariku Nigatu

2018-06-01

To investigate the effect of innovative means to distribute LARC on contraceptive use, we implemented a three arm, parallel groups, cluster randomized community trial design. The intervention consisted of placing trained community-based reproductive health nurses (CORN) within health centers or health posts. The nurses provided counseling to encourage women to use LARC and distributed all contraceptive methods. A total of 282 villages were randomly selected and assigned to a control arm (n = 94) or 1 of 2 treatment arms (n = 94 each). The treatment groups differed by where the new service providers were deployed, health post or health center. We calculated difference-in-difference (DID) estimates to assess program impacts on LARC use. After nine months of intervention, the use of LARC methods increased significantly by 72.3 percent, while the use of short acting methods declined by 19.6 percent. The proportion of women using LARC methods increased by 45.9 percent and 45.7 percent in the health post and health center based intervention arms, respectively. Compared to the control group, the DID estimates indicate that the use of LARC methods increased by 11.3 and 12.3 percentage points in the health post and health center based intervention arms. Given the low use of LARC methods in similar settings, deployment of contextually trained nurses at the grassroots level could substantially increase utilization of these methods. © 2018 The Population Council, Inc.

Healthy together Victoria and childhood obesity-a methodology for measuring changes in childhood obesity in response to a community-based, whole of system cluster randomized control trial.

PubMed

Strugnell, Claudia; Millar, Lynne; Churchill, Andrew; Jacka, Felice; Bell, Colin; Malakellis, Mary; Swinburn, Boyd; Allender, Steve

2016-01-01

Healthy Together Victoria (HTV) - a complex 'whole of system' intervention, including an embedded cluster randomized control trial, to reduce chronic disease by addressing risk factors (physical inactivity, poor diet quality, smoking and harmful alcohol use) among children and adults in selected communities in Victoria, Australia (Healthy Together Communities). To describe the methodology for: 1) assessing changes in the prevalence of measured childhood obesity and associated risks between primary and secondary school students in HTV communities, compared with comparison communities; and 2) assessing community-level system changes that influence childhood obesity in HTC and comparison communities. Twenty-four geographically bounded areas were randomized to either prevention or comparison (2012). A repeat cross-sectional study utilising opt-out consent will collect objectively measured height, weight, waist and self-reported behavioral data among primary [Grade 4 (aged 9-10y) and Grade 6 (aged 11-12y)] and secondary [Grade 8 (aged 13-14y) and Grade 10 (aged 15-16y)] school students (2014 to 2018). Relationships between measured childhood obesity and system causes, as defined in the Foresight obesity systems map, will be assessed using a range of routine and customised data. This research methodology describes the beginnings of a state-wide childhood obesity monitoring system that can evolve to regularly inform progress on reducing obesity, and situate these changes in the context of broader community-level system change.

Observed intra-cluster correlation coefficients in a cluster survey sample of patient encounters in general practice in Australia

PubMed Central

Knox, Stephanie A; Chondros, Patty

2004-01-01

Background Cluster sample study designs are cost effective, however cluster samples violate the simple random sample assumption of independence of observations. Failure to account for the intra-cluster correlation of observations when sampling through clusters may lead to an under-powered study. Researchers therefore need estimates of intra-cluster correlation for a range of outcomes to calculate sample size. We report intra-cluster correlation coefficients observed within a large-scale cross-sectional study of general practice in Australia, where the general practitioner (GP) was the primary sampling unit and the patient encounter was the unit of inference. Methods Each year the Bettering the Evaluation and Care of Health (BEACH) study recruits a random sample of approximately 1,000 GPs across Australia. Each GP completes details of 100 consecutive patient encounters. Intra-cluster correlation coefficients were estimated for patient demographics, morbidity managed and treatments received. Intra-cluster correlation coefficients were estimated for descriptive outcomes and for associations between outcomes and predictors and were compared across two independent samples of GPs drawn three years apart. Results Between April 1999 and March 2000, a random sample of 1,047 Australian general practitioners recorded details of 104,700 patient encounters. Intra-cluster correlation coefficients for patient demographics ranged from 0.055 for patient sex to 0.451 for language spoken at home. Intra-cluster correlations for morbidity variables ranged from 0.005 for the management of eye problems to 0.059 for management of psychological problems. Intra-cluster correlation for the association between two variables was smaller than the descriptive intra-cluster correlation of each variable. When compared with the April 2002 to March 2003 sample (1,008 GPs) the estimated intra-cluster correlation coefficients were found to be consistent across samples. Conclusions The demonstrated precision and reliability of the estimated intra-cluster correlations indicate that these coefficients will be useful for calculating sample sizes in future general practice surveys that use the GP as the primary sampling unit. PMID:15613248

Controllability of social networks and the strategic use of random information.

PubMed

Cremonini, Marco; Casamassima, Francesca

2017-01-01

This work is aimed at studying realistic social control strategies for social networks based on the introduction of random information into the state of selected driver agents. Deliberately exposing selected agents to random information is a technique already experimented in recommender systems or search engines, and represents one of the few options for influencing the behavior of a social context that could be accepted as ethical, could be fully disclosed to members, and does not involve the use of force or of deception. Our research is based on a model of knowledge diffusion applied to a time-varying adaptive network and considers two well-known strategies for influencing social contexts: One is the selection of few influencers for manipulating their actions in order to drive the whole network to a certain behavior; the other, instead, drives the network behavior acting on the state of a large subset of ordinary, scarcely influencing users. The two approaches have been studied in terms of network and diffusion effects. The network effect is analyzed through the changes induced on network average degree and clustering coefficient, while the diffusion effect is based on two ad hoc metrics which are defined to measure the degree of knowledge diffusion and skill level, as well as the polarization of agent interests. The results, obtained through simulations on synthetic networks, show a rich dynamics and strong effects on the communication structure and on the distribution of knowledge and skills. These findings support our hypothesis that the strategic use of random information could represent a realistic approach to social network controllability, and that with both strategies, in principle, the control effect could be remarkable.

Temporal clustering of tropical cyclones and its ecosystem impacts

PubMed Central

Mumby, Peter J.; Vitolo, Renato; Stephenson, David B.

2011-01-01

Tropical cyclones have massive economic, social, and ecological impacts, and models of their occurrence influence many planning activities from setting insurance premiums to conservation planning. Most impact models allow for geographically varying cyclone rates but assume that individual storm events occur randomly with constant rate in time. This study analyzes the statistical properties of Atlantic tropical cyclones and shows that local cyclone counts vary in time, with periods of elevated activity followed by relative quiescence. Such temporal clustering is particularly strong in the Caribbean Sea, along the coasts of Belize, Honduras, Costa Rica, Jamaica, the southwest of Haiti, and in the main hurricane development region in the North Atlantic between Africa and the Caribbean. Failing to recognize this natural nonstationarity in cyclone rates can give inaccurate impact predictions. We demonstrate this by exploring cyclone impacts on coral reefs. For a given cyclone rate, we find that clustered events have a less detrimental impact than independent random events. Predictions using a standard random hurricane model were overly pessimistic, predicting reef degradation more than a decade earlier than that expected under clustered disturbance. The presence of clustering allows coral reefs more time to recover to healthier states, but the impacts of clustering will vary from one ecosystem to another. PMID:22006300

Alcohol-Specific Parenting within a Cluster-Randomized Effectiveness Trial of a Swedish Primary Prevention Program

ERIC Educational Resources Information Center

Strandberg, Anna K.; Bodin, Maria C.

2011-01-01

Purpose: Within the framework of an ongoing cluster-randomized effectiveness trial of a parental prevention program, the aim of the present study is to investigate attitudes towards under-age drinking and use of program components, i.e. alcohol-specific parenting behaviors, in parents who did and did not take part in the programme.…

The YouthMood Project: A Cluster Randomized Controlled Trial of an Online Cognitive Behavioral Program with Adolescents

ERIC Educational Resources Information Center

Calear, Alison L.; Christensen, Helen; Mackinnon, Andrew; Griffiths, Kathleen M.; O'Kearney, Richard

2009-01-01

The aim in the current study was to investigate the effectiveness of an online, self-directed cognitive-behavioral therapy program (MoodGYM) in preventing and reducing the symptoms of anxiety and depression in an adolescent school-based population. A cluster randomized controlled trial was conducted with 30 schools (N = 1,477) from across…

Assessment Data-Informed Guidance to Individualize Kindergarten Reading Instruction: Findings from a Cluster-Randomized Control Field Trial

ERIC Educational Resources Information Center

Al Otaiba, Stephanie; Connor, Carol M.; Folsom, Jessica S.; Greulich, Luana; Meadows, Jane; Li, Zhi

2011-01-01

The purpose of this cluster-randomized control field trial was to examine whether kindergarten teachers could learn to differentiate classroom reading instruction using Individualized Student Instruction for Kindergarten (ISI-K) and to test the efficacy of differentiation on reading outcomes. The study involved 14 schools, 23 ISI-K (n = 305…

The Effects of Therapist Competence in Assigning Homework in Cognitive Therapy with Cluster C Personality Disorders: Results from a Randomized Controlled Trial

ERIC Educational Resources Information Center

Ryum, Truls; Stiles, Tore C.; Svartberg, Martin; McCullough, Leigh

2010-01-01

Therapist competence in assigning homework was used to predict mid- and posttreatment outcome for patients with Cluster C personality disorders in cognitive therapy (CT). Twenty-five patients that underwent 40 sessions of CT were taken from a randomized controlled trial (Svartberg, Stiles, & Seltzer, 2004). Therapist competence in assigning…

A General Framework for Power Analysis to Detect the Moderator Effects in Two- and Three-Level Cluster Randomized Trials

ERIC Educational Resources Information Center

Dong, Nianbo; Spybrook, Jessaca; Kelcey, Ben

2016-01-01

The purpose of this study is to propose a general framework for power analyses to detect the moderator effects in two- and three-level cluster randomized trials (CRTs). The study specifically aims to: (1) develop the statistical formulations for calculating statistical power, minimum detectable effect size (MDES) and its confidence interval to…

The Impact of Curriculum-Based Professional Development on Science Instruction: Results from a Cluster-Randomized Trial

ERIC Educational Resources Information Center

Taylor, Joseph; Kowalski, Susan; Getty, Stephen; Wilson, Christopher; Carlson, Janet

2011-01-01

This research is part of a larger, IES-funded study titled: "Measuring the Efficacy and Student Achievement of Research-based Instructional Materials in High School Multidisciplinary Science" (Award # R305K060142). The larger study seeks to use a cluster-randomized trial design, with schools as the unit of assignment, to make causal…

A Clustered Randomized Controlled Trial to Determine Impacts of the Harvest of the Month Program

ERIC Educational Resources Information Center

LaChausse, Robert G.

2017-01-01

The study purpose was to examine the impact of the Harvest of the Month (HOTM) program on fruit and vegetable (FV) consumption, FV preferences, other eating behaviors, physical activity and other variables related to healthy eating. A clustered randomized controlled trial was employed in 28 elementary schools. After parental consent was obtained,…

Impact of a Social-Emotional and Character Development Program on School-Level Indicators of Academic Achievement, Absenteeism, and Disciplinary Outcomes: A Matched-Pair, Cluster-Randomized, Controlled Trial

ERIC Educational Resources Information Center

Snyder, Frank; Flay, Brian; Vuchinich, Samuel; Acock, Alan; Washburn, Isaac; Beets, Michael; Li, Kin-Kit

2010-01-01

This article reports the effects of a comprehensive elementary school-based social-emotional and character education program on school-level achievement, absenteeism, and disciplinary outcomes utilizing a matched-pair, cluster-randomized, controlled design. The "Positive Action" Hawai'i trial included 20 racially/ethnically diverse…

Cluster Randomized-Controlled Trial of Interventions to Improve Health for Adults with Intellectual Disability Who Live in Private Dwellings

ERIC Educational Resources Information Center

Lennox, Nicholas; Bain, Chris; Rey-Conde, Therese; Taylor, Miriam; Boyle, Frances M.; Purdie, David M.; Ware, Robert S.

2010-01-01

Background: People with intellectual disability who live in the community often have poor health and healthcare, partly as a consequence of poor communication, recall difficulties and incomplete patient health information. Materials and Methods: A cluster randomized-controlled trial with 2 x 2 factorial design was conducted with adults with…

Pathways to Health: A Cluster Randomized Trial of Nicotine Gum and Motivational Interviewing for Smoking Cessation in Low-Income Housing

ERIC Educational Resources Information Center

Okuyemi, Kolawole S.; James, Aimee S.; Mayo, Matthew S.; Nollen, Nicole; Catley, Delwyn; Choi, Won S.; Ahluwalia, Jasjit S.

2007-01-01

Despite high smoking rates among those living in poverty, few cessation studies are conducted in these populations. This cluster-randomized trial tested nicotine gum plus motivational interviewing (MI) for smoking cessation in 20 low-income housing developments (HDs). Intervention participants (10 HDs, n = 66) received educational materials, 8…

Academic Clustering and Major Selection of Intercollegiate Student-Athletes

ERIC Educational Resources Information Center

Schneider, Ray G.; Ross, Sally R.; Fisher, Morgan

2010-01-01

Although journalists and reporters have written about academic clustering among college student-athletes, there has been a dearth of scholarly analysis devoted to the subject. This study explored football players' academic major selections to determine if academic clustering actually existed. The seasons 1996, 2001, and 2006 were selected for…

UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring

PubMed Central

Ammad Uddin, Mohammad; Mansour, Ali; Le Jeune, Denis; Ayaz, Mohammad; Aggoune, el-Hadi M.

2018-01-01

In this study, a crop health monitoring system is developed by using state of the art technologies including wireless sensors and Unmanned Aerial Vehicles (UAVs). Conventionally data is collected from sensor nodes either by fixed base stations or mobile sinks. Mobile sinks are considered a better choice nowadays due to their improved network coverage and energy utilization. Usually, the mobile sink is used in two ways: either it goes for random walk to find the scattered nodes and collect data, or follows a pre-defined path established by the ground network/clusters. Neither of these options is suitable in our scenario due to the factors like dynamic data collection, the strict targeted area required to be scanned, unavailability of a large number of nodes, dynamic path of the UAV, and most importantly, none of these are known in advance. The contribution of this paper is the formation of dynamic runtime clusters of field sensors by considering the above mentioned factors. Furthermore a mechanism (Bayesian classifier) is defined to select best node as cluster head. The proposed system is validated through simulation results, lab and infield experiments using concept devices. The obtained results are encouraging, especially in terms of deployment time, energy, efficiency, throughput and ease of use. PMID:29439496

UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring.

PubMed

Uddin, Mohammad Ammad; Mansour, Ali; Jeune, Denis Le; Ayaz, Mohammad; Aggoune, El-Hadi M

2018-02-11

In this study, a crop health monitoring system is developed by using state of the art technologies including wireless sensors and Unmanned Aerial Vehicles (UAVs). Conventionally data is collected from sensor nodes either by fixed base stations or mobile sinks. Mobile sinks are considered a better choice nowadays due to their improved network coverage and energy utilization. Usually, the mobile sink is used in two ways: either it goes for random walk to find the scattered nodes and collect data, or follows a pre-defined path established by the ground network/clusters. Neither of these options is suitable in our scenario due to the factors like dynamic data collection, the strict targeted area required to be scanned, unavailability of a large number of nodes, dynamic path of the UAV, and most importantly, none of these are known in advance. The contribution of this paper is the formation of dynamic runtime clusters of field sensors by considering the above mentioned factors. Furthermore a mechanism (Bayesian classifier) is defined to select best node as cluster head. The proposed system is validated through simulation results, lab and infield experiments using concept devices. The obtained results are encouraging, especially in terms of deployment time, energy, efficiency, throughput and ease of use.

Testing for X-Ray–SZ Differences and Redshift Evolution in the X-Ray Morphology of Galaxy Clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nurgaliev, D.; McDonald, M.; Benson, B. A.

We present a quantitative study of the X-ray morphology of galaxy clusters, as a function of their detection method and redshift. We analyze two separate samples of galaxy clusters: a sample of 36 clusters atmore » $$0.35\\lt z\\lt 0.9$$ selected in the X-ray with the ROSAT PSPC 400 deg(2) survey, and a sample of 90 clusters at $$0.25\\lt z\\lt 1.2$$ selected via the Sunyaev–Zel’dovich (SZ) effect with the South Pole Telescope. Clusters from both samples have similar-quality Chandra observations, which allow us to quantify their X-ray morphologies via two distinct methods: centroid shifts (w) and photon asymmetry ($${A}_{\\mathrm{phot}}$$). The latter technique provides nearly unbiased morphology estimates for clusters spanning a broad range of redshift and data quality. We further compare the X-ray morphologies of X-ray- and SZ-selected clusters with those of simulated clusters. We do not find a statistically significant difference in the measured X-ray morphology of X-ray and SZ-selected clusters over the redshift range probed by these samples, suggesting that the two are probing similar populations of clusters. We find that the X-ray morphologies of simulated clusters are statistically indistinguishable from those of X-ray- or SZ-selected clusters, implying that the most important physics for dictating the large-scale gas morphology (outside of the core) is well-approximated in these simulations. Finally, we find no statistically significant redshift evolution in the X-ray morphology (both for observed and simulated clusters), over the range of $$z\\sim 0.3$$ to $$z\\sim 1$$, seemingly in contradiction with the redshift-dependent halo merger rate predicted by simulations.« less

Testing for X-Ray–SZ Differences and Redshift Evolution in the X-Ray Morphology of Galaxy Clusters

DOE PAGES

Nurgaliev, D.; McDonald, M.; Benson, B. A.; ...

2017-05-16

We present a quantitative study of the X-ray morphology of galaxy clusters, as a function of their detection method and redshift. We analyze two separate samples of galaxy clusters: a sample of 36 clusters atmore » $$0.35\\lt z\\lt 0.9$$ selected in the X-ray with the ROSAT PSPC 400 deg(2) survey, and a sample of 90 clusters at $$0.25\\lt z\\lt 1.2$$ selected via the Sunyaev–Zel’dovich (SZ) effect with the South Pole Telescope. Clusters from both samples have similar-quality Chandra observations, which allow us to quantify their X-ray morphologies via two distinct methods: centroid shifts (w) and photon asymmetry ($${A}_{\\mathrm{phot}}$$). The latter technique provides nearly unbiased morphology estimates for clusters spanning a broad range of redshift and data quality. We further compare the X-ray morphologies of X-ray- and SZ-selected clusters with those of simulated clusters. We do not find a statistically significant difference in the measured X-ray morphology of X-ray and SZ-selected clusters over the redshift range probed by these samples, suggesting that the two are probing similar populations of clusters. We find that the X-ray morphologies of simulated clusters are statistically indistinguishable from those of X-ray- or SZ-selected clusters, implying that the most important physics for dictating the large-scale gas morphology (outside of the core) is well-approximated in these simulations. Finally, we find no statistically significant redshift evolution in the X-ray morphology (both for observed and simulated clusters), over the range of $$z\\sim 0.3$$ to $$z\\sim 1$$, seemingly in contradiction with the redshift-dependent halo merger rate predicted by simulations.« less

Effect of Reassuring Information About Musculoskeletal and Mental Health Complaints at the Workplace: A Cluster Randomized Trial of the atWork Intervention.

PubMed

Johnsen, Tone Langjordet; Eriksen, Hege Randi; Baste, Valborg; Indahl, Aage; Odeen, Magnus; Tveito, Torill Helene

2018-05-21

Purpose The purpose of this study was to investigate the possible difference between the Modified atWork intervention (MAW) and the Original atWork intervention (OAW) on sick leave and other health related outcomes. atWork is a group intervention using the workplace as an arena for distribution of evidence-based knowledge about musculoskeletal and mental health complaints. Methods A cluster randomized controlled trial with 93 kindergartens, comprising a total of 1011 employees, was conducted. Kindergartens were stratified by county and size and randomly allocated to MAW (45 clusters, 324 respondents) or OAW (48 clusters, 313 respondents). The randomization and intervention allocation processes were concealed. There was no blinding to group allocation. Primary outcome was register data on sick leave at cluster level. Secondary outcomes were health complaints, job satisfaction, social support, coping, and beliefs about musculoskeletal and mental health complaints, measured at the individual level. Results The MAW group reduced sick leave by 5.7% during the intervention year, while the OAW group had a 7.5% increase. Overall, the changes were not statistically significant, and no difference was detected between groups, based on 45 and 47 kindergartens. Compared to the OAW group, the MAW group had a smaller reduction for two of the statements concerning faulty beliefs about back pain, but believed less in the hereditary nature of depression. Conclusions The MAW did not have a different effect on sick leave at cluster level compared to the OAW. Trial registration https://Clinicaltrials.gov/ : NCT02396797. Registered March 23th, 2015.

Feasibility and effectiveness of an evidence-based asthma service in Australian community pharmacies: a pragmatic cluster randomized trial.

PubMed

Armour, Carol L; Reddel, Helen K; LeMay, Kate S; Saini, Bandana; Smith, Lorraine D; Bosnic-Anticevich, Sinthia Z; Song, Yun Ju Christine; Alles, M Chehani; Burton, Deborah L; Emmerton, Lynne; Stewart, Kay; Krass, Ines

2013-04-01

To test the feasibility, effectiveness, and sustainability of a pharmacy asthma service in primary care. A pragmatic cluster randomized trial in community pharmacies in four Australian states/territories in 2009. Specially trained pharmacists were randomized to deliver an asthma service in two groups, providing three versus four consultations over 6 months. People with poorly controlled asthma or no recent asthma review were included. Follow-up for 12 months after service completion occurred in 30% of randomly selected completing patients. Outcomes included change in asthma control (poor and fair/good) and Asthma Control Questionnaire (ACQ) score, inhaler technique, quality of life, perceived control, adherence, asthma knowledge, and asthma action plan ownership. Ninety-six pharmacists enrolled 570 patients, with 398 (70%) completing. Asthma control significantly improved with both the three- and four-visit service, with no significant difference between groups (good/fair control 29% and 21% at baseline, 61% and 59% at end, p = .791). Significant improvements were also evident in the ACQ (mean change 0.56), inhaler technique (17-33% correct baseline, 57-72% end), asthma action plan ownership (19% baseline, 56% end), quality of life, adherence, perceived control, and asthma knowledge, with no significant difference between groups for any variable. Outcomes were sustained at 12 months post-service. The pharmacy asthma service delivered clinically important improvements in both a three-visit and four-visit service. Pharmacists were able to recruit and deliver the service with minimal intervention, suggesting it is practical to implement in practice. The three-visit service would be feasible and effective to implement, with a review at 12 months.

A computational proposal for designing structured RNA pools for in vitro selection of RNAs.

PubMed

Kim, Namhee; Gan, Hin Hark; Schlick, Tamar

2007-04-01

Although in vitro selection technology is a versatile experimental tool for discovering novel synthetic RNA molecules, finding complex RNA molecules is difficult because most RNAs identified from random sequence pools are simple motifs, consistent with recent computational analysis of such sequence pools. Thus, enriching in vitro selection pools with complex structures could increase the probability of discovering novel RNAs. Here we develop an approach for engineering sequence pools that links RNA sequence space regions with corresponding structural distributions via a "mixing matrix" approach combined with a graph theory analysis. We define five classes of mixing matrices motivated by covariance mutations in RNA; these constructs define nucleotide transition rates and are applied to chosen starting sequences to yield specific nonrandom pools. We examine the coverage of sequence space as a function of the mixing matrix and starting sequence via clustering analysis. We show that, in contrast to random sequences, which are associated only with a local region of sequence space, our designed pools, including a structured pool for GTP aptamers, can target specific motifs. It follows that experimental synthesis of designed pools can benefit from using optimized starting sequences, mixing matrices, and pool fractions associated with each of our constructed pools as a guide. Automation of our approach could provide practical tools for pool design applications for in vitro selection of RNAs and related problems.

Cluster ensemble based on Random Forests for genetic data.

PubMed

Alhusain, Luluah; Hafez, Alaaeldin M

2017-01-01

Clustering plays a crucial role in several application domains, such as bioinformatics. In bioinformatics, clustering has been extensively used as an approach for detecting interesting patterns in genetic data. One application is population structure analysis, which aims to group individuals into subpopulations based on shared genetic variations, such as single nucleotide polymorphisms. Advances in DNA sequencing technology have facilitated the obtainment of genetic datasets with exceptional sizes. Genetic data usually contain hundreds of thousands of genetic markers genotyped for thousands of individuals, making an efficient means for handling such data desirable. Random Forests (RFs) has emerged as an efficient algorithm capable of handling high-dimensional data. RFs provides a proximity measure that can capture different levels of co-occurring relationships between variables. RFs has been widely considered a supervised learning method, although it can be converted into an unsupervised learning method. Therefore, RF-derived proximity measure combined with a clustering technique may be well suited for determining the underlying structure of unlabeled data. This paper proposes, RFcluE, a cluster ensemble approach for determining the underlying structure of genetic data based on RFs. The approach comprises a cluster ensemble framework to combine multiple runs of RF clustering. Experiments were conducted on high-dimensional, real genetic dataset to evaluate the proposed approach. The experiments included an examination of the impact of parameter changes, comparing RFcluE performance against other clustering methods, and an assessment of the relationship between the diversity and quality of the ensemble and its effect on RFcluE performance. This paper proposes, RFcluE, a cluster ensemble approach based on RF clustering to address the problem of population structure analysis and demonstrate the effectiveness of the approach. The paper also illustrates that applying a cluster ensemble approach, combining multiple RF clusterings, produces more robust and higher-quality results as a consequence of feeding the ensemble with diverse views of high-dimensional genetic data obtained through bagging and random subspace, the two key features of the RF algorithm.

Significant locations in auxiliary data as seeds for typical use cases of point clustering

NASA Astrophysics Data System (ADS)

Kröger, Johannes

2018-05-01

Random greedy clustering and grid-based clustering are highly susceptible by their initial parameters. When used for point data clustering in maps they often change the apparent distribution of the underlying data. We propose a process that uses precomputed weighted seed points for the initialization of clusters, for example from local maxima in population density data. Exemplary results from the clustering of a dataset of petrol stations are presented.

Composition, morphology, and growth of clusters in a gas of particles with random interactions

NASA Astrophysics Data System (ADS)

Azizi, Itay; Rabin, Yitzhak

2018-03-01

We use Langevin dynamics simulations to study the growth kinetics and the steady-state properties of condensed clusters in a dilute two-dimensional system of particles that are all different (APD) in the sense that each particle is characterized by a randomly chosen interaction parameter. The growth exponents, the transition temperatures, and the steady-state properties of the clusters and of the surrounding gas phase are obtained and compared with those of one-component systems. We investigate the fractionation phenomenon, i.e., how particles of different identities are distributed between the coexisting mother (gas) and daughter (clusters) phases. We study the local organization of particles inside clusters, according to their identity—neighbourhood identity ordering (NIO)—and compare the results with those of previous studies of NIO in dense APD systems.

Perspective: Size selected clusters for catalysis and electrochemistry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Halder, Avik; Curtiss, Larry A.; Fortunelli, Alessandro

We report that size-selected clusters containing a handful of atoms may possess noble catalytic properties different from nano-sized or bulk catalysts. Size- and composition-selected clusters can also serve as models of the catalytic active site, where an addition or removal of a single atom can have a dramatic effect on their activity and selectivity. In this Perspective, we provide an overview of studies performed under both ultra-high vacuum and realistic reaction conditions aimed at the interrogation, characterization and understanding of the performance of supported size-selected clusters in heterogeneous and electrochemical reactions, which address the effects of cluster size, cluster composition,more » cluster-support interactions and reaction conditions, the key parameters for the understanding and control of catalyst functionality. Computational modelling based on density functional theory sampling of local minima and energy barriers or ab initio Molecular Dynamics simulations is an integral part of this research by providing fundamental understanding of the catalytic processes at the atomic level, as well as by predicting new materials compositions which can be validated in experiments. Lastly, we discuss approaches which aim at the scale up of the production of well-defined clusters for use in real world applications.« less

Perspective: Size selected clusters for catalysis and electrochemistry

DOE PAGES

Halder, Avik; Curtiss, Larry A.; Fortunelli, Alessandro; ...

2018-03-15

We report that size-selected clusters containing a handful of atoms may possess noble catalytic properties different from nano-sized or bulk catalysts. Size- and composition-selected clusters can also serve as models of the catalytic active site, where an addition or removal of a single atom can have a dramatic effect on their activity and selectivity. In this Perspective, we provide an overview of studies performed under both ultra-high vacuum and realistic reaction conditions aimed at the interrogation, characterization and understanding of the performance of supported size-selected clusters in heterogeneous and electrochemical reactions, which address the effects of cluster size, cluster composition,more » cluster-support interactions and reaction conditions, the key parameters for the understanding and control of catalyst functionality. Computational modelling based on density functional theory sampling of local minima and energy barriers or ab initio Molecular Dynamics simulations is an integral part of this research by providing fundamental understanding of the catalytic processes at the atomic level, as well as by predicting new materials compositions which can be validated in experiments. Lastly, we discuss approaches which aim at the scale up of the production of well-defined clusters for use in real world applications.« less

Perspective: Size selected clusters for catalysis and electrochemistry

NASA Astrophysics Data System (ADS)

Halder, Avik; Curtiss, Larry A.; Fortunelli, Alessandro; Vajda, Stefan

2018-03-01

Size-selected clusters containing a handful of atoms may possess noble catalytic properties different from nano-sized or bulk catalysts. Size- and composition-selected clusters can also serve as models of the catalytic active site, where an addition or removal of a single atom can have a dramatic effect on their activity and selectivity. In this perspective, we provide an overview of studies performed under both ultra-high vacuum and realistic reaction conditions aimed at the interrogation, characterization, and understanding of the performance of supported size-selected clusters in heterogeneous and electrochemical reactions, which address the effects of cluster size, cluster composition, cluster-support interactions, and reaction conditions, the key parameters for the understanding and control of catalyst functionality. Computational modeling based on density functional theory sampling of local minima and energy barriers or ab initio molecular dynamics simulations is an integral part of this research by providing fundamental understanding of the catalytic processes at the atomic level, as well as by predicting new materials compositions which can be validated in experiments. Finally, we discuss approaches which aim at the scale up of the production of well-defined clusters for use in real world applications.

RRW: repeated random walks on genome-scale protein networks for local cluster discovery

PubMed Central

Macropol, Kathy; Can, Tolga; Singh, Ambuj K

2009-01-01

Background We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW) for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. Results We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL), and find a significant improvement in the RRW clusters' precision and accuracy values. Conclusion RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters. PMID:19740439

wayGoo recommender system: personalized recommendations for events scheduling, based on static and real-time information

NASA Astrophysics Data System (ADS)

Thanos, Konstantinos-Georgios; Thomopoulos, Stelios C. A.

2016-05-01

wayGoo is a fully functional application whose main functionalities include content geolocation, event scheduling, and indoor navigation. However, significant information about events do not reach users' attention, either because of the size of this information or because some information comes from real - time data sources. The purpose of this work is to facilitate event management operations by prioritizing the presented events, based on users' interests using both, static and real - time data. Through the wayGoo interface, users select conceptual topics that are interesting for them. These topics constitute a browsing behavior vector which is used for learning users' interests implicitly, without being intrusive. Then, the system estimates user preferences and return an events list sorted from the most preferred one to the least. User preferences are modeled via a Naïve Bayesian Network which consists of: a) the `decision' random variable corresponding to users' decision on attending an event, b) the `distance' random variable, modeled by a linear regression that estimates the probability that the distance between a user and each event destination is not discouraging, ` the seat availability' random variable, modeled by a linear regression, which estimates the probability that the seat availability is encouraging d) and the `relevance' random variable, modeled by a clustering - based collaborative filtering, which determines the relevance of each event users' interests. Finally, experimental results show that the proposed system contribute essentially to assisting users in browsing and selecting events to attend.

Poverty and blindness in Pakistan: results from the Pakistan national blindness and visual impairment survey.

PubMed

Gilbert, Clare E; Shah, S P; Jadoon, M Z; Bourne, R; Dineen, B; Khan, M A; Johnson, G J; Khan, M D

2008-01-05

To explore the association between blindness and deprivation in a nationally representative sample of adults in Pakistan. Cross sectional population based survey. 221 rural and urban clusters selected randomly throughout Pakistan. Nationally representative sample of 16 507 adults aged 30 or above (95.3% response rate). Associations between visual impairment and poverty assessed by a cluster level deprivation index and a household level poverty indicator; prevalence and causes of blindness; measures of the rate of uptake and quality of eye care services. 561 blind participants (<3/60 in the better eye) were identified during the survey. Clusters in urban Sindh province were the most affluent, whereas rural areas in Balochistan were the poorest. The prevalence of blindness in adults living in affluent clusters was 2.2%, compared with 3.7% in medium clusters and 3.9% in poor clusters (P<0.001 for affluent v poor). The highest prevalence of blindness was found in rural Balochistan (5.2%). The prevalence of total blindness (bilateral no light perception) was more than three times higher in poor clusters than in affluent clusters (0.24% v 0.07%, P<0.001). The prevalences of blindness caused by cataract, glaucoma, and corneal opacity were lower in affluent clusters and households. Reflecting access to eye care services, cataract surgical coverage was higher in affluent clusters (80.6%) than in medium (76.8%) and poor areas (75.1%). Intraocular lens implantation rates were significantly lower in participants from poorer households. 10.2% of adults living in affluent clusters presented to the examination station wearing spectacles, compared with 6.7% in medium clusters and 4.4% in poor cluster areas. Spectacle coverage in affluent areas was more than double that in poor clusters (23.5% v 11.1%, P<0.001). Blindness is associated with poverty in Pakistan; lower access to eye care services was one contributory factor. To reduce blindness, strategies targeting poor people will be needed. These interventions may have an impact on deprivation in Pakistan.

Spatial ecology of refuge selection by an herbivore under risk of predation

USGS Publications Warehouse

Wilson, Tammy L.; Rayburn, Andrew P.; Edwards, Thomas C.

2012-01-01

Prey species use structures such as burrows to minimize predation risk. The spatial arrangement of these resources can have important implications for individual and population fitness. For example, there is evidence that clustered resources can benefit individuals by reducing predation risk and increasing foraging opportunity concurrently, which leads to higher population density. However, the scale of clustering that is important in these processes has been ignored during theoretical and empirical development of resource models. Ecological understanding of refuge exploitation by prey can be improved by spatial analysis of refuge use and availability that incorporates the effect of scale. We measured the spatial distribution of pygmy rabbit (Brachylagus idahoensis) refugia (burrows) through censuses in four 6-ha sites. Point pattern analyses were used to evaluate burrow selection by comparing the spatial distribution of used and available burrows. The presence of food resources and additional overstory cover resources was further examined using logistic regression. Burrows were spatially clustered at scales up to approximately 25 m, and then regularly spaced at distances beyond ~40 m. Pygmy rabbit exploitation of burrows did not match availability. Burrows used by pygmy rabbits were likely to be located in areas with high overall burrow density (resource clusters) and high overstory cover, which together minimized predation risk. However, in some cases we observed an interaction between either overstory cover (safety) or understory cover (forage) and burrow density. The interactions show that pygmy rabbits will use burrows in areas with low relative burrow density (high relative predation risk) if understory food resources are high. This points to a potential trade-off whereby rabbits must sacrifice some safety afforded by additional nearby burrows to obtain ample forage resources. Observed patterns of clustered burrows and non-random burrow use improve understanding of the importance of spatial distribution of refugia for burrowing herbivores. The analyses used allowed for the estimation of the spatial scale where subtle trade-offs between predation avoidance and foraging opportunity are likely to occur in a natural system.

Cluster headache and the hypocretin receptor 2 reconsidered: a genetic association study and meta-analysis.

PubMed

Weller, Claudia M; Wilbrink, Leopoldine A; Houwing-Duistermaat, Jeanine J; Koelewijn, Stephany C; Vijfhuizen, Lisanne S; Haan, Joost; Ferrari, Michel D; Terwindt, Gisela M; van den Maagdenberg, Arn M J M; de Vries, Boukje

2015-08-01

Cluster headache is a severe neurological disorder with a complex genetic background. A missense single nucleotide polymorphism (rs2653349; p.Ile308Val) in the HCRTR2 gene that encodes the hypocretin receptor 2 is the only genetic factor that is reported to be associated with cluster headache in different studies. However, as there are conflicting results between studies, we re-evaluated its role in cluster headache. We performed a genetic association analysis for rs2653349 in our large Leiden University Cluster headache Analysis (LUCA) program study population. Systematic selection of the literature yielded three additional studies comprising five study populations, which were included in our meta-analysis. Data were extracted according to predefined criteria. A total of 575 cluster headache patients from our LUCA study and 874 controls were genotyped for HCRTR2 SNP rs2653349 but no significant association with cluster headache was found (odds ratio 0.91 (95% confidence intervals 0.75-1.10), p = 0.319). In contrast, the meta-analysis that included in total 1167 cluster headache cases and 1618 controls from the six study populations, which were part of four different studies, showed association of the single nucleotide polymorphism with cluster headache (random effect odds ratio 0.69 (95% confidence intervals 0.53-0.90), p = 0.006). The association became weaker, as the odds ratio increased to 0.80, when the meta-analysis was repeated without the initial single South European study with the largest effect size. Although we did not find evidence for association of rs2653349 in our LUCA study, which is the largest investigated study population thus far, our meta-analysis provides genetic evidence for a role of HCRTR2 in cluster headache. Regardless, we feel that the association should be interpreted with caution as meta-analyses with individual populations that have limited power have diminished validity. © International Headache Society 2014.

Case-control geographic clustering for residential histories accounting for risk factors and covariates

PubMed Central

2006-01-01

Background Methods for analyzing space-time variation in risk in case-control studies typically ignore residential mobility. We develop an approach for analyzing case-control data for mobile individuals and apply it to study bladder cancer in 11 counties in southeastern Michigan. At this time data collection is incomplete and no inferences should be drawn – we analyze these data to demonstrate the novel methods. Global, local and focused clustering of residential histories for 219 cases and 437 controls is quantified using time-dependent nearest neighbor relationships. Business address histories for 268 industries that release known or suspected bladder cancer carcinogens are analyzed. A logistic model accounting for smoking, gender, age, race and education specifies the probability of being a case, and is incorporated into the cluster randomization procedures. Sensitivity of clustering to definition of the proximity metric is assessed for 1 to 75 k nearest neighbors. Results Global clustering is partly explained by the covariates but remains statistically significant at 12 of the 14 levels of k considered. After accounting for the covariates 26 Local clusters are found in Lapeer, Ingham, Oakland and Jackson counties, with the clusters in Ingham and Oakland counties appearing in 1950 and persisting to the present. Statistically significant focused clusters are found about the business address histories of 22 industries located in Oakland (19 clusters), Ingham (2) and Jackson (1) counties. Clusters in central and southeastern Oakland County appear in the 1930's and persist to the present day. Conclusion These methods provide a systematic approach for evaluating a series of increasingly realistic alternative hypotheses regarding the sources of excess risk. So long as selection of cases and controls is population-based and not geographically biased, these tools can provide insights into geographic risk factors that were not specifically assessed in the case-control study design. PMID:16887016

Expression level of miRNAs on chromosome 14q32.31 region correlates with tumor aggressiveness and survival of glioblastoma patients.

PubMed

Shahar, Tal; Granit, Avital; Zrihan, Daniel; Canello, Tamar; Charbit, Hanna; Einstein, Ofira; Rozovski, Uri; Elgavish, Sharona; Ram, Zvi; Siegal, Tali; Lavon, Iris

2016-12-01

The 54 microRNAs (miRNAs) within the DLK-DIO3 genomic region on chromosome 14q32.31 (cluster-14-miRNAs) are organized into sub-clusters 14A and 14B. These miRNAs are downregulated in glioblastomas and might have a tumor suppressive role. Any association between the expression levels of cluster-14-miRNAs with overall survival (OS) is undetermined. We randomly selected miR-433, belonging to sub-cluster 14A and miR-323a-3p and miR-369-3p, belonging to sub-cluster 14B, and assessed their role in glioblastomas in vitro and in vivo. We also determined the expression level of cluster-14-miRNAs in 27 patients with newly diagnosed glioblastoma, and analyzed the association between their level of expression and OS. Overexpression of miR-323a-3p and miR-369-3p, but not miR-433, in glioblastoma cells inhibited their proliferation and migration in vitro. Mice implanted with glioblastoma cells overexpressing miR323a-3p and miR369-3p, but not miR433, exhibited prolonged survival compared to controls (P = .003). Bioinformatics analysis identified 13 putative target genes of cluster-14-miRNAs, and real-time RT-PCR validated these findings. Pathway analysis of the putative target genes identified neuregulin as the most enriched pathway. The expression level of cluster-14-miRNAs correlated with patients' OS. The median OS was 8.5 months for patients with low expression levels and 52.7 months for patients with high expression levels (HR 0.34; 95 % CI 0.12-0.59, P = .003). The expression level of cluster-14-miRNAs correlates directly with OS, suggesting a role for this cluster in promoting aggressive behavior of glioblastoma, possibly through ErBb/neuregulin signaling.

Dewetting and spreading transitions for active matter on random pinning substrates.

PubMed

Sándor, Cs; Libál, A; Reichhardt, C; Olson Reichhardt, C J

2017-05-28

We show that sterically interacting self-propelled disks in the presence of random pinning substrates exhibit transitions among a variety of different states. In particular, from a phase separated cluster state, the disks can spread out and homogeneously cover the substrate in what can be viewed as an example of an active matter wetting transition. We map the location of this transition as a function of activity, disk density, and substrate strength, and we also identify other phases including a cluster state, coexistence between a cluster and a labyrinth wetted phase, and a pinned liquid. Convenient measures of these phases include the cluster size, which dips at the wetting-dewetting transition, and the fraction of sixfold coordinated particles, which drops when dewetting occurs.

The asymptotic behavior in a reversible random coagulation-fragmentation polymerization process with sub-exponential decay

NASA Astrophysics Data System (ADS)

Dong, Siqun; Zhao, Dianli

2018-01-01

This paper studies the subcritical, near-critical and supercritical asymptotic behavior of a reversible random coagulation-fragmentation polymerization process as N → ∞, with the number of distinct ways to form a k-clusters from k units satisfying f(k) =(1 + o (1)) cr-ke-kαk-β, where 0 < α < 1 and β > 0. When the cluster size is small, its distribution is proved to converge to the Gaussian distribution. For the medium clusters, its distribution will converge to Poisson distribution in supercritical stage, and no large clusters exist in this stage. Furthermore, the largest length of polymers of size N is of order ln N in the subcritical stage under α ⩽ 1 / 2.

Boredom-proneness, loneliness, social engagement and depression and their association with cognitive function in older people: a population study.

PubMed

Conroy, Ronan M; Golden, Jeannette; Jeffares, Isabelle; O'Neill, Desmond; McGee, Hannah

2010-08-01

In this study, we use data from a population survey of persons aged 65 and over living in the Irish Republic to examine the relationship of cognitive impairment, assessed using the Abbreviated Mental Test, with loneliness, boredom-proneness, social relations, and depression. Participants were randomly selected community-dwelling Irish people aged 65+ years. An Abbreviated Mental Test score of 8 or 9 out of 10 was classified as 'low normal', and a score of less than 8 as 'possible cognitive impairment'. We used clustering around latent variables analysis (CLV) to identify families of variables associated with reduced cognitive function. The overall prevalence of possible cognitive impairment was 14.7% (95% CI 12.4-17.3%). Low normal scores had a prevalence of 30.5% (95% CI 27.2-33.7%). CLV analysis identified three groups of predictors: 'Low social support' (widowed, living alone, low social support), 'personal cognitive reserve' (low social activity, no leisure exercise, never having married, loneliness and boredom-proneness), and 'sociodemographic cognitive reserve' (primary education, rural domicile). In multivariate analysis, both cognitive reserve clusters, but not social support, were independently associated with cognitive function. Loneliness and boredom-proneness are associated with reduced cognitive function in older age, and cluster with other factors associated with cognitive reserve. Both may have a common underlying mechanism in the failure to select and maintain attention on particular features of the social environment (loneliness) or the non-social environment (boredom-proneness).

Implementation of a quantum random number generator based on the optimal clustering of photocounts

NASA Astrophysics Data System (ADS)

Balygin, K. A.; Zaitsev, V. I.; Klimov, A. N.; Kulik, S. P.; Molotkov, S. N.

2017-10-01

To implement quantum random number generators, it is fundamentally important to have a mathematically provable and experimentally testable process of measurements of a system from which an initial random sequence is generated. This makes sure that randomness indeed has a quantum nature. A quantum random number generator has been implemented with the use of the detection of quasi-single-photon radiation by a silicon photomultiplier (SiPM) matrix, which makes it possible to reliably reach the Poisson statistics of photocounts. The choice and use of the optimal clustering of photocounts for the initial sequence of photodetection events and a method of extraction of a random sequence of 0's and 1's, which is polynomial in the length of the sequence, have made it possible to reach a yield rate of 64 Mbit/s of the output certainly random sequence.

Phylogenetic analysis of different breeds of domestic chickens in selected area of Peninsular Malaysia inferred from partial cytochrome b gene information and RAPD markers.

PubMed

Yap, Fook Choy; Yan, Yap Jin; Loon, Kiung Teh; Zhen, Justina Lee Ning; Kamau, Nelly Warau; Kumaran, Jayaraj Vijaya

2010-10-01

The present investigation was carried out in an attempt to study the phylogenetic analysis of different breeds of domestic chickens in Peninsular Malaysia inferred from partial cytochrome b gene information and random amplified polymorphic DNA (RAPD) markers. Phylogenetic analysis using both neighbor-joining (NJ) and maximum parsimony (MP) methods produced three clusters that encompassed Type-I village chickens, the red jungle fowl subspecies and the Japanese Chunky broilers. The phylogenetic analysis also revealed that majority of the Malaysian commercial chickens were randomly assembled with the Type-II village chickens. In RAPD assay, phylogenetic analysis using neighbor-joining produced six clusters that were completely distinguished based on the locality of chickens. High levels of genetic variations were observed among the village chickens, the commercial broilers, and between the commercial broilers and layer chickens. In this study, it was found that Type-I village chickens could be distinguished from the commercial chickens and Type-II village chickens at the position of the 27th nucleotide of the 351 bp cytochrome b gene. This study also revealed that RAPD markers were unable to differentiate the type of chickens, but it showed the effectiveness of RAPD in evaluating the genetic variation and the genetic relationships between chicken lines and populations.

Outcome-Driven Cluster Analysis with Application to Microarray Data.

PubMed

Hsu, Jessie J; Finkelstein, Dianne M; Schoenfeld, David A

2015-01-01

One goal of cluster analysis is to sort characteristics into groups (clusters) so that those in the same group are more highly correlated to each other than they are to those in other groups. An example is the search for groups of genes whose expression of RNA is correlated in a population of patients. These genes would be of greater interest if their common level of RNA expression were additionally predictive of the clinical outcome. This issue arose in the context of a study of trauma patients on whom RNA samples were available. The question of interest was whether there were groups of genes that were behaving similarly, and whether each gene in the cluster would have a similar effect on who would recover. For this, we develop an algorithm to simultaneously assign characteristics (genes) into groups of highly correlated genes that have the same effect on the outcome (recovery). We propose a random effects model where the genes within each group (cluster) equal the sum of a random effect, specific to the observation and cluster, and an independent error term. The outcome variable is a linear combination of the random effects of each cluster. To fit the model, we implement a Markov chain Monte Carlo algorithm based on the likelihood of the observed data. We evaluate the effect of including outcome in the model through simulation studies and describe a strategy for prediction. These methods are applied to trauma data from the Inflammation and Host Response to Injury research program, revealing a clustering of the genes that are informed by the recovery outcome.

Evaluating the Implementation of a School-Based Emotional Well-Being Programme: A Cluster Randomized Controlled Trial of Zippy's Friends for Children in Disadvantaged Primary Schools

ERIC Educational Resources Information Center

Clarke, Aleisha M.; Bunting, Brendan; Barry, Margaret M.

2014-01-01

Schools are recognized as one of the most important settings for promoting social and emotional well-being among children and adolescents. This clustered randomized controlled trial evaluated Zippy's Friends, an international school-based emotional well-being programme, with 766 children from designated disadvantaged schools. The purpose of this…

Citywide cluster randomized trial to restore blighted vacant land and its effects on violence, crime, and fear

Treesearch

Charles C. Branas; Eugenia South; Michelle C. Kondo; Bernadette C. Hohl; Philippe Bourgois; Douglas J. Wiebe; John M. MacDonald

2018-01-01

Vacant and blighted urban land is a widespread and potentially risky environmental condition encountered by millions of people on a daily basis. About 15% of the land in US cities is deemed vacant or abandoned, an area roughly the size of Switzerland. In a citywide cluster randomized controlled trial, we investigated the effects of standardized, reproducible...

Reducing Tobacco Use among Low Socio-Economic Status Youth in Delhi, India: Outcomes from Project ACTIVITY, a Cluster Randomized Trial

ERIC Educational Resources Information Center

Harrell, Melissa B.; Arora, Monika; Bassi, Shalini; Gupta, Vinay K.; Perry, Cheryl L.; Reddy, K. Srinath

2016-01-01

To test the efficacy of an intervention to reduce tobacco use among youth (10-19 years old) in slum communities in Delhi, India. This community-based cluster-randomized trial included 14 slums composed of purposely built resettlement colonies and adjacent inhabitant-built Jhuggi Jhopris. Youth in the intervention received a 2 year…

Cluster-Randomized Controlled Trial Evaluating the Effectiveness of Computer-Assisted Intervention Delivered by Educators for Children with Speech Sound Disorders

ERIC Educational Resources Information Center

McLeod, Sharynne; Baker, Elise; McCormack, Jane; Wren, Yvonne; Roulstone, Sue; Crowe, Kathryn; Masso, Sarah; White, Paul; Howland, Charlotte

2017-01-01

Purpose: The aim was to evaluate the effectiveness of computer-assisted input-based intervention for children with speech sound disorders (SSD). Method: The Sound Start Study was a cluster-randomized controlled trial. Seventy-nine early childhood centers were invited to participate, 45 were recruited, and 1,205 parents and educators of 4- and…

Improving Elementary School Quality through the Use of a Social-Emotional and Character Development Program: A Matched-Pair, Cluster-Randomized, Controlled Trial in Hawai'i

ERIC Educational Resources Information Center

Snyder, Frank J.; Vuchinich, Samuel; Acock, Alan; Washburn, Isaac J.; Flay, Brian R.

2012-01-01

Background: School safety and quality affect student learning and success. This study examined the effects of a comprehensive elementary school-wide social-emotional and character education program, Positive Action, on teacher, parent, and student perceptions of school safety and quality utilizing a matched-pair, cluster-randomized, controlled…

Detecting Intervention Effects in a Cluster-Randomized Design Using Multilevel Structural Equation Modeling for Binary Responses

ERIC Educational Resources Information Center

Cho, Sun-Joo; Preacher, Kristopher J.; Bottge, Brian A.

2015-01-01

Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test--post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test…

Inadequacy of ethical conduct and reporting of stepped wedge cluster randomized trials: Results from a systematic review.

PubMed

Taljaard, Monica; Hemming, Karla; Shah, Lena; Giraudeau, Bruno; Grimshaw, Jeremy M; Weijer, Charles

2017-08-01

Background/aims The use of the stepped wedge cluster randomized design is rapidly increasing. This design is commonly used to evaluate health policy and service delivery interventions. Stepped wedge cluster randomized trials have unique characteristics that complicate their ethical interpretation. The 2012 Ottawa Statement provides comprehensive guidance on the ethical design and conduct of cluster randomized trials, and the 2010 CONSORT extension for cluster randomized trials provides guidelines for reporting. Our aims were to assess the adequacy of the ethical conduct and reporting of stepped wedge trials to date, focusing on research ethics review and informed consent. Methods We conducted a systematic review of stepped wedge cluster randomized trials in health research published up to 2014 in English language journals. We extracted details of study intervention and data collection procedures, as well as reporting of research ethics review and informed consent. Two reviewers independently extracted data from each trial; discrepancies were resolved through discussion. We identified the presence of any research participants at the cluster level and the individual level. We assessed ethical conduct by tabulating reporting of research ethics review and informed consent against the presence of research participants. Results Of 32 identified stepped wedge trials, only 24 (75%) reported review by a research ethics committee, and only 16 (50%) reported informed consent from any research participants-yet, all trials included research participants at some level. In the subgroup of 20 trials with research participants at cluster level, only 4 (20%) reported informed consent from such participants; in 26 trials with individual-level research participants, only 15 (58%) reported their informed consent. Interventions (regardless of whether targeting cluster- or individual-level participants) were delivered at the group level in more than two-thirds of trials; nine trials (28%) had no identifiable data collected from any research participants. Overall, only three trials (9%) indicated that a waiver of consent had been granted by a research ethics committee. When considering the combined requirement of research ethics review and informed consent (or a waiver), only one in three studies were compliant. Conclusion The ethical conduct and reporting of key ethical protections in stepped wedge trials, namely, research ethics review and informed consent, are inadequate. We recommend that stepped wedge trials be classified as research and reviewed and approved by a research ethics committee. We also recommend that researchers appropriately identify research participants (which may include health professionals), seek informed consent or appeal to an ethics committee for a waiver of consent, and include explicit details of research ethics approval and informed consent in the trial report.

Fischer–Tropsch Synthesis at a Low Pressure on Subnanometer Cobalt Oxide Clusters: The Effect of Cluster Size and Support on Activity and Selectivity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Sungsik; Lee, Byeongdu; Seifert, Sönke

2015-05-21

In this study, the catalytic activity and changes in the oxidation state during the Fischer Tropsch (FT) reaction was investigated on subnanometer size-selected cobalt clusters deposited on oxide (Al2O3, MgO) and carbon-based (ultrananocrystalline diamond UNCD) supports by temperature programmed reaction (TPRx) combined with in-situ grazing-incidence X-ray absorption characterization (GIXAS). The activity and selectivity of ultrasmall cobalt clusters exhibits a very strong dependence on cluster size and support. The evolution of the oxidation state of metal cluster during the reaction reveals that metal-support interaction plays a key role in the reaction.

The Systematics of Strong Lens Modeling Quantified: The Effects of Constraint Selection and Redshift Information on Magnification, Mass, and Multiple Image Predictability

NASA Astrophysics Data System (ADS)

Johnson, Traci L.; Sharon, Keren

2016-11-01

Until now, systematic errors in strong gravitational lens modeling have been acknowledged but have never been fully quantified. Here, we launch an investigation into the systematics induced by constraint selection. We model the simulated cluster Ares 362 times using random selections of image systems with and without spectroscopic redshifts and quantify the systematics using several diagnostics: image predictability, accuracy of model-predicted redshifts, enclosed mass, and magnification. We find that for models with >15 image systems, the image plane rms does not decrease significantly when more systems are added; however, the rms values quoted in the literature may be misleading as to the ability of a model to predict new multiple images. The mass is well constrained near the Einstein radius in all cases, and systematic error drops to <2% for models using >10 image systems. Magnification errors are smallest along the straight portions of the critical curve, and the value of the magnification is systematically lower near curved portions. For >15 systems, the systematic error on magnification is ∼2%. We report no trend in magnification error with the fraction of spectroscopic image systems when selecting constraints at random; however, when using the same selection of constraints, increasing this fraction up to ∼0.5 will increase model accuracy. The results suggest that the selection of constraints, rather than quantity alone, determines the accuracy of the magnification. We note that spectroscopic follow-up of at least a few image systems is crucial because models without any spectroscopic redshifts are inaccurate across all of our diagnostics.

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

EPA Science Inventory

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...

Cluster designs to assess the prevalence of acute malnutrition by lot quality assurance sampling: a validation study by computer simulation.

PubMed

Olives, Casey; Pagano, Marcello; Deitchler, Megan; Hedt, Bethany L; Egge, Kari; Valadez, Joseph J

2009-04-01

Traditional lot quality assurance sampling (LQAS) methods require simple random sampling to guarantee valid results. However, cluster sampling has been proposed to reduce the number of random starting points. This study uses simulations to examine the classification error of two such designs, a 67x3 (67 clusters of three observations) and a 33x6 (33 clusters of six observations) sampling scheme to assess the prevalence of global acute malnutrition (GAM). Further, we explore the use of a 67x3 sequential sampling scheme for LQAS classification of GAM prevalence. Results indicate that, for independent clusters with moderate intracluster correlation for the GAM outcome, the three sampling designs maintain approximate validity for LQAS analysis. Sequential sampling can substantially reduce the average sample size that is required for data collection. The presence of intercluster correlation can impact dramatically the classification error that is associated with LQAS analysis.

Community involvement in dengue vector control: cluster randomised trial

PubMed Central

Toledo, M E; Rodríguez, M; Gomez, D; Baly, A; Benitez, J R; Van der Stuyft, P

2009-01-01

Objective To assess the effectiveness of an integrated community based environmental management strategy to control Aedes aegypti, the vector of dengue, compared with a routine strategy. Design Cluster randomised trial. Setting Guantanamo, Cuba. Participants 32 circumscriptions (around 2000 inhabitants each). Interventions The circumscriptions were randomly allocated to control clusters (n=16) comprising routine Aedes control programme (entomological surveillance, source reduction, selective adulticiding, and health education) and to intervention clusters (n=16) comprising the routine Aedes control programme combined with a community based environmental management approach. Main outcome measures The primary outcome was levels of Aedes infestation: house index (number of houses positive for at least one container with immature stages of Ae aegypti per 100 inspected houses), Breteau index (number of containers positive for immature stages of Ae aegypti per 100 inspected houses), and the pupae per inhabitant statistic (number of Ae aegypti pupae per inhabitant). Results All clusters were subjected to the intended intervention; all completed the study protocol up to February 2006 and all were included in the analysis. At baseline the Aedes infestation levels were comparable between intervention and control clusters: house index 0.25% v 0.20%, pupae per inhabitant 0.44×10−3 v 0.29×10−3. At the end of the intervention these indices were significantly lower in the intervention clusters: rate ratio for house indices 0.49 (95% confidence interval 0.27 to 0.88) and rate ratio for pupae per inhabitant 0.27 (0.09 to 0.76). Conclusion A community based environmental management embedded in a routine control programme was effective at reducing levels of Aedes infestation. Trial registration Current Controlled Trials ISRCTN88405796. PMID:19509031

Risk factors associated with occurrence of African swine fever outbreaks in smallholder pig farms in four districts along the Uganda-Kenya border.

PubMed

Nantima, Noelina; Ocaido, Michael; Ouma, Emily; Davies, Jocelyn; Dione, Michel; Okoth, Edward; Mugisha, Anthony; Bishop, Richard

2015-03-01

A cross-sectional survey was carried out to assess risk factors associated with occurrence of African swine fever (ASF) outbreaks in smallholder pig farms in four districts along Kenya-Uganda border. Information was collected by administering questionnaires to 642 randomly selected pig households in the study area. The study showed that the major risk factors that influenced ASF occurrence were purchase of pigs in the previous year (p < 0.000) and feeding of pigs with swill (p < 0.024). By employing cluster analysis, three clusters of pig production types were identified based on production characteristics that were found to differ significantly between districts. The most vulnerable cluster to ASF was households with the highest reported number of ASF outbreaks and composed of those that practiced free range at least some of the time. The majority of the households in this cluster were from Busia district in Uganda. On the other hand, the least vulnerable cluster to ASF composed of households that had the least number of pig purchases, minimal swill feeding, and less treatment for internal and external parasites. The largest proportion of households in this cluster was from Busia district Kenya. The study recommended the need to sensitize farmers to adopt proper biosecurity practices such as total confinement of pigs, treatment of swill, isolation of newly purchased pigs for at least 2 weeks, and provision of incentives for farmers to report suspected outbreaks to authorities and rapid confirmation of outbreaks.

Model selection for clustering of pharmacokinetic responses.

PubMed

Guerra, Rui P; Carvalho, Alexandra M; Mateus, Paulo

2018-08-01

Pharmacokinetics comprises the study of drug absorption, distribution, metabolism and excretion over time. Clinical pharmacokinetics, focusing on therapeutic management, offers important insights towards personalised medicine through the study of efficacy and toxicity of drug therapies. This study is hampered by subject's high variability in drug blood concentration, when starting a therapy with the same drug dosage. Clustering of pharmacokinetics responses has been addressed recently as a way to stratify subjects and provide different drug doses for each stratum. This clustering method, however, is not able to automatically determine the correct number of clusters, using an user-defined parameter for collapsing clusters that are closer than a given heuristic threshold. We aim to use information-theoretical approaches to address parameter-free model selection. We propose two model selection criteria for clustering pharmacokinetics responses, founded on the Minimum Description Length and on the Normalised Maximum Likelihood. Experimental results show the ability of model selection schemes to unveil the correct number of clusters underlying the mixture of pharmacokinetics responses. In this work we were able to devise two model selection criteria to determine the number of clusters in a mixture of pharmacokinetics curves, advancing over previous works. A cost-efficient parallel implementation in Java of the proposed method is publicly available for the community. Copyright © 2018 Elsevier B.V. All rights reserved.

Evidence for a global seismic-moment release sequence

USGS Publications Warehouse

Bufe, C.G.; Perkins, D.M.

2005-01-01

Temporal clustering of the larger earthquakes (foreshock-mainshock-aftershock) followed by relative quiescence (stress shadow) are characteristic of seismic cycles along plate boundaries. A global seismic-moment release history, based on a little more than 100 years of instrumental earthquake data in an extended version of the catalog of Pacheco and Sykes (1992), illustrates similar behavior for Earth as a whole. Although the largest earthquakes have occurred in the circum-Pacific region, an analysis of moment release in the hemisphere antipodal to the Pacific plate shows a very similar pattern. Monte Carlo simulations confirm that the global temporal clustering of great shallow earthquakes during 1952-1964 at M ??? 9.0 is highly significant (4% random probability) as is the clustering of the events of M ??? 8.6 (0.2% random probability) during 1950-1965. We have extended the Pacheco and Sykes (1992) catalog from 1989 through 2001 using Harvard moment centroid data. Immediately after the 1950-1965 cluster, significant quiescence at and above M 8.4 begins and continues until 2001 (0.5% random probability). In alternative catalogs derived by correcting for possible random errors in magnitude estimates in the extended Pacheco-Sykes catalog, the clustering of M ??? 9 persists at a significant level. These observations indicate that, for great earthquakes, Earth behaves as a coherent seismotectonic system. A very-large-scale mechanism for global earthquake triggering and/or stress transfer is implied. There are several candidates, but so far only viscoelastic relaxation has been modeled on a global scale.

Coordinate based random effect size meta-analysis of neuroimaging studies.

PubMed

Tench, C R; Tanasescu, Radu; Constantinescu, C S; Auer, D P; Cottam, W J

2017-06-01

Low power in neuroimaging studies can make them difficult to interpret, and Coordinate based meta-analysis (CBMA) may go some way to mitigating this issue. CBMA has been used in many analyses to detect where published functional MRI or voxel-based morphometry studies testing similar hypotheses report significant summary results (coordinates) consistently. Only the reported coordinates and possibly t statistics are analysed, and statistical significance of clusters is determined by coordinate density. Here a method of performing coordinate based random effect size meta-analysis and meta-regression is introduced. The algorithm (ClusterZ) analyses both coordinates and reported t statistic or Z score, standardised by the number of subjects. Statistical significance is determined not by coordinate density, but by a random effects meta-analyses of reported effects performed cluster-wise using standard statistical methods and taking account of censoring inherent in the published summary results. Type 1 error control is achieved using the false cluster discovery rate (FCDR), which is based on the false discovery rate. This controls both the family wise error rate under the null hypothesis that coordinates are randomly drawn from a standard stereotaxic space, and the proportion of significant clusters that are expected under the null. Such control is necessary to avoid propagating and even amplifying the very issues motivating the meta-analysis in the first place. ClusterZ is demonstrated on both numerically simulated data and on real data from reports of grey matter loss in multiple sclerosis (MS) and syndromes suggestive of MS, and of painful stimulus in healthy controls. The software implementation is available to download and use freely. Copyright © 2017 Elsevier Inc. All rights reserved.

Sample size adjustments for varying cluster sizes in cluster randomized trials with binary outcomes analyzed with second-order PQL mixed logistic regression.

PubMed

Candel, Math J J M; Van Breukelen, Gerard J P

2010-06-30

Adjustments of sample size formulas are given for varying cluster sizes in cluster randomized trials with a binary outcome when testing the treatment effect with mixed effects logistic regression using second-order penalized quasi-likelihood estimation (PQL). Starting from first-order marginal quasi-likelihood (MQL) estimation of the treatment effect, the asymptotic relative efficiency of unequal versus equal cluster sizes is derived. A Monte Carlo simulation study shows this asymptotic relative efficiency to be rather accurate for realistic sample sizes, when employing second-order PQL. An approximate, simpler formula is presented to estimate the efficiency loss due to varying cluster sizes when planning a trial. In many cases sampling 14 per cent more clusters is sufficient to repair the efficiency loss due to varying cluster sizes. Since current closed-form formulas for sample size calculation are based on first-order MQL, planning a trial also requires a conversion factor to obtain the variance of the second-order PQL estimator. In a second Monte Carlo study, this conversion factor turned out to be 1.25 at most. (c) 2010 John Wiley & Sons, Ltd.

Review of Recent Methodological Developments in Group-Randomized Trials: Part 2-Analysis.

PubMed

Turner, Elizabeth L; Prague, Melanie; Gallis, John A; Li, Fan; Murray, David M

2017-07-01

In 2004, Murray et al. reviewed methodological developments in the design and analysis of group-randomized trials (GRTs). We have updated that review with developments in analysis of the past 13 years, with a companion article to focus on developments in design. We discuss developments in the topics of the earlier review (e.g., methods for parallel-arm GRTs, individually randomized group-treatment trials, and missing data) and in new topics, including methods to account for multiple-level clustering and alternative estimation methods (e.g., augmented generalized estimating equations, targeted maximum likelihood, and quadratic inference functions). In addition, we describe developments in analysis of alternative group designs (including stepped-wedge GRTs, network-randomized trials, and pseudocluster randomized trials), which require clustering to be accounted for in their design and analysis.

Cluster: A New Application for Spatial Analysis of Pixelated Data for Epiphytotics.

PubMed

Nelson, Scot C; Corcoja, Iulian; Pethybridge, Sarah J

2017-12-01

Spatial analysis of epiphytotics is essential to develop and test hypotheses about pathogen ecology, disease dynamics, and to optimize plant disease management strategies. Data collection for spatial analysis requires substantial investment in time to depict patterns in various frames and hierarchies. We developed a new approach for spatial analysis of pixelated data in digital imagery and incorporated the method in a stand-alone desktop application called Cluster. The user isolates target entities (clusters) by designating up to 24 pixel colors as nontargets and moves a threshold slider to visualize the targets. The app calculates the percent area occupied by targeted pixels, identifies the centroids of targeted clusters, and computes the relative compass angle of orientation for each cluster. Users can deselect anomalous clusters manually and/or automatically by specifying a size threshold value to exclude smaller targets from the analysis. Up to 1,000 stochastic simulations randomly place the centroids of each cluster in ranked order of size (largest to smallest) within each matrix while preserving their calculated angles of orientation for the long axes. A two-tailed probability t test compares the mean inter-cluster distances for the observed versus the values derived from randomly simulated maps. This is the basis for statistical testing of the null hypothesis that the clusters are randomly distributed within the frame of interest. These frames can assume any shape, from natural (e.g., leaf) to arbitrary (e.g., a rectangular or polygonal field). Cluster summarizes normalized attributes of clusters, including pixel number, axis length, axis width, compass orientation, and the length/width ratio, available to the user as a downloadable spreadsheet. Each simulated map may be saved as an image and inspected. Provided examples demonstrate the utility of Cluster to analyze patterns at various spatial scales in plant pathology and ecology and highlight the limitations, trade-offs, and considerations for the sensitivities of variables and the biological interpretations of results. The Cluster app is available as a free download for Apple computers at iTunes, with a link to a user guide website.

X-Ray Temperatures, Luminosities, and Masses from XMM-Newton Follow-up of the First Shear-selected Galaxy Cluster Sample

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deshpande, Amruta J.; Hughes, John P.; Wittman, David, E-mail: amrejd@physics.rutgers.edu, E-mail: jph@physics.rutgers.edu, E-mail: dwittman@physics.ucdavis.edu

We continue the study of the first sample of shear-selected clusters from the initial 8.6 square degrees of the Deep Lens Survey (DLS); a sample with well-defined selection criteria corresponding to the highest ranked shear peaks in the survey area. We aim to characterize the weak lensing selection by examining the sample’s X-ray properties. There are multiple X-ray clusters associated with nearly all the shear peaks: 14 X-ray clusters corresponding to seven DLS shear peaks. An additional three X-ray clusters cannot be definitively associated with shear peaks, mainly due to large positional offsets between the X-ray centroid and the shearmore » peak. Here we report on the XMM-Newton properties of the 17 X-ray clusters. The X-ray clusters display a wide range of luminosities and temperatures; the L {sub X} − T {sub X} relation we determine for the shear-associated X-ray clusters is consistent with X-ray cluster samples selected without regard to dynamical state, while it is inconsistent with self-similarity. For a subset of the sample, we measure X-ray masses using temperature as a proxy, and compare to weak lensing masses determined by the DLS team. The resulting mass comparison is consistent with equality. The X-ray and weak lensing masses show considerable intrinsic scatter (∼48%), which is consistent with X-ray selected samples when their X-ray and weak lensing masses are independently determined.« less

Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses.

PubMed

Kumpf, Alexander; Tost, Bianca; Baumgart, Marlene; Riemer, Michael; Westermann, Rudiger; Rautenhaus, Marc

2018-01-01

In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we - a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of "Tropical Cyclone Karl", guiding the user towards the cluster robustness information required for subsequent ensemble analysis.

Two-year impact of community-based health screening and parenting groups on child development in Zambia: Follow-up to a cluster-randomized controlled trial.

PubMed

Rockers, Peter C; Zanolini, Arianna; Banda, Bowen; Chipili, Mwaba Moono; Hughes, Robert C; Hamer, Davidson H; Fink, Günther

2018-04-01

Early childhood interventions have potential to offset the negative impact of early adversity. We evaluated the impact of a community-based parenting group intervention on child development in Zambia. We conducted a non-masked cluster-randomized controlled trial in Southern Province, Zambia. Thirty clusters of villages were matched based on population density and distance from the nearest health center, and randomly assigned to intervention (15 clusters, 268 caregiver-child dyads) or control (15 clusters, 258 caregiver-child dyads). Caregivers were eligible if they had a child 6 to 12 months old at baseline. In intervention clusters, caregivers were visited twice per month during the first year of the study by child development agents (CDAs) and were invited to attend fortnightly parenting group meetings. Parenting groups selected "head mothers" from their communities who were trained by CDAs to facilitate meetings and deliver a diverse parenting curriculum. The parenting group intervention, originally designed to run for 1 year, was extended, and households were visited for a follow-up assessment at the end of year 2. The control group did not receive any intervention. Intention-to-treat analysis was performed for primary outcomes measured at the year 2 follow-up: stunting and 5 domains of neurocognitive development measured using the Bayley Scales of Infant and Toddler Development-Third Edition (BSID-III). In order to show Cohen's d estimates, BSID-III composite scores were converted to z-scores by standardizing within the study population. In all, 195/268 children (73%) in the intervention group and 182/258 children (71%) in the control group were assessed at endline after 2 years. The intervention significantly reduced stunting (56/195 versus 72/182; adjusted odds ratio 0.45, 95% CI 0.22 to 0.92; p = 0.028) and had a significant positive impact on language (β 0.14, 95% CI 0.01 to 0.27; p = 0.039). The intervention did not significantly impact cognition (β 0.11, 95% CI -0.06 to 0.29; p = 0.196), motor skills (β -0.01, 95% CI -0.25 to 0.24; p = 0.964), adaptive behavior (β 0.21, 95% CI -0.03 to 0.44; p = 0.088), or social-emotional development (β 0.20, 95% CI -0.04 to 0.44; p = 0.098). Observed impacts may have been due in part to home visits by CDAs during the first year of the intervention. The results of this trial suggest that parenting groups hold promise for improving child development, particularly physical growth, in low-resource settings like Zambia. ClinicalTrials.gov NCT02234726.

An adequacy evaluation of a maternal health intervention in rural Honduras: the impact of engagement of men and empowerment of women.

PubMed

Berti, Peter R; Sohani, Salim; Costa, Edith da; Klaas, Naomi; Amendola, Luis; Duron, Joel

2015-02-01

To determine the impact that a 6-year maternal and child health project in rural Honduras had on maternal health services and outcomes, and to test the effect of level of father involvement on maternal health. This was a program evaluation conducted through representative household surveys administered at baseline in 2007 and endline in 2011 using 30 cluster samples randomly-selected from the 229 participating communities. Within each cluster, 10 households having at least one mother-child pair were randomly selected to complete a questionnaire, for a total of about 300 respondents answering close to 100 questions each. Changes in key outcome variables from baseline to endline were tested using logistic regression, controlling for mother's education and father's involvement. There were improvements in most maternal health indicators, including an increase in women attending prenatal checkups (84% to 92%, P = 0.05) and institutional births (44% to 63%, P = 0.002). However, the involvement of the fathers decreased as reflected by the percentage of fathers accompanying mothers to prenatal checkups (48% to 41%, P = 0.01); the fathers' reported interest in prenatal care (74% to 52%, P = 0.0001); and fathers attending the birth (66% to 54%, P = 0.05). There was an interaction between the fathers' scores and the maternal outcomes, with a larger increase in institutional births among mothers with the least-involved fathers. Rather than the father's involvement being key, changes in the mothers may have led to increased institutional births. The project may have empowered women through early identification of pregnancy and stronger social connections encouraged by home visits and pregnancy clubs. This would have enabled even the women with unsupportive fathers to make healthier choices and achieve higher rates of institutional births.

Juvenile Justice-Translational Research on Interventions for Adolescents in the Legal System (JJ-TRIALS): a cluster randomized trial targeting system-wide improvement in substance use services.

PubMed

Knight, Danica K; Belenko, Steven; Wiley, Tisha; Robertson, Angela A; Arrigona, Nancy; Dennis, Michael; Bartkowski, John P; McReynolds, Larkin S; Becan, Jennifer E; Knudsen, Hannah K; Wasserman, Gail A; Rose, Eve; DiClemente, Ralph; Leukefeld, Carl

2016-04-29

The purpose of this paper is to describe the Juvenile Justice-Translational Research on Interventions for Adolescents in the Legal System (JJ-TRIALS) study, a cooperative implementation science initiative involving the National Institute on Drug Abuse, six research centers, a coordinating center, and Juvenile Justice Partners representing seven US states. While the pooling of resources across centers enables a robust implementation study design involving 36 juvenile justice agencies and their behavioral health partner agencies, co-producing a study protocol that has potential to advance implementation science, meets the needs of all constituencies (funding agency, researchers, partners, study sites), and can be implemented with fidelity across the cooperative can be challenging. This paper describes (a) the study background and rationale, including the juvenile justice context and best practices for substance use disorders, (b) the selection and use of an implementation science framework to guide study design and inform selection of implementation components, and (c) the specific study design elements, including research questions, implementation interventions, measurement, and analytic plan. The JJ-TRIALS primary study uses a head-to-head cluster randomized trial with a phased rollout to evaluate the differential effectiveness of two conditions (Core and Enhanced) in 36 sites located in seven states. A Core strategy for promoting change is compared to an Enhanced strategy that incorporates all core strategies plus active facilitation. Target outcomes include improvements in evidence-based screening, assessment, and linkage to substance use treatment. Contributions to implementation science are discussed as well as challenges associated with designing and deploying a complex, collaborative project. NCT02672150 .

Evaluation of Cognitive and Behavioral Effects of Peer Education Model-Based Intervention to Sun Safe in Children

PubMed Central

HU, Ping; HAN, Lingli; SHARMA, Manoj; ZENG, Huan; ZHANG, Yong; LI, Hui; ZHAO, Yong

2014-01-01

Abstract Background There have been many studies that evidence the health hazards of sunlight exposure, but less study on sun safe intervention model, especially in China. Our aim was to evaluate the cognitive and behavioral effects of a peer education model-based intervention to sun safe in children. Methods Cluster random control intervention was conducted in one district in Chongqing, China. Two primary schools, selected through stratified clustered sampling approach (two grades in each school, three classes in each grade) were designated as intervention (n=304) and control schools (n=305) randomly. 36 students, selected as peer educators in intervention group, were trained for one month. Educational activities such as discussions were organized by peer educator for one month. There was no sun safe education to participants in control school during the project period. The evaluation of changes of sun safe knowledge (the primary outcome), attitude and behavior (the secondary outcome measures) were conducted before intervention and at months of 0, 1 and 6 of the intervention to two groups using quantitative and qualitative methods. Results After the intervention, sun safe knowledge score which gained by the students from intervention group has been remarkably improved, compared to baseline survey (24.48±6.17 vs. 29.51±6.75) (P<0.001), and it kept this high level (29.02±7.96 and. 28.65±8.96), while control group students' scores have made no difference (P=0.410). Most of students have changed their sun safe behavior after the intervention. Conclusion Peer education program is somewhat effective in some dimensions for improving children's understanding of sun safe knowledge and behavior. PMID:25988089

Correlates of smoking with socioeconomic status, leisure time physical activity and alcohol consumption among Polish adults from randomly selected regions.

PubMed

Woitas-Slubowska, Donata; Hurnik, Elzbieta; Skarpańska-Stejnborn, Anna

2010-12-01

To determine the association between smoking status and leisure time physical activity (LTPA), alcohol consumption, and socioeconomic status (SES) among Polish adults. 466 randomly selected men and women (aged 18-66 years) responded to an anonymous questionnaire regarding smoking, alcohol consumption, LTPA, and SES. Multiple logistic regression was used to examine the association of smoking status with six socioeconomic measures, level of LTPA, and frequency and type of alcohol consumed. Smokers were defined as individuals smoking occasionally or daily. The odds of being smoker were 9 times (men) and 27 times (women) higher among respondents who drink alcohol several times/ week or everyday in comparison to non-drinkers (p < 0.0001 and p < 0.0001). Among men with the elementary/vocational level of education the frequency of smoking was four times higher compared to those with the high educational attainment (p = 0.007). Among women we observed that students were the most frequent smokers. Female students were almost three times more likely to smoke than non-professional women, and two times more likely than physical workers (p = 0.018). The findings of this study indicated that among randomly selected Polish man and women aged 18-66 smoking and alcohol consumption tended to cluster. These results imply that intervention strategies need to target multiple risk factors simultaneously. The highest risk of smoking was observed among low educated men, female students, and both men and women drinking alcohol several times a week or every day. Information on subgroups with the high risk of smoking will help in planning future preventive strategies.

Multiple behaviour change intervention for diarrhoea control in Lusaka, Zambia: a cluster randomised trial.

PubMed

Greenland, Katie; Chipungu, Jenala; Curtis, Val; Schmidt, Wolf-Peter; Siwale, Zumbe; Mudenda, Mweetwa; Chilekwa, Joyce; Lewis, James J; Chilengi, Roma

2016-12-01

Effective prevention and control of diarrhoea requires caregivers to comply with a suite of proven measures, including exclusive breastfeeding, handwashing with soap, correct use of oral rehydration salts, and zinc administration. We aimed to assess the effect of a novel behaviour change intervention using emotional drivers on caregiver practice of these behaviours. We did a cluster randomised controlled trial in Lusaka Province, Zambia. A random sample of 16 health centres (clusters) were selected from a sampling frame of 81 health centres in three of four districts in Lusaka Province using a computerised random number generator. Each cluster was randomly assigned 1:1 to either the intervention-clinic events, community events, and radio messaging-or to a standard care control arm, both for 6 months. Primary outcomes were exclusive breastfeeding (self-report), handwashing with soap (observation), oral rehydration salt solution preparation (demonstration), and zinc use in diarrhoea treatment (self-report). We measured outcome behaviours at baseline before start of intervention and 4-6 weeks post-intervention through repeat cross-sectional surveys with mothers of an infant younger than 6 months and primary caregivers of a child younger than 5 years with recent diarrhoea. We compared outcomes on an intention-to-treat population between intervention and control groups adjusted for baseline behaviour. The study was registered with ClinicalTrials.gov, number NCT02081521. Between Jan 20 and Feb 3, 2014, we recruited 306 mothers of an infant aged 0-5 months (156 intervention, 150 standard care) and 343 primary caregiver of a child aged 0-59 months with recent diarrhoea (176 intervention, 167 standard care) at baseline. Between Oct 20 to Nov 7, 2014, we recruited 401 mothers of an infant 0-5 months (234 intervention, 167 standard care) and 410 primary caregivers of a child 0-59 months with recent diarrhoea (257 intervention, 163 standard care) at endline. Intervention was associated with increased prevalence of self-reported exclusive breastfeeding of infants aged 0-5 months (adjusted difference 10·5%, 95% CI 0·9-19·9). Other primary outcomes were not affected by intervention. Cluster intervention exposure ranged from 11-81%, measured by participant self-report with verification questions. Comparison of control and intervention clusters with coverage greater than 35% provided strong evidence of an intervention effect on oral rehydration salt solution preparation and breastfeeding outcomes. The intervention may have improved exclusive breastfeeding (assessed by self-reporting), but intervention effects were diluted in clusters with low exposure. Complex caregiver practices can improve through interventions built around human motives, but these must be implemented more intensely. Absolute Return for Kids (ARK) and Comic Relief. Copyright © 2016 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license. Published by Elsevier Ltd.. All rights reserved.

A polymer, random walk model for the size-distribution of large DNA fragments after high linear energy transfer radiation

NASA Technical Reports Server (NTRS)

Ponomarev, A. L.; Brenner, D.; Hlatky, L. R.; Sachs, R. K.

2000-01-01

DNA double-strand breaks (DSBs) produced by densely ionizing radiation are not located randomly in the genome: recent data indicate DSB clustering along chromosomes. Stochastic DSB clustering at large scales, from > 100 Mbp down to < 0.01 Mbp, is modeled using computer simulations and analytic equations. A random-walk, coarse-grained polymer model for chromatin is combined with a simple track structure model in Monte Carlo software called DNAbreak and is applied to data on alpha-particle irradiation of V-79 cells. The chromatin model neglects molecular details but systematically incorporates an increase in average spatial separation between two DNA loci as the number of base-pairs between the loci increases. Fragment-size distributions obtained using DNAbreak match data on large fragments about as well as distributions previously obtained with a less mechanistic approach. Dose-response relations, linear at small doses of high linear energy transfer (LET) radiation, are obtained. They are found to be non-linear when the dose becomes so large that there is a significant probability of overlapping or close juxtaposition, along one chromosome, for different DSB clusters from different tracks. The non-linearity is more evident for large fragments than for small. The DNAbreak results furnish an example of the RLC (randomly located clusters) analytic formalism, which generalizes the broken-stick fragment-size distribution of the random-breakage model that is often applied to low-LET data.

Mechanisms contributing to cluster formation in the inferior olivary nucleus in brainstem slices from postnatal mice

PubMed Central

Kølvraa, Mathias; Müller, Felix C; Jahnsen, Henrik; Rekling, Jens C

2014-01-01

Abstract The inferior olivary nucleus (IO) in in vitro slices from postnatal mice (P5.5–P15.5) spontaneously generates clusters of neurons with synchronous calcium transients, and intracellular recordings from IO neurons suggest that electrical coupling between neighbouring IO neurons may serve as a synchronizing mechanism. Here, we studied the cluster-forming mechanism and find that clusters overlap extensively with an overlap distribution that resembles the distribution for a random overlap model. The average somatodendritic field size of single curly IO neurons was ∼6400 μm2, which is slightly smaller than the average IO cluster size. Eighty-seven neurons with overlapping dendrites were estimated to be contained in the principal olive mean cluster size, and about six non-overlapping curly IO neurons could be contained within the largest clusters. Clusters could also be induced by iontophoresis with glutamate. Induced clusters were inhibited by tetrodotoxin, carbenoxelone and 18β-glycyrrhetinic acid, suggesting that sodium action potentials and electrical coupling are involved in glutamate-induced cluster formation, which could also be induced by activation of N-methyl-d-aspartate and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors. Spikelets and a small transient depolarizing response were observed during glutamate-induced cluster formation. Calcium transients spread with decreasing velocity during cluster formation, and somatic action potentials and cluster formation are accompanied by large dendritic calcium transients. In conclusion, cluster formation depends on gap junctions, sodium action potentials and spontaneous clusters occur randomly throughout the IO. The relative slow signal spread during cluster formation, combined with a strong dendritic influx of calcium, may signify that active dendritic properties contribute to cluster formation. PMID:24042500

Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation.

PubMed

Saatchi, Mahdi; McClure, Mathew C; McKay, Stephanie D; Rolf, Megan M; Kim, JaeWoo; Decker, Jared E; Taxis, Tasia M; Chapple, Richard H; Ramey, Holly R; Northcutt, Sally L; Bauck, Stewart; Woodward, Brent; Dekkers, Jack C M; Fernando, Rohan L; Schnabel, Robert D; Garrick, Dorian J; Taylor, Jeremy F

2011-11-28

Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction. Deregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values. Accuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied. These results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy.

Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation

PubMed Central

2011-01-01

Background Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction. Methods Deregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values. Results Accuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied. Conclusions These results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy. PMID:22122853

On the Coupling Time of the Heat-Bath Process for the Fortuin-Kasteleyn Random-Cluster Model

NASA Astrophysics Data System (ADS)

Collevecchio, Andrea; Elçi, Eren Metin; Garoni, Timothy M.; Weigel, Martin

2018-01-01

We consider the coupling from the past implementation of the random-cluster heat-bath process, and study its random running time, or coupling time. We focus on hypercubic lattices embedded on tori, in dimensions one to three, with cluster fugacity at least one. We make a number of conjectures regarding the asymptotic behaviour of the coupling time, motivated by rigorous results in one dimension and Monte Carlo simulations in dimensions two and three. Amongst our findings, we observe that, for generic parameter values, the distribution of the appropriately standardized coupling time converges to a Gumbel distribution, and that the standard deviation of the coupling time is asymptotic to an explicit universal constant multiple of the relaxation time. Perhaps surprisingly, we observe these results to hold both off criticality, where the coupling time closely mimics the coupon collector's problem, and also at the critical point, provided the cluster fugacity is below the value at which the transition becomes discontinuous. Finally, we consider analogous questions for the single-spin Ising heat-bath process.

Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset

PubMed Central

Guo, Hao; Liu, Lei; Chen, Junjie; Xu, Yong; Jie, Xiang

2017-01-01

Functional magnetic resonance imaging (fMRI) is one of the most useful methods to generate functional connectivity networks of the brain. However, conventional network generation methods ignore dynamic changes of functional connectivity between brain regions. Previous studies proposed constructing high-order functional connectivity networks that consider the time-varying characteristics of functional connectivity, and a clustering method was performed to decrease computational cost. However, random selection of the initial clustering centers and the number of clusters negatively affected classification accuracy, and the network lost neurological interpretability. Here we propose a novel method that introduces the minimum spanning tree method to high-order functional connectivity networks. As an unbiased method, the minimum spanning tree simplifies high-order network structure while preserving its core framework. The dynamic characteristics of time series are not lost with this approach, and the neurological interpretation of the network is guaranteed. Simultaneously, we propose a multi-parameter optimization framework that involves extracting discriminative features from the minimum spanning tree high-order functional connectivity networks. Compared with the conventional methods, our resting-state fMRI classification method based on minimum spanning tree high-order functional connectivity networks greatly improved the diagnostic accuracy for Alzheimer's disease. PMID:29249926

[Automatic Sleep Stage Classification Based on an Improved K-means Clustering Algorithm].

PubMed

Xiao, Shuyuan; Wang, Bei; Zhang, Jian; Zhang, Qunfeng; Zou, Junzhong

2016-10-01

Sleep stage scoring is a hotspot in the field of medicine and neuroscience.Visual inspection of sleep is laborious and the results may be subjective to different clinicians.Automatic sleep stage classification algorithm can be used to reduce the manual workload.However,there are still limitations when it encounters complicated and changeable clinical cases.The purpose of this paper is to develop an automatic sleep staging algorithm based on the characteristics of actual sleep data.In the proposed improved K-means clustering algorithm,points were selected as the initial centers by using a concept of density to avoid the randomness of the original K-means algorithm.Meanwhile,the cluster centers were updated according to the‘Three-Sigma Rule’during the iteration to abate the influence of the outliers.The proposed method was tested and analyzed on the overnight sleep data of the healthy persons and patients with sleep disorders after continuous positive airway pressure(CPAP)treatment.The automatic sleep stage classification results were compared with the visual inspection by qualified clinicians and the averaged accuracy reached 76%.With the analysis of morphological diversity of sleep data,it was proved that the proposed improved K-means algorithm was feasible and valid for clinical practice.

Environmental, health and economic conditions perceived by 50 rural communities in Bangladesh.

PubMed

Ohtsuka, Ryutaro; Inaoka, Tsukasa; Moji, Kazuhiko; Karim, Enamul; Yoshinaga, Mari

2002-12-01

For randomly selected 50 villages in Bangladesh, an interview survey with a structured questionnaire was conducted to reveal their perception on the environmental, health and economic conditions at present and for the past 10-year change. The eight following items were analyzed in this paper: air pollution and water pollution, which represent environmental conditions with close relation to health conditions, soil degradation and deforestation, which represent environmental conditions with close relation to economic conditions, epidemic diseases and malnutrition, which represent health conditions, and poverty and jobless, which represent economic conditions. Among the 50 villages, deforestation was most frequently perceived serious at present and worsened in the past 10 years. Of the remaining seven items, those related to economic conditions were more seriously perceived than those related to health and environmental conditions. As revealed by the cluster analysis for the inter-item relations, epidemic diseases, which formed the same cluster with the environmental items, were recognized less serious whereas malnutrition, which formed the same cluster with the economic items, was recognized more serious. These findings are useful not only for rural development programs but also for mitigation programs toward health and environmental hazards in Bangladesh.

Methods of developing core collections based on the predicted genotypic value of rice ( Oryza sativa L.).

PubMed

Li, C T; Shi, C H; Wu, J G; Xu, H M; Zhang, H Z; Ren, Y L

2004-04-01

The selection of an appropriate sampling strategy and a clustering method is important in the construction of core collections based on predicted genotypic values in order to retain the greatest degree of genetic diversity of the initial collection. In this study, methods of developing rice core collections were evaluated based on the predicted genotypic values for 992 rice varieties with 13 quantitative traits. The genotypic values of the traits were predicted by the adjusted unbiased prediction (AUP) method. Based on the predicted genotypic values, Mahalanobis distances were calculated and employed to measure the genetic similarities among the rice varieties. Six hierarchical clustering methods, including the single linkage, median linkage, centroid, unweighted pair-group average, weighted pair-group average and flexible-beta methods, were combined with random, preferred and deviation sampling to develop 18 core collections of rice germplasm. The results show that the deviation sampling strategy in combination with the unweighted pair-group average method of hierarchical clustering retains the greatest degree of genetic diversities of the initial collection. The core collections sampled using predicted genotypic values had more genetic diversity than those based on phenotypic values.

Self-organisation after embryonic kidney dissociation is driven via selective adhesion of ureteric epithelial cells.

PubMed

Lefevre, James G; Chiu, Han S; Combes, Alexander N; Vanslambrouck, Jessica M; Ju, Ali; Hamilton, Nicholas A; Little, Melissa H

2017-03-15

Human pluripotent stem cells, after directed differentiation in vitro , can spontaneously generate complex tissues via self-organisation of the component cells. Self-organisation can also reform embryonic organ structure after tissue disruption. It has previously been demonstrated that dissociated embryonic kidneys can recreate component epithelial and mesenchymal relationships sufficient to allow continued kidney morphogenesis. Here, we investigate the timing and underlying mechanisms driving self-organisation after dissociation of the embryonic kidney using time-lapse imaging, high-resolution confocal analyses and mathematical modelling. Organotypic self-organisation sufficient for nephron initiation was observed within a 24 h period. This involved cell movement, with structure emerging after the clustering of ureteric epithelial cells, a process consistent with models of random cell movement with preferential cell adhesion. Ureteric epithelialisation rapidly followed the formation of ureteric cell clusters with the reformation of nephron-forming niches representing a later event. Disruption of P-cadherin interactions was seen to impair this ureteric epithelial cell clustering without affecting epithelial maturation. This understanding could facilitate improved regulation of patterning within organoids and facilitate kidney engineering approaches guided by cell-cell self-organisation. © 2017. Published by The Company of Biologists Ltd.

Psychoanalytic-Interactional Therapy versus Psychodynamic Therapy by Experts for Personality Disorders: A Randomized Controlled Efficacy-Effectiveness Study in Cluster B Personality Disorders.

PubMed

Leichsenring, Falk; Masuhr, Oliver; Jaeger, Ulrich; Rabung, Sven; Dally, Andreas; Dümpelmann, Michael; Fricke-Neef, Christian; Steinert, Christiane; Streeck, Ulrich

2016-01-01

With regard to cluster B personality disorders, most psychotherapeutic treatments focus on borderline personality disorder. Evidence-based treatments for patients with other cluster B personality disorders are not yet available. Psychoanalytic-interactional therapy (PIT) represents a transdiagnostic treatment for severe personality disorders. PIT has been applied in clinical practice for many years and has proven effective in open studies. In a randomized controlled trial, we compared manual-guided PIT to nonmanualized pychodynamic therapy by experts in personality disorders (E-PDT) in patients with cluster B personality disorders. In an inpatient setting, patients with cluster B personality disorders were randomly assigned to manual-guided PIT (n = 64) or nonmanualized E-PDT (n = 58). In addition, a quasi-experimental control condition was used (n = 46) including both patients receiving treatment as usual and patients waiting for treatment. Primary outcomes were level of personality organization and overall psychological distress. As secondary outcomes, depression, anxiety and interpersonal problems were examined. No significant improvements were found in the control patients. Both PIT and E-PDT achieved significant improvements in all outcome measures and were superior to the control condition. No differences were found between PIT and E-PDT in any outcome measure at the end of treatment. The type of cluster B personality disorder had no impact on the results. In an inpatient setting, both PIT and E-PDT proved to be superior to a control condition in cluster B personality disorders. In a head-to-head comparison, both treatments appeared to be equally effective. Further research on the treatment of cluster B personality disorders is required. © 2016 S. Karger AG, Basel.

Specialist mental health consultation for depression in Australian aged care residents with dementia: a cluster randomized trial.

PubMed

McSweeney, Kate; Jeffreys, Aimee; Griffith, Joanne; Plakiotis, Chris; Kharsas, Renee; O'Connor, Daniel W

2012-11-01

This cluster randomized controlled trial sought to determine whether multidisciplinary specialist mental health consultation was more effective than care as usual in treating the depression of aged care residents with dementia. Three hundred and eighty nine aged care residents were screened for dementia and major depression. Forty four were ultimately included in the intervention sample, selected from 20 aged care facilities located in Melbourne, Australia. Facilities were randomly allocated to an intervention condition involving the provision of multidisciplinary specialist consultation regarding the best-practice management of depression in dementia, or to a care as usual condition. Consultations involved individually tailored medical and psychosocial recommendations provided to care staff and general practitioners. All residents participated in a comprehensive pre-intervention diagnostic assessment, including the administration of the Cornell Scale for Depression in Dementia. This assessment was repeated approximately 15 weeks post-intervention by a rater blind to study condition. Multidisciplinary specialist mental health consultation was significantly more effective than care as usual in treating the clinical depression of aged care residents with dementia (p < 0.05, partial η(2)  = 0.16). At follow-up, the mean Cornell Scale for Depression in Dementia score for the intervention group was 9.47, compared with 14.23 for the control group. In addition, 77% of the intervention group no longer met criteria for major depression. The results of this study suggest that the psychosocial and medical management of depressed aged care residents can be improved by increasing access to specialist mental health consultation. Copyright © 2012 John Wiley & Sons, Ltd.

Impact of Implementing Antibiotic Stewardship Programs in 15 Small Hospitals: A Cluster-Randomized Intervention.

PubMed

Stenehjem, Edward; Hersh, Adam L; Buckel, Whitney R; Jones, Peter; Sheng, Xiaoming; Evans, R Scott; Burke, John P; Lopansri, Bert K; Srivastava, Rajendu; Greene, Tom; Pavia, Andrew T

2018-02-23

Studies on the implementation of antibiotic stewardship programs (ASPs) in small hospitals are limited. Accreditation organizations now require all hospitals to have ASPs. The objective of this cluster-randomized intervention was to assess the effectiveness of implementing ASPs in Intermountain Healthcare's 15 small hospitals. Each hospital was randomized to 1 of 3 ASPs of escalating intensity. Program 1 hospitals were provided basic antibiotic stewardship education and tools, access to an infectious disease hotline, and antibiotic utilization data. Program 2 hospitals received those interventions plus advanced education, audit and feedback for select antibiotics, and locally controlled antibiotic restrictions. Program 3 hospitals received program 2 interventions plus audit and feedback on the majority of antibiotics, and an infectious diseases-trained clinician approved restricted antibiotics and reviewed microbiology results. Changes in total and broad-spectrum antibiotic use within programs (intervention versus baseline) and the difference between programs in the magnitude of change in antibiotic use (eg, program 3 vs 1) were evaluated with mixed models. Program 3 hospitals showed reductions in total (rate ratio, 0.89; confidence interval, .80-.99) and broad-spectrum (0.76; .63-.91) antibiotic use when the intervention period was compared with the baseline period. Program 1 and 2 hospitals did not experience a reduction in antibiotic use. Comparison of the magnitude of effects between programs showed a similar trend favoring program 3, but this was not statistically significant. Only the most intensive ASP intervention was associated with reduction in total and broad-spectrum antibiotic use when compared with baseline. NCT03245879.

Outcomes and moderators of a preventive school-based mental health intervention for children affected by war in Sri Lanka: a cluster randomized trial

PubMed Central

TOL, WIETSE A.; KOMPROE, IVAN H.; JORDANS, MARK J.D.; VALLIPURAM, ANAVARATHAN; SIPSMA, HEATHER; SIVAYOKAN, SAMBASIVAMOORTHY; MACY, ROBERT D.; DE JONG, JOOP T.

2012-01-01

We aimed to examine outcomes, moderators and mediators of a preventive school-based mental health intervention implemented by paraprofessionals in a war-affected setting in northern Sri Lanka. A cluster randomized trial was employed. Subsequent to screening 1,370 children in randomly selected schools, 399 children were assigned to an intervention (n=199) or waitlist control condition (n=200). The intervention consisted of 15 manualized sessions over 5 weeks of cognitive behavioral techniques and creative expressive elements. Assessments took place before, 1 week after, and 3 months after the intervention. Primary outcomes included post-traumatic stress disorder (PTSD), depressive, and anxiety symptoms. No main effects on primary outcomes were identified. A main effect in favor of intervention for conduct problems was observed. This effect was stronger for younger children. Furthermore, we found intervention benefits for specific subgroups. Stronger effects were found for boys with regard to PTSD and anxiety symptoms, and for younger children on pro-social behavior. Moreover, we found stronger intervention effects on PTSD, anxiety, and function impairment for children experiencing lower levels of current war-related stressors. Girls in the intervention condition showed smaller reductions on PTSD symptoms than waitlisted girls. We conclude that preventive school-based psychosocial interventions in volatile areas characterized by ongoing war-related stressors may effectively improve indicators of psychological wellbeing and posttraumatic stress-related symptoms in some children. However, they may undermine natural recovery for others. Further research is necessary to examine how gender, age and current war-related experiences contribute to differential intervention effects. PMID:22654944

Do X-ray dark or underluminous galaxy clusters exist?

NASA Astrophysics Data System (ADS)

Andreon, S.; Moretti, A.

2011-12-01

We study the X-ray properties of a color-selected sample of clusters at 0.1 < z < 0.3, to quantify the real aboundance of the population of X-ray dark or underluminous clusters and at the same time the spurious detection contamination level of color-selected cluster catalogs. Starting from a local sample of color-selected clusters, we restrict our attention to those with sufficiently deep X-ray observations to probe their X-ray luminosity down to very faint values and without introducing any X-ray bias. This allowed us to have an X-ray- unbiased sample of 33 clusters to measure the LX-richness relation. Swift 1.4 Ms X-ray observations show that at least 89% of the color-detected clusters are real objects with a potential well deep enough to heat and retain an intracluster medium. The percentage rises to 94% when one includes the single spectroscopically confirmed color-selected cluster whose X-ray emission is not secured. Looking at our results from the opposite perspective, the percentage of X-ray dark clusters among color-selected clusters is very low: at most about 11 per cent (at 90% confidence). Supplementing our data with those from literature, we conclude that X-ray- and color- cluster surveys sample the same population and consequently that in this regard we can safely use clusters selected with any of the two methods for cosmological purposes. This is an essential and promising piece of information for upcoming surveys in both the optical/IR (DES, EUCLID) and X-ray (eRosita). Richness correlates with X-ray luminosity with a large scatter, 0.51 ± 0.08 (0.44 ± 0.07) dex in lgLX at a given richness, when Lx is measured in a 500 (1070) kpc aperture. We release data and software to estimate the X-ray flux, or its upper limit, of a source with over-Poisson background fluctuations (found in this work to be ~20% on cluster angular scales) and to fit X-ray luminosity vs richness if there is an intrinsic scatter. These Bayesian applications rigorously account for boundaries (e.g., the X-ray luminosity and the richness cannot be negative).

Cascades on a class of clustered random networks

NASA Astrophysics Data System (ADS)

Hackett, Adam; Melnik, Sergey; Gleeson, James P.

2011-05-01

We present an analytical approach to determining the expected cascade size in a broad range of dynamical models on the class of random networks with arbitrary degree distribution and nonzero clustering introduced previously in [M. E. J. Newman, Phys. Rev. Lett. PRLTAO0031-900710.1103/PhysRevLett.103.058701103, 058701 (2009)]. A condition for the existence of global cascades is derived as well as a general criterion that determines whether increasing the level of clustering will increase, or decrease, the expected cascade size. Applications, examples of which are provided, include site percolation, bond percolation, and Watts’ threshold model; in all cases analytical results give excellent agreement with numerical simulations.

Seizure clustering.

PubMed

Haut, Sheryl R

2006-02-01

Seizure clusters, also known as repetitive or serial seizures, occur commonly in epilepsy. Clustering implies that the occurrence of one seizure may influence the probability of a subsequent seizure; thus, the investigation of the clustering phenomenon yields insights into both specific mechanisms of seizure clustering and more general concepts of seizure occurrence. Seizure clustering has been defined clinically as a number of seizures per unit time and, statistically, as a deviation from a random distribution, or interseizure interval dependence. This review explores the pathophysiology, epidemiology, and clinical implications of clustering, as well as other periodic patterns of seizure occurrence. Risk factors for experiencing clusters and potential precipitants of clustering are also addressed.

Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications

PubMed Central

Qian, Guoqi; Wu, Yuehua; Ferrari, Davide; Qiao, Puxue; Hollande, Frédéric

2016-01-01

Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method. PMID:27212939

Morphology of size-selected Ptn clusters on CeO2(111)

NASA Astrophysics Data System (ADS)

Shahed, Syed Mohammad Fakruddin; Beniya, Atsushi; Hirata, Hirohito; Watanabe, Yoshihide

2018-03-01

Supported Pt catalysts and ceria are well known for their application in automotive exhaust catalysts. Size-selected Pt clusters supported on a CeO2(111) surface exhibit distinct physical and chemical properties. We investigated the morphology of the size-selected Ptn (n = 5-13) clusters on a CeO2(111) surface using scanning tunneling microscopy at room temperature. Ptn clusters prefer a two-dimensional morphology for n = 5 and a three-dimensional (3D) morphology for n ≥ 6. We further observed the preference for a 3D tri-layer structure when n ≥ 10. For each cluster size, we quantitatively estimated the relative fraction of the clusters for each type of morphology. Size-dependent morphology of the Ptn clusters on the CeO2(111) surface was attributed to the Pt-Pt interaction in the cluster and the Pt-O interaction between the cluster and CeO2(111) surface. The results obtained herein provide a clear understanding of the size-dependent morphology of the Ptn clusters on a CeO2(111) surface.

Morphology of size-selected Ptn clusters on CeO2(111).

PubMed

Shahed, Syed Mohammad Fakruddin; Beniya, Atsushi; Hirata, Hirohito; Watanabe, Yoshihide

2018-03-21

Supported Pt catalysts and ceria are well known for their application in automotive exhaust catalysts. Size-selected Pt clusters supported on a CeO 2 (111) surface exhibit distinct physical and chemical properties. We investigated the morphology of the size-selected Pt n (n = 5-13) clusters on a CeO 2 (111) surface using scanning tunneling microscopy at room temperature. Pt n clusters prefer a two-dimensional morphology for n = 5 and a three-dimensional (3D) morphology for n ≥ 6. We further observed the preference for a 3D tri-layer structure when n ≥ 10. For each cluster size, we quantitatively estimated the relative fraction of the clusters for each type of morphology. Size-dependent morphology of the Pt n clusters on the CeO 2 (111) surface was attributed to the Pt-Pt interaction in the cluster and the Pt-O interaction between the cluster and CeO 2 (111) surface. The results obtained herein provide a clear understanding of the size-dependent morphology of the Pt n clusters on a CeO 2 (111) surface.

Source selection for cluster weak lensing measurements in the Hyper Suprime-Cam survey

NASA Astrophysics Data System (ADS)

Medezinski, Elinor; Oguri, Masamune; Nishizawa, Atsushi J.; Speagle, Joshua S.; Miyatake, Hironao; Umetsu, Keiichi; Leauthaud, Alexie; Murata, Ryoma; Mandelbaum, Rachel; Sifón, Cristóbal; Strauss, Michael A.; Huang, Song; Simet, Melanie; Okabe, Nobuhiro; Tanaka, Masayuki; Komiyama, Yutaka

2018-03-01

We present optimized source galaxy selection schemes for measuring cluster weak lensing (WL) mass profiles unaffected by cluster member dilution from the Subaru Hyper Suprime-Cam Strategic Survey Program (HSC-SSP). The ongoing HSC-SSP survey will uncover thousands of galaxy clusters to z ≲ 1.5. In deriving cluster masses via WL, a critical source of systematics is contamination and dilution of the lensing signal by cluster members, and by foreground galaxies whose photometric redshifts are biased. Using the first-year CAMIRA catalog of ˜900 clusters with richness larger than 20 found in ˜140 deg2 of HSC-SSP data, we devise and compare several source selection methods, including selection in color-color space (CC-cut), and selection of robust photometric redshifts by applying constraints on their cumulative probability distribution function (P-cut). We examine the dependence of the contamination on the chosen limits adopted for each method. Using the proper limits, these methods give mass profiles with minimal dilution in agreement with one another. We find that not adopting either the CC-cut or P-cut methods results in an underestimation of the total cluster mass (13% ± 4%) and the concentration of the profile (24% ± 11%). The level of cluster contamination can reach as high as ˜10% at R ≈ 0.24 Mpc/h for low-z clusters without cuts, while employing either the P-cut or CC-cut results in cluster contamination consistent with zero to within the 0.5% uncertainties. Our robust methods yield a ˜60 σ detection of the stacked CAMIRA surface mass density profile, with a mean mass of M200c = [1.67 ± 0.05(stat)] × 1014 M⊙/h.

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

PubMed

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 different components involved in the evaluation process.

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

PubMed Central

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 different components involved in the evaluation process. PMID:28719616

A WISE Selection of MIR AGN in Different Environments

NASA Astrophysics Data System (ADS)

Cheeseboro, Belinda D.; Norman, Dara J.

2015-01-01

This study was undertaken to understand the role of large scale environment in the evolution of MIR-selected AGN. In this study we examine AGN candidates in two types of environments: 7 clusters and 6 blank fields. Two types of clusters were studied in this project: 3 virialized and 4 non-virialized. The redshift of the clusters ranged 0.22≤z≤0.28. We used the mid-infrared WISE All-Sky database to identify AGN, applying various methods to refine our AGN candidate selection. To ascertain if there is an excess or deficit of MIR AGN in galaxy clusters vs. blank fields, we compared the AGN candidate distributions in virialized vs. non-virialized clusters to the blank fields. After close examination and comparison of the results to X-ray selected AGN from the Gilmour et al. (2009) study, we concluded that we do not detect an excess or deficit of MIR AGN in our clusters whether the cluster was virialized or non-virialized. This contrasted the conclusion of the Gilmour et al. (2009) study where there was an excess of X-Ray selected AGN in clusters.We also note an interesting feature in our WISE color-color plots that might be used for further investigation.Cheeseboro was supported by the NOAO/KPNO ResearchExperiences for Undergraduates (REU) Program which is funded by theNational Science Foundation Research Experiences for UndergraduatesProgram (AST-1262829).

A comparison of confidence interval methods for the intraclass correlation coefficient in community-based cluster randomization trials with a binary outcome.

PubMed

Braschel, Melissa C; Svec, Ivana; Darlington, Gerarda A; Donner, Allan

2016-04-01

Many investigators rely on previously published point estimates of the intraclass correlation coefficient rather than on their associated confidence intervals to determine the required size of a newly planned cluster randomized trial. Although confidence interval methods for the intraclass correlation coefficient that can be applied to community-based trials have been developed for a continuous outcome variable, fewer methods exist for a binary outcome variable. The aim of this study is to evaluate confidence interval methods for the intraclass correlation coefficient applied to binary outcomes in community intervention trials enrolling a small number of large clusters. Existing methods for confidence interval construction are examined and compared to a new ad hoc approach based on dividing clusters into a large number of smaller sub-clusters and subsequently applying existing methods to the resulting data. Monte Carlo simulation is used to assess the width and coverage of confidence intervals for the intraclass correlation coefficient based on Smith's large sample approximation of the standard error of the one-way analysis of variance estimator, an inverted modified Wald test for the Fleiss-Cuzick estimator, and intervals constructed using a bootstrap-t applied to a variance-stabilizing transformation of the intraclass correlation coefficient estimate. In addition, a new approach is applied in which clusters are randomly divided into a large number of smaller sub-clusters with the same methods applied to these data (with the exception of the bootstrap-t interval, which assumes large cluster sizes). These methods are also applied to a cluster randomized trial on adolescent tobacco use for illustration. When applied to a binary outcome variable in a small number of large clusters, existing confidence interval methods for the intraclass correlation coefficient provide poor coverage. However, confidence intervals constructed using the new approach combined with Smith's method provide nominal or close to nominal coverage when the intraclass correlation coefficient is small (<0.05), as is the case in most community intervention trials. This study concludes that when a binary outcome variable is measured in a small number of large clusters, confidence intervals for the intraclass correlation coefficient may be constructed by dividing existing clusters into sub-clusters (e.g. groups of 5) and using Smith's method. The resulting confidence intervals provide nominal or close to nominal coverage across a wide range of parameters when the intraclass correlation coefficient is small (<0.05). Application of this method should provide investigators with a better understanding of the uncertainty associated with a point estimator of the intraclass correlation coefficient used for determining the sample size needed for a newly designed community-based trial. © The Author(s) 2015.

Effects of a Physical Education-Based Programme on Health-Related Physical Fitness and Its Maintenance in High School Students: A Cluster-Randomized Controlled Trial

ERIC Educational Resources Information Center

Mayorga-Vega, Daniel; Montoro-Escaño, Jorge; Merino-Marban, Rafael; Viciana, Jesús

2016-01-01

The purpose of this study was to examine the effects of a physical education-based development and maintenance programme on objective and perceived health-related physical fitness in high school students. A sample of 111 students aged 12-14 years old from six classes were cluster-randomly assigned to an experimental group (n = 54) or a control…

Visualizing Time-Varying Distribution Data in EOS Application

NASA Technical Reports Server (NTRS)

Shen, Han-Wei

2004-01-01

In this research, we have developed several novel visualization methods for spatial probability density function data. Our focus has been on 2D spatial datasets, where each pixel is a random variable, and has multiple samples which are the results of experiments on that random variable. We developed novel clustering algorithms as a means to reduce the information contained in these datasets; and investigated different ways of interpreting and clustering the data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sills, Alison; Glebbeek, Evert; Chatterjee, Sourav

We created artificial color-magnitude diagrams of Monte Carlo dynamical models of globular clusters and then used observational methods to determine the number of blue stragglers in those clusters. We compared these blue stragglers to various cluster properties, mimicking work that has been done for blue stragglers in Milky Way globular clusters to determine the dominant formation mechanism(s) of this unusual stellar population. We find that a mass-based prescription for selecting blue stragglers will select approximately twice as many blue stragglers than a selection criterion that was developed for observations of real clusters. However, the two numbers of blue stragglers aremore » well-correlated, so either selection criterion can be used to characterize the blue straggler population of a cluster. We confirm previous results that the simplified prescription for the evolution of a collision or merger product in the BSE code overestimates their lifetimes. We show that our model blue stragglers follow similar trends with cluster properties (core mass, binary fraction, total mass, collision rate) as the true Milky Way blue stragglers as long as we restrict ourselves to model clusters with an initial binary fraction higher than 5%. We also show that, in contrast to earlier work, the number of blue stragglers in the cluster core does have a weak dependence on the collisional parameter Γ in both our models and in Milky Way globular clusters.« less

Phytomonas: analysis of polymorphism and genetic relatedness between isolates from plants and phytophagous insects from different geographic regions by RAPD fingerprints and synapomorphic markers.

PubMed

Serrano, M G; Camargo, E P; Teixeira, M M

1999-01-01

The random amplification of polymorphic DNA was used for easy, quick and sensitive assessment of genetic polymorphism within Phytomonas to discriminate isolates and determine genetic relationships within the genus. We examined 48 Phytomonas spp., 31 isolates from plants and 17 from insects, from different geographic regions. Topology of the dendrogram based on randomly amplified polymorphic DNA fingerprints segregated the Phytomonas spp. into 5 main clusters, despite the high genetic variability within this genus. Similar clustering could also be obtained by both visual and cross-hybridization analysis of randomly amplified synapomorphic DNA fragments. There was some concordance between the genetic relationship of isolates and their plant tissue tropism. Moreover, Phytomonas spp. from plants and insects were grouped according to geographic origin, thus revealing a complex structure of this taxon comprising several clusters of very closely related organisms.

Outcomes of a pilot hand hygiene randomized cluster trial to reduce communicable infections among US office-based employees.

PubMed

Stedman-Smith, Maggie; DuBois, Cathy L Z; Grey, Scott F; Kingsbury, Diana M; Shakya, Sunita; Scofield, Jennifer; Slenkovich, Ken

2015-04-01

To determine the effectiveness of an office-based multimodal hand hygiene improvement intervention in reducing self-reported communicable infections and work-related absence. A randomized cluster trial including an electronic training video, hand sanitizer, and educational posters (n = 131, intervention; n = 193, control). Primary outcomes include (1) self-reported acute respiratory infections (ARIs)/influenza-like illness (ILI) and/or gastrointestinal (GI) infections during the prior 30 days; and (2) related lost work days. Incidence rate ratios calculated using generalized linear mixed models with a Poisson distribution, adjusted for confounders and random cluster effects. A 31% relative reduction in self-reported combined ARI-ILI/GI infections (incidence rate ratio: 0.69; 95% confidence interval, 0.49 to 0.98). A 21% nonsignificant relative reduction in lost work days. An office-based multimodal hand hygiene improvement intervention demonstrated a substantive reduction in self-reported combined ARI-ILI/GI infections.

X-Ray Temperatures, Luminosities, and Masses from XMM-Newton Follow-upof the First Shear-selected Galaxy Cluster Sample

NASA Astrophysics Data System (ADS)

Deshpande, Amruta J.; Hughes, John P.; Wittman, David

2017-04-01

We continue the study of the first sample of shear-selected clusters from the initial 8.6 square degrees of the Deep Lens Survey (DLS); a sample with well-defined selection criteria corresponding to the highest ranked shear peaks in the survey area. We aim to characterize the weak lensing selection by examining the sample’s X-ray properties. There are multiple X-ray clusters associated with nearly all the shear peaks: 14 X-ray clusters corresponding to seven DLS shear peaks. An additional three X-ray clusters cannot be definitively associated with shear peaks, mainly due to large positional offsets between the X-ray centroid and the shear peak. Here we report on the XMM-Newton properties of the 17 X-ray clusters. The X-ray clusters display a wide range of luminosities and temperatures; the L X -T X relation we determine for the shear-associated X-ray clusters is consistent with X-ray cluster samples selected without regard to dynamical state, while it is inconsistent with self-similarity. For a subset of the sample, we measure X-ray masses using temperature as a proxy, and compare to weak lensing masses determined by the DLS team. The resulting mass comparison is consistent with equality. The X-ray and weak lensing masses show considerable intrinsic scatter (˜48%), which is consistent with X-ray selected samples when their X-ray and weak lensing masses are independently determined. Some of the data presented herein were obtained at the W.M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California, and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation.

Evaluation of a demand-creation intervention for couples' HIV testing services among married or cohabiting individuals in Rakai, Uganda: a cluster-randomized intervention trial.

PubMed

Matovu, Joseph K B; Todd, Jim; Wanyenze, Rhoda K; Kairania, Robert; Serwadda, David; Wabwire-Mangen, Fred

2016-08-08

Uptake of couples' HIV counseling and testing (couples' HCT) services remains largely low in most settings. We report the effect of a demand-creation intervention trial on couples' HCT uptake among married or cohabiting individuals who had never received couples' HCT. This was a cluster-randomized intervention trial implemented in three study regions with differing HIV prevalence levels (range: 9-43 %) in Rakai district, southwestern Uganda, between February and September 2014. We randomly assigned six clusters (1:1) to receive the intervention or serve as the comparison arm using computer-generated random numbers. In the intervention clusters, individuals attended small group, couple and male-focused interactive sessions, reinforced with testimonies from 'expert couples', and received invitation coupons to test together with their partners at designated health facilities. In the comparison clusters, participants attended general adult health education sessions but received no invitation coupons. The primary outcome was couples' HCT uptake, measured 12 months post-baseline. Baseline data were collected between November 2013 and February 2014 while follow-up data were collected between March and April 2015. We conducted intention-to-treat analysis using a mixed effects Poisson regression model to assess for differences in couples' HCT uptake between the intervention and comparison clusters. Data analysis was conducted using STATA statistical software, version 14.1. Of 2135 married or cohabiting individuals interviewed at baseline, 42 % (n = 846) had ever received couples' HCT. Of those who had never received couples' HCT (n = 1,174), 697 were interviewed in the intervention clusters while 477 were interviewed in the comparison clusters. 73.6 % (n = 513) of those interviewed in the intervention and 82.6 % (n = 394) of those interviewed in the comparison cluster were interviewed at follow-up. Of those interviewed, 72.3 % (n = 371) in the intervention and 65.2 % (n = 257) in the comparison clusters received HCT. Couples' HCT uptake was higher in the intervention than in the comparison clusters (20.3 % versus 13.7 %; adjusted prevalence ratio (aPR) = 1.43, 95 % CI: 1.02, 2.01, P = 0.04). Our findings show that a small group, couple and male-focused, demand-creation intervention reinforced with testimonies from 'expert couples', improved uptake of couples' HCT in this rural setting. ClinicalTrials.gov, NCT02492061 . Date of registration: June 14, 2015.

A prospective cohort and extended comprehensive-cohort design provided insights about the generalizability of a pragmatic trial: the ProtecT prostate cancer trial.

PubMed

Donovan, Jenny L; Young, Grace J; Walsh, Eleanor I; Metcalfe, Chris; Lane, J Athene; Martin, Richard M; Tazewell, Marta K; Davis, Michael; Peters, Tim J; Turner, Emma L; Mills, Nicola; Khazragui, Hanan; Khera, Tarnjit K; Neal, David E; Hamdy, Freddie C

2018-04-01

Randomized controlled trials (RCTs) deliver robust internally valid evidence but generalizability is often neglected. Design features built into the Prostate testing for cancer and Treatment (ProtecT) RCT of treatments for localized prostate cancer (PCa) provided insights into its generalizability. Population-based cluster randomization created a prospective study of prostate-specific antigen (PSA) testing and a comprehensive-cohort study including groups choosing treatment or excluded from the RCT, as well as those randomized. Baseline information assessed selection and response during RCT conduct. The prospective study (82,430 PSA-tested men) represented healthy men likely to respond to a screening invitation. The extended comprehensive cohort comprised 1,643 randomized, 997 choosing treatment, and 557 excluded with advanced cancer/comorbidities. Men choosing treatment were very similar to randomized men except for having more professional/managerial occupations. Excluded men were similar to the randomized socio-demographically but different clinically, representing less healthy men with more advanced PCa. The design features of the ProtecT RCT provided data to assess the representativeness of the prospective cohort and generalizability of the findings of the RCT. Greater attention to collecting data at the design stage of pragmatic trials would better support later judgments by clinicians/policy-makers about the generalizability of RCT findings in clinical practice. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

A cluster randomized trial of strategies to increase uptake amongst young women invited for their first cervical screen: The STRATEGIC trial.

PubMed

Kitchener, H; Gittins, M; Cruickshank, M; Moseley, C; Fletcher, S; Albrow, R; Gray, A; Brabin, L; Torgerson, D; Crosbie, E J; Sargent, A; Roberts, C

2018-06-01

Objectives To measure the feasibility and effectiveness of interventions to increase cervical screening uptake amongst young women. Methods A two-phase cluster randomized trial conducted in general practices in the NHS Cervical Screening Programme. In Phase 1, women in practices randomized to intervention due for their first invitation to cervical screening received a pre-invitation leaflet and, separately, access to online booking. In Phase 2, non-attenders at six months were randomized to one of: vaginal self-sample kits sent unrequested or offered; timed appointments; nurse navigator; or the choice between nurse navigator or self-sample kits. Primary outcome was uplift in intervention vs. control practices, at 3 and 12 months post invitation. Results Phase 1 randomized 20,879 women. Neither pre-invitation leaflet nor online booking increased screening uptake by three months (18.8% pre-invitation leaflet vs. 19.2% control and 17.8% online booking vs. 17.2% control). Uptake was higher amongst human papillomavirus vaccinees at three months (OR 2.07, 95% CI 1.69-2.53, p < 0.001). Phase 2 randomized 10,126 non-attenders, with 32-34 clusters for each intervention and 100 clusters as controls. Sending self-sample kits increased uptake at 12 months (OR 1.51, 95% CI 1.20-1.91, p = 0.001), as did timed appointments (OR 1.41, 95% CI 1.14-1.74, p = 0.001). The offer of a nurse navigator, a self-sample kits on request, and choice between timed appointments and nurse navigator were ineffective. Conclusions Amongst non-attenders, self-sample kits sent and timed appointments achieved an uplift in screening over the short term; longer term impact is less certain. Prior human papillomavirus vaccination was associated with increased screening uptake.

On the Clustering of Europa's Small Craters

NASA Technical Reports Server (NTRS)

Bierhaus, E. B.; Chapman, C. R.; Merline, W. J.

2001-01-01

We analyze the spatial distribution of Europa's small craters and find that many are too tightly clustered to result from random, primary impacts. Additional information is contained in the original extended abstract.

The additional benefit of residual spraying and insecticide-treated curtains for dengue control over current best practice in Cuba: Evaluation of disease incidence in a cluster randomized trial in a low burden setting with intensive routine control

PubMed Central

Rosales, Julio Popa; Mirabal, Mayelin; Cabrera, Pedro; Fonseca, Viviana; Gómez Padrón, Tania; Pérez Menzies, Mirtha; Montada, Domingo; Van der Stuyft, Patrick

2017-01-01

Background Aedes control interventions are considered the cornerstone of dengue control programmes, but there is scarce evidence on their effect on disease. We set-up a cluster randomized controlled trial in Santiago de Cuba to evaluate the entomological and epidemiological effectiveness of periodical intra- and peri-domiciliary residual insecticide (deltamethrin) treatment (RIT) and long lasting insecticide treated curtains (ITC). Methodology/Principal findings Sixty three clusters (around 250 households each) were randomly allocated to two intervention (RIT and ITC) and one control arm. Routine Aedes control activities (entomological surveillance, source reduction, selective adulticiding, health education) were applied in the whole study area. The outcome measures were clinical dengue case incidence and immature Aedes infestation. Effectiveness of tools was evaluated using a generalized linear regression model with a negative binomial link function. Despite significant reduction in Aedes indices (Rate Ratio (RR) 0.54 (95%CI 0.32–0.89) in the first month after RIT, the effect faded out over time and dengue incidence was not reduced. Overall, in this setting there was no protective effect of RIT or ITC over routine in the 17months intervention period, with for house index RR of 1.16 (95%CI 0.96–1.40) and 1.25 (95%CI 1.03–1.50) and for dengue incidence RR of 1.43 (95%CI 1.08–1.90) and 0.96 (95%CI 0.72–1.28) respectively. The monthly dengue incidence rate (IR) at cluster level was best explained by epidemic periods (Incidence Rate Ratio (IRR) 5.50 (95%CI 4.14–7.31)), the IR in bordering houseblocks (IRR 1.03 (95%CI 1.02–1.04)) and the IR pre-intervention (IRR 1.02 (95%CI 1.00–1.04)). Conclusions Adding RIT to an intensive routine Aedes control programme has a transient effect on the already moderate low entomological infestation levels, while ITC did not have any effect. For both interventions, we didn’t evidence impact on disease incidence. Further studies are needed to evaluate impact in settings with high Aedes infestation and arbovirus case load. PMID:29117180

The additional benefit of residual spraying and insecticide-treated curtains for dengue control over current best practice in Cuba: Evaluation of disease incidence in a cluster randomized trial in a low burden setting with intensive routine control.

PubMed

Toledo, Maria Eugenia; Vanlerberghe, Veerle; Rosales, Julio Popa; Mirabal, Mayelin; Cabrera, Pedro; Fonseca, Viviana; Gómez Padrón, Tania; Pérez Menzies, Mirtha; Montada, Domingo; Van der Stuyft, Patrick

2017-11-01

Aedes control interventions are considered the cornerstone of dengue control programmes, but there is scarce evidence on their effect on disease. We set-up a cluster randomized controlled trial in Santiago de Cuba to evaluate the entomological and epidemiological effectiveness of periodical intra- and peri-domiciliary residual insecticide (deltamethrin) treatment (RIT) and long lasting insecticide treated curtains (ITC). Sixty three clusters (around 250 households each) were randomly allocated to two intervention (RIT and ITC) and one control arm. Routine Aedes control activities (entomological surveillance, source reduction, selective adulticiding, health education) were applied in the whole study area. The outcome measures were clinical dengue case incidence and immature Aedes infestation. Effectiveness of tools was evaluated using a generalized linear regression model with a negative binomial link function. Despite significant reduction in Aedes indices (Rate Ratio (RR) 0.54 (95%CI 0.32-0.89) in the first month after RIT, the effect faded out over time and dengue incidence was not reduced. Overall, in this setting there was no protective effect of RIT or ITC over routine in the 17months intervention period, with for house index RR of 1.16 (95%CI 0.96-1.40) and 1.25 (95%CI 1.03-1.50) and for dengue incidence RR of 1.43 (95%CI 1.08-1.90) and 0.96 (95%CI 0.72-1.28) respectively. The monthly dengue incidence rate (IR) at cluster level was best explained by epidemic periods (Incidence Rate Ratio (IRR) 5.50 (95%CI 4.14-7.31)), the IR in bordering houseblocks (IRR 1.03 (95%CI 1.02-1.04)) and the IR pre-intervention (IRR 1.02 (95%CI 1.00-1.04)). Adding RIT to an intensive routine Aedes control programme has a transient effect on the already moderate low entomological infestation levels, while ITC did not have any effect. For both interventions, we didn't evidence impact on disease incidence. Further studies are needed to evaluate impact in settings with high Aedes infestation and arbovirus case load.

Childhood antecedents of adolescent personality disorders.

PubMed

Bernstein, D P; Cohen, P; Skodol, A; Bezirganian, S; Brook, J S

1996-07-01

The purpose of this study was to investigate the childhood antecedents of personality disorders that are diagnosed in adolescence. A randomly selected community sample of 641 youths was assessed initially in childhood and followed longitudinally over 10 years. Childhood behavior ratings were based on maternal report; diagnoses of adolescent personality disorders were based on data obtained from both maternal and youth informants. Four composite measures of childhood behavior problems were used: conduct problems, depressive symptoms, anxiety/fear, and immaturity. Adolescent personality disorders were considered present only if the disorders persisted over a 2-year period. For all analyses, personality disorders were grouped into the three clusters (A, B, and C) of DSM-III-R. Logistic regression analyses indicated that all four of the putative childhood antecedents were associated with greater odds of an adolescent personality disorder 10 years later. Childhood conduct problems remained an independent predictor of personality disorders in all three clusters, even when other childhood problems were included in the same regression model. Additionally, depressive symptoms emerged as an independent predictor of cluster A personality disorders in boys, while immaturity was an independent predictor of cluster B personality disorders in girls. No moderating effects of age at time of childhood assessment were found. These results support the view that personality disorders can be traced to childhood emotional and behavioral disturbances and suggest that these problems have both general and specific relationships to adolescent personality functioning.

Occupational risk factors for Wilms' tumor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bunin, G.; Kramer, S.; Nass, C.

A matched case-control study of Wilms' tumor investigated parental occupational risk factors. Cases diagnosed in 1970-1983 were identified through a population-based tumor registry and hospital registries in the Greater Philadelphia area. Controls were selected by random digit dialing and were matched to cases on race, birth date (+/- 3 years), and the area code and exchange of the case's telephone number at diagnosis. Parents of 100 matched pairs were interviewed by telephone. Parents of patients and controls were generally similar in demographic characteristics, except that mothers differed in religion. Published schemes were used to group jobs into clusters of similarmore » exposures and to determine exposures from industry and job title. Analyses were done for preconception, pregnancy, and postnatal time periods. More case than control fathers had jobs in a cluster that includes machinists and welders (odds ratios (ORs) = 4.0-5.7, p less than or equal to 0.04). Paternal exposures to lead, silver, tin, and iron (some exposures of this cluster) were associated with Wilms' tumor in some analyses, with moderate odds ratios (ORs = 1.5-3.4). In general, the highest odds ratios were found for the preconception period among the genetic (prezygotic) cases. No maternal job clusters or exposures gave significantly elevated odds ratios. These results support a previous finding that lead is a risk factor, but not radiation, hydrocarbon, or boron exposures.« less

Coverage Dependent Charge Reduction of Cationic Gold Clusters on Surfaces Prepared Using Soft Landing of Mass-selected Ions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Grant E.; Priest, Thomas A.; Laskin, Julia

2012-11-29

The ionic charge state of monodisperse cationic gold clusters on surfaces may be controlled by selecting the coverage of mass-selected ions soft landed onto a substrate. Polydisperse diphosphine-capped gold clusters were synthesized in solution by reduction of chloro(triphenylphosphine)gold(I) with borane tert-butylamine in the presence of 1,3-bis(diphenylphosphino)propane. The polydisperse gold clusters were introduced into the gas phase by electrospray ionization and mass selection was employed to select a multiply charged cationic cluster species (Au11L53+, m/z = 1409, L = 1,3-bis(diphenylphosphino)propane) which was delivered to the surfaces of four different self-assembled monolayers on gold (SAMs) at coverages of 1011 and 1012 clusters/mm2.more » Employing the spatial profiling capabilities of in-situ time-of-flight secondary ion mass spectrometry (TOF-SIMS) it is shown that, in addition to the chemical functionality of the monolayer (as demonstrated previously: ACS Nano, 2012, 6, 573) the coverage of cationic gold clusters on the surface may be used to control the distribution of ionic charge states of the soft-landed multiply charged clusters. In the case of a 1H,1H,2H,2H-perfluorodecanethiol SAM (FSAM) almost complete retention of charge by the deposited Au11L53+ clusters was observed at a lower coverage of 1011 clusters/mm2. In contrast, at a higher coverage of 1012 clusters/mm2, pronounced reduction of charge to Au11L52+ and Au11L5+ was observed on the FSAM. When soft landed onto 16- and 11-mercaptohexadecanoic acid surfaces on gold (16,11-COOH-SAMs), the mass-selected Au11L53+ clusters exhibited partial reduction of charge to Au11L52+ at lower coverage and additional reduction of charge to both Au11L52+ and Au11L5+ at higher coverage. The reduction of charge was found to be more pronounced on the surface of the shorter (thinner) C11 than the longer (thicker) C16-COOH-SAM. On the surface of the 1-dodecanethiol (HSAM) monolayer, the most abundant charge state was found to be Au11L52+ at lower coverage and Au11L5+ at higher coverage, respectively. A coverage-dependent electron tunneling mechanism is proposed to account for the observed reduction of charge of mass-selected multiply charged gold clusters soft landed on SAMs. The results demonstrate that one of the critical parameters that influence the chemical and physical properties of supported metal clusters, ionic charge state, may be controlled by selecting the coverage of charged species soft landed onto surfaces.« less

Moderating effect of intrinsic religiosity on the relationship between depression and cognitive function among community-dwelling older adults.

PubMed

Foong, Hui Foh; Hamid, Tengku Aizan; Ibrahim, Rahimah; Haron, Sharifah Azizah

2018-04-01

Research has found that depression in later life is associated with cognitive impairment. Thus, the mechanism to reduce the effect of depression on cognitive function is warranted. In this paper, we intend to examine whether intrinsic religiosity mediates the association between depression and cognitive function. The study included 2322 nationally representative community-dwelling elderly in Malaysia, randomly selected through a multi-stage proportional cluster random sampling from Peninsular Malaysia. The elderly were surveyed on socio-demographic information, cognitive function, depression and intrinsic religiosity. A four-step moderated hierarchical regression analysis was employed to test the moderating effect. Statistical analyses were performed using SPSS (version 15.0). Bivariate analyses showed that both depression and intrinsic religiosity had significant relationships with cognitive function. In addition, four-step moderated hierarchical regression analysis revealed that the intrinsic religiosity moderated the association between depression and cognitive function, after controlling for selected socio-demographic characteristics. Intrinsic religiosity might reduce the negative effect of depression on cognitive function. Professionals who are working with depressed older adults should seek ways to improve their intrinsic religiosity as one of the strategies to prevent cognitive impairment.

Reporting non-adherence in cluster randomised trials: A systematic review.

PubMed

Agbla, Schadrac C; DiazOrdaz, Karla

2018-06-01

Treatment non-adherence in randomised trials refers to situations where some participants do not receive their allocated treatment as intended. For cluster randomised trials, where the unit of randomisation is a group of participants, non-adherence may occur at the cluster or individual level. When non-adherence occurs, randomisation no longer guarantees that the relationship between treatment receipt and outcome is unconfounded, and the power to detect the treatment effects in intention-to-treat analysis may be reduced. Thus, recording adherence and estimating the causal treatment effect adequately are of interest for clinical trials. To assess the extent of reporting of non-adherence issues in published cluster trials and to establish which methods are currently being used for addressing non-adherence, if any, and whether clustering is accounted for in these. We systematically reviewed 132 cluster trials published in English in 2011 previously identified through a search in PubMed. One-hundred and twenty three cluster trials were included in this systematic review. Non-adherence was reported in 56 cluster trials. Among these, 19 reported a treatment efficacy estimate: per protocol in 15 and as treated in 4. No study discussed the assumptions made by these methods, their plausibility or the sensitivity of the results to deviations from these assumptions. The year of publication of the cluster trials included in this review (2011) could be considered a limitation of this study; however, no new guidelines regarding the reporting and the handling of non-adherence for cluster trials have been published since. In addition, a single reviewer undertook the data extraction. To mitigate this, a second reviewer conducted a validation of the extraction process on 15 randomly selected reports. Agreement was satisfactory (93%). Despite the recommendations of the Consolidated Standards of Reporting Trials statement extension to cluster randomised trials, treatment adherence is under-reported. Among the trials providing adherence information, there was substantial variation in how adherence was defined, handled and reported. Researchers should discuss the assumptions required for the results to be interpreted causally and whether these are scientifically plausible in their studies. Sensitivity analyses to study the robustness of the results to departures from these assumptions should be performed.

Power Analysis for Cross Level Mediation in CRTs

ERIC Educational Resources Information Center

Kelcey, Ben

2014-01-01

A common design in education research for interventions operating at a group or cluster level is a cluster randomized trial (CRT) (Bloom, 2005). In CRTs, intact clusters (e.g., schools) are assigned to treatment conditions rather than individuals (e.g., students) and are frequently an effective way to study interventions because they permit…

Effects of a tobacco control intervention for teachers in India: results of the Bihar school teachers study.

PubMed

Sorensen, Glorian; Pednekar, Mangesh S; Sinha, Dhirendra N; Stoddard, Anne M; Nagler, Eve; Aghi, Mira B; Lando, Harry A; Viswanath, Kasisomayajula; Pawar, Pratibha; Gupta, Prakash C

2013-11-01

We assessed a school-based intervention designed to promote tobacco control among teachers in the Indian state of Bihar. We used a cluster-randomized design to test the intervention, which comprised educational efforts, tobacco control policies, and cessation support and was tailored to the local social context. In 2009 to 2011, we randomly selected 72 schools from participating school districts and randomly assigned them in blocks (rural or urban) to intervention or delayed-intervention control conditions. Immediately after the intervention, the 30-day quit rate was 50% in the intervention and 15% in the control group (P = .001). At the 9-month postintervention survey, the adjusted 6-month quit rate was 19% in the intervention and 7% in the control group (P = .06). Among teachers employed for the entire academic year of the intervention, the adjusted 6-month abstinence rates were 20% and 5%, respectively, for the intervention and control groups (P = .04). These findings demonstrate the potent impact of an intervention that took advantage of social resources among teachers, who can serve as role models for tobacco control in their communities.

THE CLUSTERING OF ALFALFA GALAXIES: DEPENDENCE ON H I MASS, RELATIONSHIP WITH OPTICAL SAMPLES, AND CLUES OF HOST HALO PROPERTIES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Papastergis, Emmanouil; Giovanelli, Riccardo; Haynes, Martha P.

We use a sample of ≈6000 galaxies detected by the Arecibo Legacy Fast ALFA (ALFALFA) 21 cm survey to measure the clustering properties of H I-selected galaxies. We find no convincing evidence for a dependence of clustering on galactic atomic hydrogen (H I) mass, over the range M{sub H{sub I}} ≈ 10{sup 8.5}-10{sup 10.5} M{sub ☉}. We show that previously reported results of weaker clustering for low H I mass galaxies are probably due to finite-volume effects. In addition, we compare the clustering of ALFALFA galaxies with optically selected samples drawn from the Sloan Digital Sky Survey (SDSS). We findmore » that H I-selected galaxies cluster more weakly than even relatively optically faint galaxies, when no color selection is applied. Conversely, when SDSS galaxies are split based on their color, we find that the correlation function of blue optical galaxies is practically indistinguishable from that of H I-selected galaxies. At the same time, SDSS galaxies with red colors are found to cluster significantly more than H I-selected galaxies, a fact that is evident in both the projected as well as the full two-dimensional correlation function. A cross-correlation analysis further reveals that gas-rich galaxies 'avoid' being located within ≈3 Mpc of optical galaxies with red colors. Next, we consider the clustering properties of halo samples selected from the Bolshoi ΛCDM simulation. A comparison with the clustering of ALFALFA galaxies suggests that galactic H I mass is not tightly related to host halo mass and that a sizable fraction of subhalos do not host H I galaxies. Lastly, we find that we can recover fairly well the correlation function of H I galaxies by just excluding halos with low spin parameter. This finding lends support to the hypothesis that halo spin plays a key role in determining the gas content of galaxies.« less

Prevalence and factors related to dental caries among pre-school children of Saddar town, Karachi, Pakistan: a cross-sectional study.

PubMed

Dawani, Narendar; Nisar, Nighat; Khan, Nazeer; Syed, Shahbano; Tanweer, Navara

2012-12-27

Dental caries is highly prevalent and a significant public health problem among children throughout the world. Epidemiological data regarding prevalence of dental caries amongst Pakistani pre-school children is very limited. The objective of this study is to determine the frequency of dental caries among pre-school children of Saddar Town, Karachi, Pakistan and the factors related to caries. A cross-sectional study of 1000 preschool children was conducted in Saddar town, Karachi. Two-stage cluster sampling was used to select the sample. At first stage, eight clusters were selected randomly from total 11 clusters. In second stage, from the eight selected clusters, preschools were identified and children between 3- to 6-years age group were assessed for dental caries. Caries prevalence was 51% with a mean dmft score being 2.08 (±2.97) of which decayed teeth constituted 1.95. The mean dmft of males was 2.3 (±3.08) and of females was 1.90 (±2.90). The mean dmft of 3, 4, 5 and 6-year olds was 1.65, 2.11, 2.16 and 3.11 respectively. A significant association was found between dental caries and following variables: age group of 4-years (p-value < 0.029, RR = 1.248, 95% Bias corrected CI 0.029-0.437) and 5-years (p-value < 0.009, RR = 1.545, 95% Bias corrected CI 0.047-0.739), presence of dental plaque (p-value < 0.003, RR = 0.744, 95% Bias corrected CI (-0.433)-(-0.169)), poor oral hygiene (p-value < 0.000, RR = 0.661, 95% Bias corrected CI (-0.532)-(-0.284)), as well as consumption of non-sweetened milk (p-value < 0.049, RR = 1.232, 95% Bias corrected CI 0.061-0.367). Half of the preschoolers had dental caries coupled with a high prevalence of unmet dental treatment needs. Association between caries experience and age of child, consumption of non-sweetened milk, dental plaque and poor oral hygiene had been established.

Mathematical modelling of complex contagion on clustered networks

NASA Astrophysics Data System (ADS)

O'sullivan, David J.; O'Keeffe, Gary; Fennell, Peter; Gleeson, James

2015-09-01

The spreading of behavior, such as the adoption of a new innovation, is influenced bythe structure of social networks that interconnect the population. In the experiments of Centola (Science, 2010), adoption of new behavior was shown to spread further and faster across clustered-lattice networks than across corresponding random networks. This implies that the “complex contagion” effects of social reinforcement are important in such diffusion, in contrast to “simple” contagion models of disease-spread which predict that epidemics would grow more efficiently on random networks than on clustered networks. To accurately model complex contagion on clustered networks remains a challenge because the usual assumptions (e.g. of mean-field theory) regarding tree-like networks are invalidated by the presence of triangles in the network; the triangles are, however, crucial to the social reinforcement mechanism, which posits an increased probability of a person adopting behavior that has been adopted by two or more neighbors. In this paper we modify the analytical approach that was introduced by Hebert-Dufresne et al. (Phys. Rev. E, 2010), to study disease-spread on clustered networks. We show how the approximation method can be adapted to a complex contagion model, and confirm the accuracy of the method with numerical simulations. The analytical results of the model enable us to quantify the level of social reinforcement that is required to observe—as in Centola’s experiments—faster diffusion on clustered topologies than on random networks.

As-built design specification for proportion estimate software subsystem

NASA Technical Reports Server (NTRS)

Obrien, S. (Principal Investigator)

1980-01-01

The Proportion Estimate Processor evaluates four estimation techniques in order to get an improved estimate of the proportion of a scene that is planted in a selected crop. The four techniques to be evaluated were provided by the techniques development section and are: (1) random sampling; (2) proportional allocation, relative count estimate; (3) proportional allocation, Bayesian estimate; and (4) sequential Bayesian allocation. The user is given two options for computation of the estimated mean square error. These are referred to as the cluster calculation option and the segment calculation option. The software for the Proportion Estimate Processor is operational on the IBM 3031 computer.

Combining Mixture Components for Clustering*

PubMed Central

Baudry, Jean-Patrick; Raftery, Adrian E.; Celeux, Gilles; Lo, Kenneth; Gottardo, Raphaël

2010-01-01

Model-based clustering consists of fitting a mixture model to data and identifying each cluster with one of its components. Multivariate normal distributions are typically used. The number of clusters is usually determined from the data, often using BIC. In practice, however, individual clusters can be poorly fitted by Gaussian distributions, and in that case model-based clustering tends to represent one non-Gaussian cluster by a mixture of two or more Gaussian distributions. If the number of mixture components is interpreted as the number of clusters, this can lead to overestimation of the number of clusters. This is because BIC selects the number of mixture components needed to provide a good approximation to the density, rather than the number of clusters as such. We propose first selecting the total number of Gaussian mixture components, K, using BIC and then combining them hierarchically according to an entropy criterion. This yields a unique soft clustering for each number of clusters less than or equal to K. These clusterings can be compared on substantive grounds, and we also describe an automatic way of selecting the number of clusters via a piecewise linear regression fit to the rescaled entropy plot. We illustrate the method with simulated data and a flow cytometry dataset. Supplemental Materials are available on the journal Web site and described at the end of the paper. PMID:20953302

A Variable-Selection Heuristic for K-Means Clustering.

ERIC Educational Resources Information Center

Brusco, Michael J.; Cradit, J. Dennis

2001-01-01

Presents a variable selection heuristic for nonhierarchical (K-means) cluster analysis based on the adjusted Rand index for measuring cluster recovery. Subjected the heuristic to Monte Carlo testing across more than 2,200 datasets. Results indicate that the heuristic is extremely effective at eliminating masking variables. (SLD)

Hierarchical Cluster Analysis of Three-Dimensional Reconstructions of Unbiased Sampled Microglia Shows not Continuous Morphological Changes from Stage 1 to 2 after Multiple Dengue Infections in Callithrix penicillata

PubMed Central

Diniz, Daniel G.; Silva, Geane O.; Naves, Thaís B.; Fernandes, Taiany N.; Araújo, Sanderson C.; Diniz, José A. P.; de Farias, Luis H. S.; Sosthenes, Marcia C. K.; Diniz, Cristovam G.; Anthony, Daniel C.; da Costa Vasconcelos, Pedro F.; Picanço Diniz, Cristovam W.

2016-01-01

It is known that microglial morphology and function are related, but few studies have explored the subtleties of microglial morphological changes in response to specific pathogens. In the present report we quantitated microglia morphological changes in a monkey model of dengue disease with virus CNS invasion. To mimic multiple infections that usually occur in endemic areas, where higher dengue infection incidence and abundant mosquito vectors carrying different serotypes coexist, subjects received once a week subcutaneous injections of DENV3 (genotype III)-infected culture supernatant followed 24 h later by an injection of anti-DENV2 antibody. Control animals received either weekly anti-DENV2 antibodies, or no injections. Brain sections were immunolabeled for DENV3 antigens and IBA-1. Random and systematic microglial samples were taken from the polymorphic layer of dentate gyrus for 3-D reconstructions, where we found intense immunostaining for TNFα and DENV3 virus antigens. We submitted all bi- or multimodal morphological parameters of microglia to hierarchical cluster analysis and found two major morphological phenotypes designated types I and II. Compared to type I (stage 1), type II microglia were more complex; displaying higher number of nodes, processes and trees and larger surface area and volumes (stage 2). Type II microglia were found only in infected monkeys, whereas type I microglia was found in both control and infected subjects. Hierarchical cluster analysis of morphological parameters of 3-D reconstructions of random and systematic selected samples in control and ADE dengue infected monkeys suggests that microglia morphological changes from stage 1 to stage 2 may not be continuous. PMID:27047345

Effects of a community-based nutrition promotion programme on child feeding and hygiene practices among caregivers in rural Eastern Ethiopia.

PubMed

Kang, Yunhee; Suh, Youn Kyoung; Debele, Lemma; Juon, Hee-Soon; Christian, Parul

2017-06-01

To evaluate the effectiveness of a community-based participatory nutrition promotion (CPNP) programme involving a 2-week group nutrition session in improving child feeding and hygiene practices among caregivers. Cluster randomized trial. In the intervention area (six clusters), the CPNP programme was added to the context of government nutrition programmes; the control area (six clusters) received the government programme only. Child feeding practices were assessed every 3 months using a 24 h dietary recall questionnaire, and hand washing with soap was assessed every 6 months, over a period of 12 months. Feeding and hygiene measures at each visit were scored and the scores summed up for the entire follow-up period. Habro and Melka Bello districts, Ethiopia. Randomly selected mothers with a child aged 6-12 months (n 1790). A total of 1199 mothers, 629 in the control and 570 in the intervention areas, were assessed at all visits and included in the analysis. Mothers in the intervention area showed higher scores than those in the control area regarding meal frequency (difference: 1·04, 95 % CI 0·35, 1·73), composite feeding score_1 (difference: 1·25, 95 % CI 0·37, 2·13; a summing score of currently breast-feeding, meal frequency and dietary diversity) and composite feeding score_2 (difference: 1·40, 95 % CI 0·49, 2·32; a summing score of meal frequency and dietary diversity). However, there were no differences in the scores of breast-feeding, dietary diversity and hand washing between the two areas (all P>0·05). The CPNP programme was effective in improving some child feeding behaviours in rural Eastern Ethiopia.

Hierarchical Cluster Analysis of Three-Dimensional Reconstructions of Unbiased Sampled Microglia Shows not Continuous Morphological Changes from Stage 1 to 2 after Multiple Dengue Infections in Callithrix penicillata.

PubMed

Diniz, Daniel G; Silva, Geane O; Naves, Thaís B; Fernandes, Taiany N; Araújo, Sanderson C; Diniz, José A P; de Farias, Luis H S; Sosthenes, Marcia C K; Diniz, Cristovam G; Anthony, Daniel C; da Costa Vasconcelos, Pedro F; Picanço Diniz, Cristovam W

2016-01-01

It is known that microglial morphology and function are related, but few studies have explored the subtleties of microglial morphological changes in response to specific pathogens. In the present report we quantitated microglia morphological changes in a monkey model of dengue disease with virus CNS invasion. To mimic multiple infections that usually occur in endemic areas, where higher dengue infection incidence and abundant mosquito vectors carrying different serotypes coexist, subjects received once a week subcutaneous injections of DENV3 (genotype III)-infected culture supernatant followed 24 h later by an injection of anti-DENV2 antibody. Control animals received either weekly anti-DENV2 antibodies, or no injections. Brain sections were immunolabeled for DENV3 antigens and IBA-1. Random and systematic microglial samples were taken from the polymorphic layer of dentate gyrus for 3-D reconstructions, where we found intense immunostaining for TNFα and DENV3 virus antigens. We submitted all bi- or multimodal morphological parameters of microglia to hierarchical cluster analysis and found two major morphological phenotypes designated types I and II. Compared to type I (stage 1), type II microglia were more complex; displaying higher number of nodes, processes and trees and larger surface area and volumes (stage 2). Type II microglia were found only in infected monkeys, whereas type I microglia was found in both control and infected subjects. Hierarchical cluster analysis of morphological parameters of 3-D reconstructions of random and systematic selected samples in control and ADE dengue infected monkeys suggests that microglia morphological changes from stage 1 to stage 2 may not be continuous.