Analysis of radiation-induced small Cu particle cluster formation in aqueous CuCl2

USGS Publications Warehouse

Jayanetti, Sumedha; Mayanovic, Robert A.; Anderson, Alan J.; Bassett, William A.; Chou, I.-Ming

2001-01-01

Radition-induced small Cu particle cluster formation in aqueous CuCl2 was analyzed. It was noticed that nearest neighbor distance increased with the increase in the time of irradiation. This showed that the clusters approached the lattice dimension of bulk copper. As the average cluster size approached its bulk dimensions, an increase in the nearest neighbor coordination number was found with the decrease in the surface to volume ratio. Radiolysis of water by incident x-ray beam led to the reduction of copper ions in the solution to themetallic state.

Nursing home care quality: a cluster analysis.

PubMed

Grøndahl, Vigdis Abrahamsen; Fagerli, Liv Berit

2017-02-13

Purpose The purpose of this paper is to explore potential differences in how nursing home residents rate care quality and to explore cluster characteristics. Design/methodology/approach A cross-sectional design was used, with one questionnaire including questions from quality from patients' perspective and Big Five personality traits, together with questions related to socio-demographic aspects and health condition. Residents ( n=103) from four Norwegian nursing homes participated (74.1 per cent response rate). Hierarchical cluster analysis identified clusters with respect to care quality perceptions. χ 2 tests and one-way between-groups ANOVA were performed to characterise the clusters ( p<0.05). Findings Two clusters were identified; Cluster 1 residents (28.2 per cent) had the best care quality perceptions and Cluster 2 (67.0 per cent) had the worst perceptions. The clusters were statistically significant and characterised by personal-related conditions: gender, psychological well-being, preferences, admission, satisfaction with staying in the nursing home, emotional stability and agreeableness, and by external objective care conditions: healthcare personnel and registered nurses. Research limitations/implications Residents assessed as having no cognitive impairments were included, thus excluding the largest group. By choosing questionnaire design and structured interviews, the number able to participate may increase. Practical implications Findings may provide healthcare personnel and managers with increased knowledge on which to develop strategies to improve specific care quality perceptions. Originality/value Cluster analysis can be an effective tool for differentiating between nursing homes residents' care quality perceptions.

A formal concept analysis approach to consensus clustering of multi-experiment expression data

PubMed Central

2014-01-01

Background Presently, with the increasing number and complexity of available gene expression datasets, the combination of data from multiple microarray studies addressing a similar biological question is gaining importance. The analysis and integration of multiple datasets are expected to yield more reliable and robust results since they are based on a larger number of samples and the effects of the individual study-specific biases are diminished. This is supported by recent studies suggesting that important biological signals are often preserved or enhanced by multiple experiments. An approach to combining data from different experiments is the aggregation of their clusterings into a consensus or representative clustering solution which increases the confidence in the common features of all the datasets and reveals the important differences among them. Results We propose a novel generic consensus clustering technique that applies Formal Concept Analysis (FCA) approach for the consolidation and analysis of clustering solutions derived from several microarray datasets. These datasets are initially divided into groups of related experiments with respect to a predefined criterion. Subsequently, a consensus clustering algorithm is applied to each group resulting in a clustering solution per group. These solutions are pooled together and further analysed by employing FCA which allows extracting valuable insights from the data and generating a gene partition over all the experiments. In order to validate the FCA-enhanced approach two consensus clustering algorithms are adapted to incorporate the FCA analysis. Their performance is evaluated on gene expression data from multi-experiment study examining the global cell-cycle control of fission yeast. The FCA results derived from both methods demonstrate that, although both algorithms optimize different clustering characteristics, FCA is able to overcome and diminish these differences and preserve some relevant biological signals. Conclusions The proposed FCA-enhanced consensus clustering technique is a general approach to the combination of clustering algorithms with FCA for deriving clustering solutions from multiple gene expression matrices. The experimental results presented herein demonstrate that it is a robust data integration technique able to produce good quality clustering solution that is representative for the whole set of expression matrices. PMID:24885407

Cluster analysis and its application to healthcare claims data: a study of end-stage renal disease patients who initiated hemodialysis.

PubMed

Liao, Minlei; Li, Yunfeng; Kianifard, Farid; Obi, Engels; Arcona, Stephen

2016-03-02

Cluster analysis (CA) is a frequently used applied statistical technique that helps to reveal hidden structures and "clusters" found in large data sets. However, this method has not been widely used in large healthcare claims databases where the distribution of expenditure data is commonly severely skewed. The purpose of this study was to identify cost change patterns of patients with end-stage renal disease (ESRD) who initiated hemodialysis (HD) by applying different clustering methods. A retrospective, cross-sectional, observational study was conducted using the Truven Health MarketScan® Research Databases. Patients aged ≥18 years with ≥2 ESRD diagnoses who initiated HD between 2008 and 2010 were included. The K-means CA method and hierarchical CA with various linkage methods were applied to all-cause costs within baseline (12-months pre-HD) and follow-up periods (12-months post-HD) to identify clusters. Demographic, clinical, and cost information was extracted from both periods, and then examined by cluster. A total of 18,380 patients were identified. Meaningful all-cause cost clusters were generated using K-means CA and hierarchical CA with either flexible beta or Ward's methods. Based on cluster sample sizes and change of cost patterns, the K-means CA method and 4 clusters were selected: Cluster 1: Average to High (n = 113); Cluster 2: Very High to High (n = 89); Cluster 3: Average to Average (n = 16,624); or Cluster 4: Increasing Costs, High at Both Points (n = 1554). Median cost changes in the 12-month pre-HD and post-HD periods increased from $185,070 to $884,605 for Cluster 1 (Average to High), decreased from $910,930 to $157,997 for Cluster 2 (Very High to High), were relatively stable and remained low from $15,168 to $13,026 for Cluster 3 (Average to Average), and increased from $57,909 to $193,140 for Cluster 4 (Increasing Costs, High at Both Points). Relatively stable costs after starting HD were associated with more stable scores on comorbidity index scores from the pre-and post-HD periods, while increasing costs were associated with more sharply increasing comorbidity scores. The K-means CA method appeared to be the most appropriate in healthcare claims data with highly skewed cost information when taking into account both change of cost patterns and sample size in the smallest cluster.

Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles.

PubMed

Williams, N J; Nasuto, S J; Saddy, J D

2015-07-30

The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. We propose a complete pipeline for the cluster analysis of ERP data. To increase the signal-to-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA) to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). After validating the pipeline on simulated data, we tested it on data from two experiments - a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership. Our analysis operates on denoised single-trials, the number of clusters are determined in a principled manner and the results are presented through an intuitive visualisation. Given the cluster structure in some experimental conditions, we suggest application of cluster analysis as a preliminary step before ensemble averaging. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Using cluster analysis to organize and explore regional GPS velocities

USGS Publications Warehouse

Simpson, Robert W.; Thatcher, Wayne; Savage, James C.

2012-01-01

Cluster analysis offers a simple visual exploratory tool for the initial investigation of regional Global Positioning System (GPS) velocity observations, which are providing increasingly precise mappings of actively deforming continental lithosphere. The deformation fields from dense regional GPS networks can often be concisely described in terms of relatively coherent blocks bounded by active faults, although the choice of blocks, their number and size, can be subjective and is often guided by the distribution of known faults. To illustrate our method, we apply cluster analysis to GPS velocities from the San Francisco Bay Region, California, to search for spatially coherent patterns of deformation, including evidence of block-like behavior. The clustering process identifies four robust groupings of velocities that we identify with four crustal blocks. Although the analysis uses no prior geologic information other than the GPS velocities, the cluster/block boundaries track three major faults, both locked and creeping.

Ligand Effects in Aluminum Cluster based Energetic Materials

DTIC Science & Technology

2017-09-01

was recently reported and the effect of their increased steric bulk is discussed here. Experimental results and density functional theory (DFT...analysis show that these clusters are enthalpically more stable than the Cp* variant, due primarily to non -covalent interactions (NCIs) across ligand...C5Me4iPr), two clusters similar to Al4Cp*4, was recently reported and the effect of their increased steric bulk is discussed here. Experimental

Statistical analysis of activation and reaction energies with quasi-variational coupled-cluster theory

NASA Astrophysics Data System (ADS)

Black, Joshua A.; Knowles, Peter J.

2018-06-01

The performance of quasi-variational coupled-cluster (QV) theory applied to the calculation of activation and reaction energies has been investigated. A statistical analysis of results obtained for six different sets of reactions has been carried out, and the results have been compared to those from standard single-reference methods. In general, the QV methods lead to increased activation energies and larger absolute reaction energies compared to those obtained with traditional coupled-cluster theory.

Impact of Sampling Density on the Extent of HIV Clustering

PubMed Central

Novitsky, Vlad; Moyo, Sikhulile; Lei, Quanhong; DeGruttola, Victor

2014-01-01

Abstract Identifying and monitoring HIV clusters could be useful in tracking the leading edge of HIV transmission in epidemics. Currently, greater specificity in the definition of HIV clusters is needed to reduce confusion in the interpretation of HIV clustering results. We address sampling density as one of the key aspects of HIV cluster analysis. The proportion of viral sequences in clusters was estimated at sampling densities from 1.0% to 70%. A set of 1,248 HIV-1C env gp120 V1C5 sequences from a single community in Botswana was utilized in simulation studies. Matching numbers of HIV-1C V1C5 sequences from the LANL HIV Database were used as comparators. HIV clusters were identified by phylogenetic inference under bootstrapped maximum likelihood and pairwise distance cut-offs. Sampling density below 10% was associated with stochastic HIV clustering with broad confidence intervals. HIV clustering increased linearly at sampling density >10%, and was accompanied by narrowing confidence intervals. Patterns of HIV clustering were similar at bootstrap thresholds 0.7 to 1.0, but the extent of HIV clustering decreased with higher bootstrap thresholds. The origin of sampling (local concentrated vs. scattered global) had a substantial impact on HIV clustering at sampling densities ≥10%. Pairwise distances at 10% were estimated as a threshold for cluster analysis of HIV-1 V1C5 sequences. The node bootstrap support distribution provided additional evidence for 10% sampling density as the threshold for HIV cluster analysis. The detectability of HIV clusters is substantially affected by sampling density. A minimal genotyping density of 10% and sampling density of 50–70% are suggested for HIV-1 V1C5 cluster analysis. PMID:25275430

Using conjoint and cluster analysis in developing new product for micro, small and medium enterprises (SMEs) based on customer preferences (Case study: Lampung province's banana chips)

NASA Astrophysics Data System (ADS)

Kosasih, Wilson; Salomon, Lithrone Laricha; Hutomo, Reynaldo

2017-08-01

This paper discusses the development of new products of Micro, Small and Medium Entreprises (SMEs) to identify what attributes are considered by consumers, as well as combinations of attributes that need to be analyzed into the main preferences of consumers. The purpose of this research is to increase the added value and competitiveness of SMEs through product innovation. The object of this study is banana chips produced by SMEs from the province of Lampung which it considered to be unique souvenirs of the province. The research data were collected by distributing questionnaires in Jakarta which has heterogeneous population, in order to develop banana chip's marketing and increase its market share in Indonesia. Data processing was performed using conjoint analysis and cluster analysis. Segmentation was performed using conjoint analysis based on the importance level of attributes and part-worth of level attributes of each cluster. Finally, characteristics and consumer preferences of each cluster will be a consideration in determining the product development and marketing strategies.

Measuring consistent masses for 25 Milky Way globular clusters

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

Kimmig, Brian; Seth, Anil; Ivans, Inese I.

2015-02-01

We present central velocity dispersions, masses, mass-to-light ratios (M/Ls ), and rotation strengths for 25 Galactic globular clusters (GCs). We derive radial velocities of 1951 stars in 12 GCs from single order spectra taken with Hectochelle on the MMT telescope. To this sample we add an analysis of available archival data of individual stars. For the full set of data we fit King models to derive consistent dynamical parameters for the clusters. We find good agreement between single-mass King models and the observed radial dispersion profiles. The large, uniform sample of dynamical masses we derive enables us to examine trendsmore » of M/L with cluster mass and metallicity. The overall values of M/L and the trends with mass and metallicity are consistent with existing measurements from a large sample of M31 clusters. This includes a clear trend of increasing M/L with cluster mass and lower than expected M/Ls for the metal-rich clusters. We find no clear trend of increasing rotation with increasing cluster metallicity suggested in previous work.« less

Text mining to decipher free-response consumer complaints: insights from the NHTSA vehicle owner's complaint database.

PubMed

Ghazizadeh, Mahtab; McDonald, Anthony D; Lee, John D

2014-09-01

This study applies text mining to extract clusters of vehicle problems and associated trends from free-response data in the National Highway Traffic Safety Administration's vehicle owner's complaint database. As the automotive industry adopts new technologies, it is important to systematically assess the effect of these changes on traffic safety. Driving simulators, naturalistic driving data, and crash databases all contribute to a better understanding of how drivers respond to changing vehicle technology, but other approaches, such as automated analysis of incident reports, are needed. Free-response data from incidents representing two severity levels (fatal incidents and incidents involving injury) were analyzed using a text mining approach: latent semantic analysis (LSA). LSA and hierarchical clustering identified clusters of complaints for each severity level, which were compared and analyzed across time. Cluster analysis identified eight clusters of fatal incidents and six clusters of incidents involving injury. Comparisons showed that although the airbag clusters across the two severity levels have the same most frequent terms, the circumstances around the incidents differ. The time trends show clear increases in complaints surrounding the Ford/Firestone tire recall and the Toyota unintended acceleration recall. Increases in complaints may be partially driven by these recall announcements and the associated media attention. Text mining can reveal useful information from free-response databases that would otherwise be prohibitively time-consuming and difficult to summarize manually. Text mining can extend human analysis capabilities for large free-response databases to support earlier detection of problems and more timely safety interventions.

Hierarchical cluster analysis of progression patterns in open-angle glaucoma patients with medical treatment.

PubMed

Bae, Hyoung Won; Rho, Seungsoo; Lee, Hye Sun; Lee, Naeun; Hong, Samin; Seong, Gong Je; Sung, Kyung Rim; Kim, Chan Yun

2014-04-29

To classify medically treated open-angle glaucoma (OAG) by the pattern of progression using hierarchical cluster analysis, and to determine OAG progression characteristics by comparing clusters. Ninety-five eyes of 95 OAG patients who received medical treatment, and who had undergone visual field (VF) testing at least once per year for 5 or more years. OAG was classified into subgroups using hierarchical cluster analysis based on the following five variables: baseline mean deviation (MD), baseline visual field index (VFI), MD slope, VFI slope, and Glaucoma Progression Analysis (GPA) printout. After that, other parameters were compared between clusters. Two clusters were made after a hierarchical cluster analysis. Cluster 1 showed -4.06 ± 2.43 dB baseline MD, 92.58% ± 6.27% baseline VFI, -0.28 ± 0.38 dB per year MD slope, -0.52% ± 0.81% per year VFI slope, and all "no progression" cases in GPA printout, whereas cluster 2 showed -8.68 ± 3.81 baseline MD, 77.54 ± 12.98 baseline VFI, -0.72 ± 0.55 MD slope, -2.22 ± 1.89 VFI slope, and seven "possible" and four "likely" progression cases in GPA printout. There were no significant differences in age, sex, mean IOP, central corneal thickness, and axial length between clusters. However, cluster 2 included more high-tension glaucoma patients and used a greater number of antiglaucoma eye drops significantly compared with cluster 1. Hierarchical cluster analysis of progression patterns divided OAG into slow and fast progression groups, evidenced by assessing the parameters of glaucomatous progression in VF testing. In the fast progression group, the prevalence of high-tension glaucoma was greater and the number of antiglaucoma medications administered was increased versus the slow progression group. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

The Productivity Analysis of Chennai Automotive Industry Cluster

NASA Astrophysics Data System (ADS)

Bhaskaran, E.

2014-07-01

Chennai, also called the Detroit of India, is India's second fastest growing auto market and exports auto components and vehicles to US, Germany, Japan and Brazil. For inclusive growth and sustainable development, 250 auto component industries in Ambattur, Thirumalisai and Thirumudivakkam Industrial Estates located in Chennai have adopted the Cluster Development Approach called Automotive Component Cluster. The objective is to study the Value Chain, Correlation and Data Envelopment Analysis by determining technical efficiency, peer weights, input and output slacks of 100 auto component industries in three estates. The methodology adopted is using Data Envelopment Analysis of Output Oriented Banker Charnes Cooper model by taking net worth, fixed assets, employment as inputs and gross output as outputs. The non-zero represents the weights for efficient clusters. The higher slack obtained reveals the excess net worth, fixed assets, employment and shortage in gross output. To conclude, the variables are highly correlated and the inefficient industries should increase their gross output or decrease the fixed assets or employment. Moreover for sustainable development, the cluster should strengthen infrastructure, technology, procurement, production and marketing interrelationships to decrease costs and to increase productivity and efficiency to compete in the indigenous and export market.

Peeking Network States with Clustered Patterns

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

Kim, Jinoh; Sim, Alex

2015-10-20

Network traffic monitoring has long been a core element for effec- tive network management and security. However, it is still a chal- lenging task with a high degree of complexity for comprehensive analysis when considering multiple variables and ever-increasing traffic volumes to monitor. For example, one of the widely con- sidered approaches is to scrutinize probabilistic distributions, but it poses a scalability concern and multivariate analysis is not gen- erally supported due to the exponential increase of the complexity. In this work, we propose a novel method for network traffic moni- toring based on clustering, one of the powerful deep-learningmore » tech- niques. We show that the new approach enables us to recognize clustered results as patterns representing the network states, which can then be utilized to evaluate “similarity” of network states over time. In addition, we define a new quantitative measure for the similarity between two compared network states observed in dif- ferent time windows, as a supportive means for intuitive analysis. Finally, we demonstrate the clustering-based network monitoring with public traffic traces, and show that the proposed approach us- ing the clustering method has a great opportunity for feasible, cost- effective network monitoring.« less

Cluster analysis of accelerated molecular dynamics simulations: A case study of the decahedron to icosahedron transition in Pt nanoparticles.

PubMed

Huang, Rao; Lo, Li-Ta; Wen, Yuhua; Voter, Arthur F; Perez, Danny

2017-10-21

Modern molecular-dynamics-based techniques are extremely powerful to investigate the dynamical evolution of materials. With the increase in sophistication of the simulation techniques and the ubiquity of massively parallel computing platforms, atomistic simulations now generate very large amounts of data, which have to be carefully analyzed in order to reveal key features of the underlying trajectories, including the nature and characteristics of the relevant reaction pathways. We show that clustering algorithms, such as the Perron Cluster Cluster Analysis, can provide reduced representations that greatly facilitate the interpretation of complex trajectories. To illustrate this point, clustering tools are used to identify the key kinetic steps in complex accelerated molecular dynamics trajectories exhibiting shape fluctuations in Pt nanoclusters. This analysis provides an easily interpretable coarse representation of the reaction pathways in terms of a handful of clusters, in contrast to the raw trajectory that contains thousands of unique states and tens of thousands of transitions.

Cluster analysis of accelerated molecular dynamics simulations: A case study of the decahedron to icosahedron transition in Pt nanoparticles

NASA Astrophysics Data System (ADS)

Huang, Rao; Lo, Li-Ta; Wen, Yuhua; Voter, Arthur F.; Perez, Danny

2017-10-01

Modern molecular-dynamics-based techniques are extremely powerful to investigate the dynamical evolution of materials. With the increase in sophistication of the simulation techniques and the ubiquity of massively parallel computing platforms, atomistic simulations now generate very large amounts of data, which have to be carefully analyzed in order to reveal key features of the underlying trajectories, including the nature and characteristics of the relevant reaction pathways. We show that clustering algorithms, such as the Perron Cluster Cluster Analysis, can provide reduced representations that greatly facilitate the interpretation of complex trajectories. To illustrate this point, clustering tools are used to identify the key kinetic steps in complex accelerated molecular dynamics trajectories exhibiting shape fluctuations in Pt nanoclusters. This analysis provides an easily interpretable coarse representation of the reaction pathways in terms of a handful of clusters, in contrast to the raw trajectory that contains thousands of unique states and tens of thousands of transitions.

Recent increased identification and transmission of HIV-1 unique recombinant forms in Sweden.

PubMed

Neogi, Ujjwal; Siddik, Abu Bakar; Kalaghatgi, Prabhav; Gisslén, Magnus; Bratt, Göran; Marrone, Gaetano; Sönnerborg, Anders

2017-07-25

A temporal increase in non-B subtypes has earlier been described in Sweden by us and we hypothesized that this increased viral heterogeneity may become a hotspot for the development of more complex and unique recombinant forms (URFs) if the epidemics converge. In the present study, we performed subtyping using four automated tools and phylogenetic analysis by RAxML of pol gene sequences (n = 5246) and HIV-1 near full-length genome (HIV-NFLG) sequences (n = 104). A CD4 + T-cell decline trajectory algorithm was used to estimate time of HIV infection. Transmission clusters were identified using the family-joining method. The analysis of HIV-NFLG and pol gene described 10.6% (11/104) and 2.6% (137/5246) of the strains as URFs, respectively. An increasing trend of URFs was observed in recent years by both approaches (p = 0·0082; p < 0·0001). Transmission cluster analysis using the pol gene of all URFs identified 14 clusters with two to eight sequences. Larger transmission clusters of URFs (BF1 and 01B) were observed among MSM who mostly were sero-diagnosed in recent time. Understanding the increased appearance and transmission of URFs in recent years could have importance for public health interventions and the use of HIV-NFLG would provide better statistical support for such assessments.

Astrophysical properties of star clusters in the Magellanic Clouds homogeneously estimated by ASteCA

NASA Astrophysics Data System (ADS)

Perren, G. I.; Piatti, A. E.; Vázquez, R. A.

2017-06-01

Aims: We seek to produce a homogeneous catalog of astrophysical parameters of 239 resolved star clusters, located in the Small and Large Magellanic Clouds, observed in the Washington photometric system. Methods: The cluster sample was processed with the recently introduced Automated Stellar Cluster Analysis (ASteCA) package, which ensures both an automatized and a fully reproducible treatment, together with a statistically based analysis of their fundamental parameters and associated uncertainties. The fundamental parameters determined for each cluster with this tool, via a color-magnitude diagram (CMD) analysis, are metallicity, age, reddening, distance modulus, and total mass. Results: We generated a homogeneous catalog of structural and fundamental parameters for the studied cluster sample and performed a detailed internal error analysis along with a thorough comparison with values taken from 26 published articles. We studied the distribution of cluster fundamental parameters in both Clouds and obtained their age-metallicity relationships. Conclusions: The ASteCA package can be applied to an unsupervised determination of fundamental cluster parameters, which is a task of increasing relevance as more data becomes available through upcoming surveys. A table with the estimated fundamental parameters for the 239 clusters analyzed is only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/602/A89

Study on text mining algorithm for ultrasound examination of chronic liver diseases based on spectral clustering

NASA Astrophysics Data System (ADS)

Chang, Bingguo; Chen, Xiaofei

2018-05-01

Ultrasonography is an important examination for the diagnosis of chronic liver disease. The doctor gives the liver indicators and suggests the patient's condition according to the description of ultrasound report. With the rapid increase in the amount of data of ultrasound report, the workload of professional physician to manually distinguish ultrasound results significantly increases. In this paper, we use the spectral clustering method to cluster analysis of the description of the ultrasound report, and automatically generate the ultrasonic diagnostic diagnosis by machine learning. 110 groups ultrasound examination report of chronic liver disease were selected as test samples in this experiment, and the results were validated by spectral clustering and compared with k-means clustering algorithm. The results show that the accuracy of spectral clustering is 92.73%, which is higher than that of k-means clustering algorithm, which provides a powerful ultrasound-assisted diagnosis for patients with chronic liver disease.

The impact of catchment source group classification on the accuracy of sediment fingerprinting outputs.

PubMed

Pulley, Simon; Foster, Ian; Collins, Adrian L

2017-06-01

The objective classification of sediment source groups is at present an under-investigated aspect of source tracing studies, which has the potential to statistically improve discrimination between sediment sources and reduce uncertainty. This paper investigates this potential using three different source group classification schemes. The first classification scheme was simple surface and subsurface groupings (Scheme 1). The tracer signatures were then used in a two-step cluster analysis to identify the sediment source groupings naturally defined by the tracer signatures (Scheme 2). The cluster source groups were then modified by splitting each one into a surface and subsurface component to suit catchment management goals (Scheme 3). The schemes were tested using artificial mixtures of sediment source samples. Controlled corruptions were made to some of the mixtures to mimic the potential causes of tracer non-conservatism present when using tracers in natural fluvial environments. It was determined how accurately the known proportions of sediment sources in the mixtures were identified after unmixing modelling using the three classification schemes. The cluster analysis derived source groups (2) significantly increased tracer variability ratios (inter-/intra-source group variability) (up to 2122%, median 194%) compared to the surface and subsurface groupings (1). As a result, the composition of the artificial mixtures was identified an average of 9.8% more accurately on the 0-100% contribution scale. It was found that the cluster groups could be reclassified into a surface and subsurface component (3) with no significant increase in composite uncertainty (a 0.1% increase over Scheme 2). The far smaller effects of simulated tracer non-conservatism for the cluster analysis based schemes (2 and 3) was primarily attributed to the increased inter-group variability producing a far larger sediment source signal that the non-conservatism noise (1). Modified cluster analysis based classification methods have the potential to reduce composite uncertainty significantly in future source tracing studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Sputum neutrophil counts are associated with more severe asthma phenotypes using cluster analysis.

PubMed

Moore, Wendy C; Hastie, Annette T; Li, Xingnan; Li, Huashi; Busse, William W; Jarjour, Nizar N; Wenzel, Sally E; Peters, Stephen P; Meyers, Deborah A; Bleecker, Eugene R

2014-06-01

Clinical cluster analysis from the Severe Asthma Research Program (SARP) identified 5 asthma subphenotypes that represent the severity spectrum of early-onset allergic asthma, late-onset severe asthma, and severe asthma with chronic obstructive pulmonary disease characteristics. Analysis of induced sputum from a subset of SARP subjects showed 4 sputum inflammatory cellular patterns. Subjects with concurrent increases in eosinophil (≥2%) and neutrophil (≥40%) percentages had characteristics of very severe asthma. To better understand interactions between inflammation and clinical subphenotypes, we integrated inflammatory cellular measures and clinical variables in a new cluster analysis. Participants in SARP who underwent sputum induction at 3 clinical sites were included in this analysis (n = 423). Fifteen variables, including clinical characteristics and blood and sputum inflammatory cell assessments, were selected using factor analysis for unsupervised cluster analysis. Four phenotypic clusters were identified. Cluster A (n = 132) and B (n = 127) subjects had mild-to-moderate early-onset allergic asthma with paucigranulocytic or eosinophilic sputum inflammatory cell patterns. In contrast, these inflammatory patterns were present in only 7% of cluster C (n = 117) and D (n = 47) subjects who had moderate-to-severe asthma with frequent health care use despite treatment with high doses of inhaled or oral corticosteroids and, in cluster D, reduced lung function. The majority of these subjects (>83%) had sputum neutrophilia either alone or with concurrent sputum eosinophilia. Baseline lung function and sputum neutrophil percentages were the most important variables determining cluster assignment. This multivariate approach identified 4 asthma subphenotypes representing the severity spectrum from mild-to-moderate allergic asthma with minimal or eosinophil-predominant sputum inflammation to moderate-to-severe asthma with neutrophil-predominant or mixed granulocytic inflammation. Published by Mosby, Inc.

Sputum neutrophils are associated with more severe asthma phenotypes using cluster analysis

PubMed Central

Moore, Wendy C.; Hastie, Annette T.; Li, Xingnan; Li, Huashi; Busse, William W.; Jarjour, Nizar N.; Wenzel, Sally E.; Peters, Stephen P.; Meyers, Deborah A.; Bleecker, Eugene R.

2013-01-01

Background Clinical cluster analysis from the Severe Asthma Research Program (SARP) identified five asthma subphenotypes that represent the severity spectrum of early onset allergic asthma, late onset severe asthma and severe asthma with COPD characteristics. Analysis of induced sputum from a subset of SARP subjects showed four sputum inflammatory cellular patterns. Subjects with concurrent increases in eosinophils (≥2%) and neutrophils (≥40%) had characteristics of very severe asthma. Objective To better understand interactions between inflammation and clinical subphenotypes we integrated inflammatory cellular measures and clinical variables in a new cluster analysis. Methods Participants in SARP at three clinical sites who underwent sputum induction were included in this analysis (n=423). Fifteen variables including clinical characteristics and blood and sputum inflammatory cell assessments were selected by factor analysis for unsupervised cluster analysis. Results Four phenotypic clusters were identified. Cluster A (n=132) and B (n=127) subjects had mild-moderate early onset allergic asthma with paucigranulocytic or eosinophilic sputum inflammatory cell patterns. In contrast, these inflammatory patterns were present in only 7% of Cluster C (n=117) and D (n=47) subjects who had moderate-severe asthma with frequent health care utilization despite treatment with high doses of inhaled or oral corticosteroids, and in Cluster D, reduced lung function. The majority these subjects (>83%) had sputum neutrophilia either alone or with concurrent sputum eosinophilia. Baseline lung function and sputum neutrophils were the most important variables determining cluster assignment. Conclusion This multivariate approach identified four asthma subphenotypes representing the severity spectrum from mild-moderate allergic asthma with minimal or eosinophilic predominant sputum inflammation to moderate-severe asthma with neutrophilic predominant or mixed granulocytic inflammation. PMID:24332216

Trajectories of acute low back pain: a latent class growth analysis.

PubMed

Downie, Aron S; Hancock, Mark J; Rzewuska, Magdalena; Williams, Christopher M; Lin, Chung-Wei Christine; Maher, Christopher G

2016-01-01

Characterising the clinical course of back pain by mean pain scores over time may not adequately reflect the complexity of the clinical course of acute low back pain. We analysed pain scores over 12 weeks for 1585 patients with acute low back pain presenting to primary care to identify distinct pain trajectory groups and baseline patient characteristics associated with membership of each cluster. This was a secondary analysis of the PACE trial that evaluated paracetamol for acute low back pain. Latent class growth analysis determined a 5 cluster model, which comprised 567 (35.8%) patients who recovered by week 2 (cluster 1, rapid pain recovery); 543 (34.3%) patients who recovered by week 12 (cluster 2, pain recovery by week 12); 222 (14.0%) patients whose pain reduced but did not recover (cluster 3, incomplete pain recovery); 167 (10.5%) patients whose pain initially decreased but then increased by week 12 (cluster 4, fluctuating pain); and 86 (5.4%) patients who experienced high-level pain for the whole 12 weeks (cluster 5, persistent high pain). Patients with longer pain duration were more likely to experience delayed recovery or nonrecovery. Belief in greater risk of persistence was associated with nonrecovery, but not delayed recovery. Higher pain intensity, longer duration, and workers' compensation were associated with persistent high pain, whereas older age and increased number of episodes were associated with fluctuating pain. Identification of discrete pain trajectory groups offers the potential to better manage acute low back pain.

Correlation and network analysis of global financial indices

NASA Astrophysics Data System (ADS)

Kumar, Sunil; Deo, Nivedita

2012-08-01

Random matrix theory (RMT) and network methods are applied to investigate the correlation and network properties of 20 financial indices. The results are compared before and during the financial crisis of 2008. In the RMT method, the components of eigenvectors corresponding to the second largest eigenvalue form two clusters of indices in the positive and negative directions. The components of these two clusters switch in opposite directions during the crisis. The network analysis uses the Fruchterman-Reingold layout to find clusters in the network of indices at different thresholds. At a threshold of 0.6, before the crisis, financial indices corresponding to the Americas, Europe, and Asia-Pacific form separate clusters. On the other hand, during the crisis at the same threshold, the American and European indices combine together to form a strongly linked cluster while the Asia-Pacific indices form a separate weakly linked cluster. If the value of the threshold is further increased to 0.9 then the European indices (France, Germany, and the United Kingdom) are found to be the most tightly linked indices. The structure of the minimum spanning tree of financial indices is more starlike before the crisis and it changes to become more chainlike during the crisis. The average linkage hierarchical clustering algorithm is used to find a clearer cluster structure in the network of financial indices. The cophenetic correlation coefficients are calculated and found to increase significantly, which indicates that the hierarchy increases during the financial crisis. These results show that there is substantial change in the structure of the organization of financial indices during a financial crisis.

Correlation and network analysis of global financial indices.

PubMed

Kumar, Sunil; Deo, Nivedita

2012-08-01

Random matrix theory (RMT) and network methods are applied to investigate the correlation and network properties of 20 financial indices. The results are compared before and during the financial crisis of 2008. In the RMT method, the components of eigenvectors corresponding to the second largest eigenvalue form two clusters of indices in the positive and negative directions. The components of these two clusters switch in opposite directions during the crisis. The network analysis uses the Fruchterman-Reingold layout to find clusters in the network of indices at different thresholds. At a threshold of 0.6, before the crisis, financial indices corresponding to the Americas, Europe, and Asia-Pacific form separate clusters. On the other hand, during the crisis at the same threshold, the American and European indices combine together to form a strongly linked cluster while the Asia-Pacific indices form a separate weakly linked cluster. If the value of the threshold is further increased to 0.9 then the European indices (France, Germany, and the United Kingdom) are found to be the most tightly linked indices. The structure of the minimum spanning tree of financial indices is more starlike before the crisis and it changes to become more chainlike during the crisis. The average linkage hierarchical clustering algorithm is used to find a clearer cluster structure in the network of financial indices. The cophenetic correlation coefficients are calculated and found to increase significantly, which indicates that the hierarchy increases during the financial crisis. These results show that there is substantial change in the structure of the organization of financial indices during a financial crisis.

The Clusters - Collaborative Models of Sustainable Regional Development

NASA Astrophysics Data System (ADS)

Mănescu, Gabriel; Kifor, Claudiu

2014-12-01

The clusters are the subject of actions and of whole series of documents issued by national and international organizations, and, based on experience, many authorities promote the idea that because of the clusters, competitiveness increases, the workforce specializes, regional businesses and economies grow. The present paper is meant to be an insight into the initiatives of forming clusters in Romania. Starting from a comprehensive analysis of the development potential offered by each region of economic development, we present the main types of clusters grouped according to fields of activity and their overall objectives

Topic modeling for cluster analysis of large biological and medical datasets

PubMed Central

2014-01-01

Background The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. Results In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Conclusion Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting that topic model-based methods could provide an analytic advancement in the analysis of large biological or medical datasets. PMID:25350106

Topic modeling for cluster analysis of large biological and medical datasets.

PubMed

Zhao, Weizhong; Zou, Wen; Chen, James J

2014-01-01

The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting that topic model-based methods could provide an analytic advancement in the analysis of large biological or medical datasets.

Technical Efficiency of Automotive Industry Cluster in Chennai

NASA Astrophysics Data System (ADS)

Bhaskaran, E.

2012-07-01

Chennai is also called as Detroit of India due to its automotive industry presence producing over 40 % of the India's vehicle and components. During 2001-2002, diagnostic study was conducted on the Automotive Component Industries (ACI) in Ambattur Industrial Estate, Chennai and in SWOT analysis it was found that it had faced problems on infrastructure, technology, procurement, production and marketing. In the year 2004-2005 under the cluster development approach (CDA), they formed Chennai auto cluster, under public private partnership concept, received grant from Government of India, Government of Tamil Nadu, Ambattur Municipality, bank loans and stake holders. This results development in infrastructure, technology, procurement, production and marketing interrelationships among ACI. The objective is to determine the correlation coefficient, regression equation, technical efficiency, peer weights, slack variables and return to scale of cluster before and after the CDA. The methodology adopted is collection of primary data from ACI and analyzing using data envelopment analysis (DEA) of input oriented Banker-Charnes-Cooper model. There is significant increase in correlation coefficient and the regression analysis reveals that for one percent increase in employment and net worth, the gross output increases significantly after the CDA. The DEA solver gives the technical efficiency of ACI by taking shift, employment, net worth as input data and quality, gross output and export ratio as output data. From the technical score and ranking of ACI, it is found that there is significant increase in technical efficiency of ACI when compared to CDA. The slack variables obtained clearly reveals the excess employment and net worth and no shortage of gross output. To conclude there is increase in technical efficiency of not only Chennai auto cluster in general but also Chennai auto components industries in particular.

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.

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.

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.

A genetic graph-based approach for partitional clustering.

PubMed

Menéndez, Héctor D; Barrero, David F; Camacho, David

2014-05-01

Clustering is one of the most versatile tools for data analysis. In the recent years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the spectral clustering (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments.

Effect of functionalization of boron nitride flakes by main group metal clusters on their optoelectronic properties

NASA Astrophysics Data System (ADS)

Chakraborty, Debdutta; Chattaraj, Pratim Kumar

2017-10-01

The possibility of functionalizing boron nitride flakes (BNFs) with some selected main group metal clusters, viz. OLi4, NLi5, CLi6, BLI7 and Al12Be, has been analyzed with the aid of density functional theory (DFT) based computations. Thermochemical as well as energetic considerations suggest that all the metal clusters interact with the BNF moiety in a favorable fashion. As a result of functionalization, the static (first) hyperpolarizability (β ) values of the metal cluster supported BNF moieties increase quite significantly as compared to that in the case of pristine BNF. Time dependent DFT analysis reveals that the metal clusters can lower the transition energies associated with the dominant electronic transitions quite significantly thereby enabling the metal cluster supported BNF moieties to exhibit significant non-linear optical activity. Moreover, the studied systems demonstrate broad band absorption capability spanning the UV-visible as well as infra-red domains. Energy decomposition analysis reveals that the electrostatic interactions principally stabilize the metal cluster supported BNF moieties.

Effect of functionalization of boron nitride flakes by main group metal clusters on their optoelectronic properties.

PubMed

Chakraborty, Debdutta; Chattaraj, Pratim Kumar

2017-10-25

The possibility of functionalizing boron nitride flakes (BNFs) with some selected main group metal clusters, viz. OLi 4 , NLi 5 , CLi 6 , BLI 7 and Al 12 Be, has been analyzed with the aid of density functional theory (DFT) based computations. Thermochemical as well as energetic considerations suggest that all the metal clusters interact with the BNF moiety in a favorable fashion. As a result of functionalization, the static (first) hyperpolarizability ([Formula: see text]) values of the metal cluster supported BNF moieties increase quite significantly as compared to that in the case of pristine BNF. Time dependent DFT analysis reveals that the metal clusters can lower the transition energies associated with the dominant electronic transitions quite significantly thereby enabling the metal cluster supported BNF moieties to exhibit significant non-linear optical activity. Moreover, the studied systems demonstrate broad band absorption capability spanning the UV-visible as well as infra-red domains. Energy decomposition analysis reveals that the electrostatic interactions principally stabilize the metal cluster supported BNF moieties.

Clustering of Dietary Patterns, Lifestyles, and Overweight among Spanish Children and Adolescents in the ANIBES Study

PubMed Central

Pérez-Rodrigo, Carmen; Gil, Ángel; González-Gross, Marcela; Ortega, Rosa M.; Serra-Majem, Lluis; Varela-Moreiras, Gregorio; Aranceta-Bartrina, Javier

2015-01-01

Weight gain has been associated with behaviors related to diet, sedentary lifestyle, and physical activity. We investigated dietary patterns and possible meaningful clustering of physical activity, sedentary behavior, and sleep time in Spanish children and adolescents and whether the identified clusters could be associated with overweight. Analysis was based on a subsample (n = 415) of the cross-sectional ANIBES study in Spain. We performed exploratory factor analysis and subsequent cluster analysis of dietary patterns, physical activity, sedentary behaviors, and sleep time. Logistic regression analysis was used to explore the association between the cluster solutions and overweight. Factor analysis identified four dietary patterns, one reflecting a profile closer to the traditional Mediterranean diet. Dietary patterns, physical activity behaviors, sedentary behaviors and sleep time on weekdays in Spanish children and adolescents clustered into two different groups. A low physical activity-poorer diet lifestyle pattern, which included a higher proportion of girls, and a high physical activity, low sedentary behavior, longer sleep duration, healthier diet lifestyle pattern. Although increased risk of being overweight was not significant, the Prevalence Ratios (PRs) for the low physical activity-poorer diet lifestyle pattern were >1 in children and in adolescents. The healthier lifestyle pattern included lower proportions of children and adolescents from low socioeconomic status backgrounds. PMID:26729155

Infrared spectroscopy reveals both qualitative and quantitative differences in equine subchondral bone during maturation

NASA Astrophysics Data System (ADS)

Kobrina, Yevgeniya; Isaksson, Hanna; Sinisaari, Miikka; Rieppo, Lassi; Brama, Pieter A.; van Weeren, René; Helminen, Heikki J.; Jurvelin, Jukka S.; Saarakkala, Simo

2010-11-01

The collagen phase in bone is known to undergo major changes during growth and maturation. The objective of this study is to clarify whether Fourier transform infrared (FTIR) microspectroscopy, coupled with cluster analysis, can detect quantitative and qualitative changes in the collagen matrix of subchondral bone in horses during maturation and growth. Equine subchondral bone samples (n = 29) from the proximal joint surface of the first phalanx are prepared from two sites subjected to different loading conditions. Three age groups are studied: newborn (0 days old), immature (5 to 11 months old), and adult (6 to 10 years old) horses. Spatial collagen content and collagen cross-link ratio are quantified from the spectra. Additionally, normalized second derivative spectra of samples are clustered using the k-means clustering algorithm. In quantitative analysis, collagen content in the subchondral bone increases rapidly between the newborn and immature horses. The collagen cross-link ratio increases significantly with age. In qualitative analysis, clustering is able to separate newborn and adult samples into two different groups. The immature samples display some nonhomogeneity. In conclusion, this is the first study showing that FTIR spectral imaging combined with clustering techniques can detect quantitative and qualitative changes in the collagen matrix of subchondral bone during growth and maturation.

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

PubMed

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

2015-01-01

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

Whole Blood Gene Expression Profiling Predicts Severe Morbidity and Mortality in Cystic Fibrosis: A 5-Year Follow-Up Study.

PubMed

Saavedra, Milene T; Quon, Bradley S; Faino, Anna; Caceres, Silvia M; Poch, Katie R; Sanders, Linda A; Malcolm, Kenneth C; Nichols, David P; Sagel, Scott D; Taylor-Cousar, Jennifer L; Leach, Sonia M; Strand, Matthew; Nick, Jerry A

2018-05-01

Cystic fibrosis pulmonary exacerbations accelerate pulmonary decline and increase mortality. Previously, we identified a 10-gene leukocyte panel measured directly from whole blood, which indicates response to exacerbation treatment. We hypothesized that molecular characteristics of exacerbations could also predict future disease severity. We tested whether a 10-gene panel measured from whole blood could identify patient cohorts at increased risk for severe morbidity and mortality, beyond standard clinical measures. Transcript abundance for the 10-gene panel was measured from whole blood at the beginning of exacerbation treatment (n = 57). A hierarchical cluster analysis of subjects based on their gene expression was performed, yielding four molecular clusters. An analysis of cluster membership and outcomes incorporating an independent cohort (n = 21) was completed to evaluate robustness of cluster partitioning of genes to predict severe morbidity and mortality. The four molecular clusters were analyzed for differences in forced expiratory volume in 1 second, C-reactive protein, return to baseline forced expiratory volume in 1 second after treatment, time to next exacerbation, and time to morbidity or mortality events (defined as lung transplant referral, lung transplant, intensive care unit admission for respiratory insufficiency, or death). Clustering based on gene expression discriminated between patient groups with significant differences in forced expiratory volume in 1 second, admission frequency, and overall morbidity and mortality. At 5 years, all subjects in cluster 1 (very low risk) were alive and well, whereas 90% of subjects in cluster 4 (high risk) had suffered a major event (P = 0.0001). In multivariable analysis, the ability of gene expression to predict clinical outcomes remained significant, despite adjustment for forced expiratory volume in 1 second, sex, and admission frequency. The robustness of gene clustering to categorize patients appropriately in terms of clinical characteristics, and short- and long-term clinical outcomes, remained consistent, even when adding in a secondary population with significantly different clinical outcomes. Whole blood gene expression profiling allows molecular classification of acute pulmonary exacerbations, beyond standard clinical measures, providing a predictive tool for identifying subjects at increased risk for mortality and disease progression.

[Study of the clinical phenotype of symptomatic chronic airways disease by hierarchical cluster analysis and two-step cluster analyses].

PubMed

Ning, P; Guo, Y F; Sun, T Y; Zhang, H S; Chai, D; Li, X M

2016-09-01

To study the distinct clinical phenotype of chronic airway diseases by hierarchical cluster analysis and two-step cluster analysis. A population sample of adult patients in Donghuamen community, Dongcheng district and Qinghe community, Haidian district, Beijing from April 2012 to January 2015, who had wheeze within the last 12 months, underwent detailed investigation, including a clinical questionnaire, pulmonary function tests, total serum IgE levels, blood eosinophil level and a peak flow diary. Nine variables were chosen as evaluating parameters, including pre-salbutamol forced expired volume in one second(FEV1)/forced vital capacity(FVC) ratio, pre-salbutamol FEV1, percentage of post-salbutamol change in FEV1, residual capacity, diffusing capacity of the lung for carbon monoxide/alveolar volume adjusted for haemoglobin level, peak expiratory flow(PEF) variability, serum IgE level, cumulative tobacco cigarette consumption (pack-years) and respiratory symptoms (cough and expectoration). Subjects' different clinical phenotype by hierarchical cluster analysis and two-step cluster analysis was identified. (1) Four clusters were identified by hierarchical cluster analysis. Cluster 1 was chronic bronchitis in smokers with normal pulmonary function. Cluster 2 was chronic bronchitis or mild chronic obstructive pulmonary disease (COPD) patients with mild airflow limitation. Cluster 3 included COPD patients with heavy smoking, poor quality of life and severe airflow limitation. Cluster 4 recognized atopic patients with mild airflow limitation, elevated serum IgE and clinical features of asthma. Significant differences were revealed regarding pre-salbutamol FEV1/FVC%, pre-salbutamol FEV1% pred, post-salbutamol change in FEV1%, maximal mid-expiratory flow curve(MMEF)% pred, carbon monoxide diffusing capacity per liter of alveolar(DLCO)/(VA)% pred, residual volume(RV)% pred, total serum IgE level, smoking history (pack-years), St.George's respiratory questionnaire(SGRQ) score, acute exacerbation in the past one year, PEF variability and allergic dermatitis (P<0.05). (2) Four clusters were also identified by two-step cluster analysis as followings, cluster 1, COPD patients with moderate to severe airflow limitation; cluster 2, asthma and COPD patients with heavy smoking, airflow limitation and increased airways reversibility; cluster 3, patients having less smoking and normal pulmonary function with wheezing but no chronic cough; cluster 4, chronic bronchitis patients with normal pulmonary function and chronic cough. Significant differences were revealed regarding gender distribution, respiratory symptoms, pre-salbutamol FEV1/FVC%, pre-salbutamol FEV1% pred, post-salbutamol change in FEV1%, MMEF% pred, DLCO/VA% pred, RV% pred, PEF variability, total serum IgE level, cumulative tobacco cigarette consumption (pack-years), and SGRQ score (P<0.05). By different cluster analyses, distinct clinical phenotypes of chronic airway diseases are identified. Thus, individualized treatments may guide doctors to provide based on different phenotypes.

Novel approach to classifying patients with pulmonary arterial hypertension using cluster analysis.

PubMed

Parikh, Kishan S; Rao, Youlan; Ahmad, Tariq; Shen, Kai; Felker, G Michael; Rajagopal, Sudarshan

2017-01-01

Pulmonary arterial hypertension (PAH) patients have distinct disease courses and responses to treatment, but current diagnostic and treatment schemes provide limited insight. We aimed to see if cluster analysis could distinguish clinical phenotypes in PAH. An unbiased cluster analysis was performed on 17 baseline clinical variables of PAH patients from the FREEDOM-M, FREEDOM-C, and FREEDOM-C2 randomized trials of oral treprostinil versus placebo. Participants were either treatment-naïve (FREEDOM-M) or on background therapy (FREEDOM-C, FREEDOM-C2). We tested for association of clusters with outcomes and interaction with respect to treatment. Primary outcome was 6-minute walking distance (6MWD) change. We included 966 participants with 12-week (FREEDOM-M) or 16-week (FREEDOM-C and FREEDOM-C2) follow-up. Four patient clusters were identified. Compared with Clusters 1 (n = 131) and 2 (n = 496), Clusters 3 (n = 246) and 4 (n = 93) patients were older, heavier, had worse baseline functional class, 6MWD, Borg Dyspnea Index, and fewer years since PAH diagnosis. Clusters also differed by PAH etiology and background therapies, but not gender or race. Mean treatment effect of oral treprostinil differed across Clusters 1-4 increased in a monotonic fashion (Cluster 1: 10.9 m; Cluster 2: 13.0 m; Cluster 3: 25.0 m; Cluster 4: 50.9 m; interaction P value = 0.048). We identified four distinct clusters of PAH patients based on common patient characteristics. Patients who were older, diagnosed with PAH for a shorter period, and had worse baseline symptoms and exercise capacity had the greatest response to oral treprostinil treatment.

Clustering of Health Behaviors and Cardiorespiratory Fitness Among U.S. Adolescents.

PubMed

Hartz, Jacob; Yingling, Leah; Ayers, Colby; Adu-Brimpong, Joel; Rivers, Joshua; Ahuja, Chaarushi; Powell-Wiley, Tiffany M

2018-05-01

Decreased cardiorespiratory fitness (CRF) is associated with an increased risk of cardiovascular disease. However, little is known how the interaction of diet, physical activity (PA), and sedentary time (ST) affects CRF among adolescents. By using a nationally representative sample of U.S. adolescents, we used cluster analysis to investigate the interactions of these behaviors with CRF. We hypothesized that distinct clustering patterns exist and that less healthy clusters are associated with lower CRF. We used 2003-2004 National Health and Nutrition Examination Survey data for persons aged 12-19 years (N = 1,225). PA and ST were measured objectively by an accelerometer, and the American Heart Association Healthy Diet Score quantified diet quality. Maximal oxygen consumption (V˙O 2 ​max) was measured by submaximal treadmill exercise test. We performed cluster analysis to identify sex-specific clustering of diet, PA, and ST. Adjusting for accelerometer wear time, age, body mass index, race/ethnicity, and the poverty-to-income ratio, we performed sex-stratified linear regression analysis to evaluate the association of cluster with V˙O 2 ​max. Three clusters were identified for girls and boys. For girls, there was no difference across clusters for age (p = .1), weight (p = .3), and BMI (p = .5), and no relationship between clusters and V˙O 2 ​max. For boys, the youngest cluster (p < .01) had three healthy behaviors, weighed less, and was associated with a higher V˙O 2 ​max compared with the two older clusters. We observed clustering of diet, PA, and ST in U.S. adolescents. Specific patterns were associated with lower V˙O 2 ​max for boys, suggesting that our clusters may help identify adolescent boys most in need of interventions. Published by Elsevier Inc.

Variation of heavy metals in recent sediments from Piratininga Lagoon (Brazil): interpretation of geochemical data with the aid of multivariate analysis

NASA Astrophysics Data System (ADS)

Huang, W.; Campredon, R.; Abrao, J. J.; Bernat, M.; Latouche, C.

1994-06-01

In the last decade, the Atlantic coast of south-eastern Brazil has been affected by increasing deforestation and anthropogenic effluents. Sediments in the coastal lagoons have recorded the process of such environmental change. Thirty-seven sediment samples from three cores in Piratininga Lagoon, Rio de Janeiro, were analyzed for their major components and minor element concentrations in order to examine geochemical characteristics and the depositional environment and to investigate the variation of heavy metals of environmental concern. Two multivariate analysis methods, principal component analysis and cluster analysis, were performed on the analytical data set to help visualize the sample clusters and the element associations. On the whole, the sediment samples from each core are similar and the sample clusters corresponding to the three cores are clearly separated, as a result of the different conditions of sedimentation. Some changes in the depositional environment are recognized using the results of multivariate analysis. The enrichment of Pb, Cu, and Zn in the upper parts of cores is in agreement with increasing anthropogenic influx (pollution).

Improvements on GPS Location Cluster Analysis for the Prediction of Large Carnivore Feeding Activities: Ground-Truth Detection Probability and Inclusion of Activity Sensor Measures

PubMed Central

Blecha, Kevin A.; Alldredge, Mat W.

2015-01-01

Animal space use studies using GPS collar technology are increasingly incorporating behavior based analysis of spatio-temporal data in order to expand inferences of resource use. GPS location cluster analysis is one such technique applied to large carnivores to identify the timing and location of feeding events. For logistical and financial reasons, researchers often implement predictive models for identifying these events. We present two separate improvements for predictive models that future practitioners can implement. Thus far, feeding prediction models have incorporated a small range of covariates, usually limited to spatio-temporal characteristics of the GPS data. Using GPS collared cougar (Puma concolor) we include activity sensor data as an additional covariate to increase prediction performance of feeding presence/absence. Integral to the predictive modeling of feeding events is a ground-truthing component, in which GPS location clusters are visited by human observers to confirm the presence or absence of feeding remains. Failing to account for sources of ground-truthing false-absences can bias the number of predicted feeding events to be low. Thus we account for some ground-truthing error sources directly in the model with covariates and when applying model predictions. Accounting for these errors resulted in a 10% increase in the number of clusters predicted to be feeding events. Using a double-observer design, we show that the ground-truthing false-absence rate is relatively low (4%) using a search delay of 2–60 days. Overall, we provide two separate improvements to the GPS cluster analysis techniques that can be expanded upon and implemented in future studies interested in identifying feeding behaviors of large carnivores. PMID:26398546

Analysis of EEG-fMRI data in focal epilepsy based on automated spike classification and Signal Space Projection.

PubMed

Liston, Adam D; De Munck, Jan C; Hamandi, Khalid; Laufs, Helmut; Ossenblok, Pauly; Duncan, John S; Lemieux, Louis

2006-07-01

Simultaneous acquisition of EEG and fMRI data enables the investigation of the hemodynamic correlates of interictal epileptiform discharges (IEDs) during the resting state in patients with epilepsy. This paper addresses two issues: (1) the semi-automation of IED classification in statistical modelling for fMRI analysis and (2) the improvement of IED detection to increase experimental fMRI efficiency. For patients with multiple IED generators, sensitivity to IED-correlated BOLD signal changes can be improved when the fMRI analysis model distinguishes between IEDs of differing morphology and field. In an attempt to reduce the subjectivity of visual IED classification, we implemented a semi-automated system, based on the spatio-temporal clustering of EEG events. We illustrate the technique's usefulness using EEG-fMRI data from a subject with focal epilepsy in whom 202 IEDs were visually identified and then clustered semi-automatically into four clusters. Each cluster of IEDs was modelled separately for the purpose of fMRI analysis. This revealed IED-correlated BOLD activations in distinct regions corresponding to three different IED categories. In a second step, Signal Space Projection (SSP) was used to project the scalp EEG onto the dipoles corresponding to each IED cluster. This resulted in 123 previously unrecognised IEDs, the inclusion of which, in the General Linear Model (GLM), increased the experimental efficiency as reflected by significant BOLD activations. We have also shown that the detection of extra IEDs is robust in the face of fluctuations in the set of visually detected IEDs. We conclude that automated IED classification can result in more objective fMRI models of IEDs and significantly increased sensitivity.

Influence of exposure differences on city-to-city heterogeneity ...

EPA Pesticide Factsheets

Multi-city population-based epidemiological studies have observed heterogeneity between city-specific fine particulate matter (PM2.5)-mortality effect estimates. These studies typically use ambient monitoring data as a surrogate for exposure leading to potential exposure misclassification. The level of exposure misclassification can differ by city affecting the observed health effect estimate. The objective of this analysis is to evaluate whether previously developed residential infiltration-based city clusters can explain city-to-city heterogeneity in PM2.5 mortality risk estimates. In a prior paper 94 cities were clustered based on residential infiltration factors (e.g. home age/size, prevalence of air conditioning (AC)), resulting in 5 clusters. For this analysis, the association between PM2.5 and all-cause mortality was first determined in 77 cities across the United States for 2001–2005. Next, a second stage analysis was conducted evaluating the influence of cluster assignment on heterogeneity in the risk estimates. Associations between a 2-day (lag 0–1 days) moving average of PM2.5 concentrations and non-accidental mortality were determined for each city. Estimated effects ranged from −3.2 to 5.1% with a pooled estimate of 0.33% (95% CI: 0.13, 0.53) increase in mortality per 10 μg/m3 increase in PM2.5. The second stage analysis determined that cluster assignment was marginally significant in explaining the city-to-city heterogeneity. The health effe

Cluster analysis of sputum cytokine-high profiles reveals diversity in T(h)2-high asthma patients.

PubMed

Seys, Sven F; Scheers, Hans; Van den Brande, Paul; Marijsse, Gudrun; Dilissen, Ellen; Van Den Bergh, Annelies; Goeminne, Pieter C; Hellings, Peter W; Ceuppens, Jan L; Dupont, Lieven J; Bullens, Dominique M A

2017-02-23

Asthma is characterized by a heterogeneous inflammatory profile and can be subdivided into T(h)2-high and T(h)2-low airway inflammation. Profiling of a broader panel of airway cytokines in large unselected patient cohorts is lacking. Patients (n = 205) were defined as being "cytokine-low/high" if sputum mRNA expression of a particular cytokine was outside the respective 10 th /90 th percentile range of the control group (n = 80). Unsupervised hierarchical clustering was used to determine clusters based on sputum cytokine profiles. Half of patients (n = 108; 52.6%) had a classical T(h)2-high ("IL-4-, IL-5- and/or IL-13-high") sputum cytokine profile. Unsupervised cluster analysis revealed 5 clusters. Patients with an "IL-4- and/or IL-13-high" pattern surprisingly did not cluster but were equally distributed among the 5 clusters. Patients with an "IL-5-, IL-17A-/F- and IL-25- high" profile were restricted to cluster 1 (n = 24) with increased sputum eosinophil as well as neutrophil counts and poor lung function parameters at baseline and 2 years later. Four other clusters were identified: "IL-5-high or IL-10-high" (n = 16), "IL-6-high" (n = 8), "IL-22-high" (n = 25). Cluster 5 (n = 132) consists of patients without "cytokine-high" pattern or patients with only high IL-4 and/or IL-13. We identified 5 unique asthma molecular phenotypes by biological clustering. Type 2 cytokines cluster with non-type 2 cytokines in 4 out of 5 clusters. Unsupervised analysis thus not supports a priori type 2 versus non-type 2 molecular phenotypes. www.clinicaltrials.gov NCT01224938. Registered 18 October 2010.

Generating a Magellanic star cluster catalog with ASteCA

NASA Astrophysics Data System (ADS)

Perren, G. I.; Piatti, A. E.; Vázquez, R. A.

2016-08-01

An increasing number of software tools have been employed in the recent years for the automated or semi-automated processing of astronomical data. The main advantages of using these tools over a standard by-eye analysis include: speed (particularly for large databases), homogeneity, reproducibility, and precision. At the same time, they enable a statistically correct study of the uncertainties associated with the analysis, in contrast with manually set errors, or the still widespread practice of simply not assigning errors. We present a catalog comprising 210 star clusters located in the Large and Small Magellanic Clouds, observed with Washington photometry. Their fundamental parameters were estimated through an homogeneous, automatized and completely unassisted process, via the Automated Stellar Cluster Analysis package ( ASteCA). Our results are compared with two types of studies on these clusters: one where the photometry is the same, and another where the photometric system is different than that employed by ASteCA.

Geographic atrophy phenotype identification by cluster analysis.

PubMed

Monés, Jordi; Biarnés, Marc

2018-03-01

To identify ocular phenotypes in patients with geographic atrophy secondary to age-related macular degeneration (GA) using a data-driven cluster analysis. This was a retrospective analysis of data from a prospective, natural history study of patients with GA who were followed for ≥6 months. Cluster analysis was used to identify subgroups within the population based on the presence of several phenotypic features: soft drusen, reticular pseudodrusen (RPD), primary foveal atrophy, increased fundus autofluorescence (FAF), greyish FAF appearance and subfoveal choroidal thickness (SFCT). A comparison of features between the subgroups was conducted, and a qualitative description of the new phenotypes was proposed. The atrophy growth rate between phenotypes was then compared. Data were analysed from 77 eyes of 77 patients with GA. Cluster analysis identified three groups: phenotype 1 was characterised by high soft drusen load, foveal atrophy and slow growth; phenotype 3 showed high RPD load, extrafoveal and greyish FAF appearance and thin SFCT; the characteristics of phenotype 2 were midway between phenotypes 1 and 3. Phenotypes differed in all measured features (p≤0.013), with decreases in the presence of soft drusen, foveal atrophy and SFCT seen from phenotypes 1 to 3 and corresponding increases in high RPD load, high FAF and greyish FAF appearance. Atrophy growth rate differed between phenotypes 1, 2 and 3 (0.63, 1.91 and 1.73 mm 2 /year, respectively, p=0.0005). Cluster analysis identified three distinct phenotypes in GA. One of them showed a particularly slow growth pattern. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Use of multiple cluster analysis methods to explore the validity of a community outcomes concept map.

PubMed

Orsi, Rebecca

2017-02-01

Concept mapping is now a commonly-used technique for articulating and evaluating programmatic outcomes. However, research regarding validity of knowledge and outcomes produced with concept mapping is sparse. The current study describes quantitative validity analyses using a concept mapping dataset. We sought to increase the validity of concept mapping evaluation results by running multiple cluster analysis methods and then using several metrics to choose from among solutions. We present four different clustering methods based on analyses using the R statistical software package: partitioning around medoids (PAM), fuzzy analysis (FANNY), agglomerative nesting (AGNES) and divisive analysis (DIANA). We then used the Dunn and Davies-Bouldin indices to assist in choosing a valid cluster solution for a concept mapping outcomes evaluation. We conclude that the validity of the outcomes map is high, based on the analyses described. Finally, we discuss areas for further concept mapping methods research. Copyright © 2016 Elsevier Ltd. All rights reserved.

Cluster analysis of phytoplankton data collected from the National Stream Quality Accounting Network in the Tennessee River basin, 1974-81

USGS Publications Warehouse

Stephens, D.W.; Wangsgard, J.B.

1988-01-01

A computer program, Numerical Taxonomy System of Multivariate Statistical Programs (NTSYS), was used with interfacing software to perform cluster analyses of phytoplankton data stored in the biological files of the U.S. Geological Survey. The NTSYS software performs various types of statistical analyses and is capable of handling a large matrix of data. Cluster analyses were done on phytoplankton data collected from 1974 to 1981 at four national Stream Quality Accounting Network stations in the Tennessee River basin. Analysis of the changes in clusters of phytoplankton genera indicated possible changes in the water quality of the French Broad River near Knoxville, Tennessee. At this station, the most common diatom groups indicated a shift in dominant forms with some of the less common diatoms being replaced by green and blue-green algae. There was a reduction in genera variability between 1974-77 and 1979-81 sampling periods. Statistical analysis of chloride and dissolved solids confirmed that concentrations of these substances were smaller in 1974-77 than in 1979-81. At Pickwick Landing Dam, the furthest downstream station used in the study, there was an increase in the number of genera of ' rare ' organisms with time. The appearance of two groups of green and blue-green algae indicated that an increase in temperature or nutrient concentrations occurred from 1974 to 1981, but this could not be confirmed using available water quality data. Associations of genera forming the phytoplankton communities at three stations on the Tennessee River were found to be seasonal. Nodal analysis of combined data from all four stations used in the study did not identify any seasonal or temporal patterns during 1974-81. Cluster analysis using the NYSYS programs was effective in reducing the large phytoplankton data set to a manageable size and provided considerable insight into the structure of phytoplankton communities in the Tennessee River basin. Problems encountered using cluster analysis were the subjectivity introduced in the definition of meaningful clusters, and the lack of taxonomic identification to the species level. (Author 's abstract)

A Gender Bias Habit-Breaking Intervention Led to Increased Hiring of Female Faculty in STEMM Departments.

PubMed

Devine, Patricia G; Forscher, Patrick S; Cox, William T L; Kaatz, Anna; Sheridan, Jennifer; Carnes, Molly

2017-11-01

Addressing the underrepresentation of women in science is a top priority for many institutions, but the majority of efforts to increase representation of women are neither evidence-based nor rigorously assessed. One exception is the gender bias habit-breaking intervention (Carnes et al., 2015), which, in a cluster-randomized trial involving all but two departmental clusters ( N = 92) in the 6 STEMM focused schools/colleges at the University of Wisconsin - Madison, led to increases in gender bias awareness and self-efficacy to promote gender equity in academic science departments. Following this initial success, the present study compares, in a preregistered analysis, hiring rates of new female faculty pre- and post-manipulation. Whereas the proportion of women hired by control departments remained stable over time, the proportion of women hired by intervention departments increased by an estimated 18 percentage points ( OR = 2.23, d OR = 0.34). Though the preregistered analysis did not achieve conventional levels of statistical significance ( p < 0.07), our study has a hard upper limit on statistical power, as the cluster-randomized trial has a maximum sample size of 92 departmental clusters. These patterns have undeniable practical significance for the advancement of women in science, and provide promising evidence that psychological interventions can facilitate gender equity and diversity.

Oxygen Vacancy Linear Clustering in a Perovskite Oxide

DOE PAGES

Eom, Kitae; Choi, Euiyoung; Choi, Minsu; ...

2017-07-14

Oxygen vacancies have been implicitly assumed isolated ones, and understanding oxide materials possibly containing oxygen vacancies remains elusive within the scheme of the isolated vacancies, although the oxygen vacancies have been playing a decisive role in oxide materials. We report the presence of oxygen vacancy linear clusters and their orientation along a specific crystallographic direction in SrTiO 3, a representative of a perovskite oxide. The presence of the linear clusters and associated electron localization was revealed by an electronic structure represented in the increase in the Ti 2+ valence state or corresponding Ti 3d 2 electronic configuration along with divacancymore » cluster model analysis and transport measurement. The orientation of the linear clusters along the [001] direction in perovskite SrTiO 3 was verified by further X-ray diffuse scattering analysis. And because SrTiO 3 is an archetypical perovskite oxide, the vacancy linear clustering with the specific aligned direction and electron localization can be extended to a wide variety of the perovskite oxides.« less

Oxygen Vacancy Linear Clustering in a Perovskite Oxide

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

Eom, Kitae; Choi, Euiyoung; Choi, Minsu

Oxygen vacancies have been implicitly assumed isolated ones, and understanding oxide materials possibly containing oxygen vacancies remains elusive within the scheme of the isolated vacancies, although the oxygen vacancies have been playing a decisive role in oxide materials. We report the presence of oxygen vacancy linear clusters and their orientation along a specific crystallographic direction in SrTiO 3, a representative of a perovskite oxide. The presence of the linear clusters and associated electron localization was revealed by an electronic structure represented in the increase in the Ti 2+ valence state or corresponding Ti 3d 2 electronic configuration along with divacancymore » cluster model analysis and transport measurement. The orientation of the linear clusters along the [001] direction in perovskite SrTiO 3 was verified by further X-ray diffuse scattering analysis. And because SrTiO 3 is an archetypical perovskite oxide, the vacancy linear clustering with the specific aligned direction and electron localization can be extended to a wide variety of the perovskite oxides.« less

Variable number of tandem repeats and pulsed-field gel electrophoresis cluster analysis of enterohemorrhagic Escherichia coli serovar O157 strains.

PubMed

Yokoyama, Eiji; Uchimura, Masako

2007-11-01

Ninety-five enterohemorrhagic Escherichia coli serovar O157 strains, including 30 strains isolated from 13 intrafamily outbreaks and 14 strains isolated from 3 mass outbreaks, were studied by pulsed-field gel electrophoresis (PFGE) and variable number of tandem repeats (VNTR) typing, and the resulting data were subjected to cluster analysis. Cluster analysis of the VNTR typing data revealed that 57 (60.0%) of 95 strains, including all epidemiologically linked strains, formed clusters with at least 95% similarity. Cluster analysis of the PFGE patterns revealed that 67 (70.5%) of 95 strains, including all but 1 of the epidemiologically linked strains, formed clusters with 90% similarity. The number of epidemiologically unlinked strains forming clusters was significantly less by VNTR cluster analysis than by PFGE cluster analysis. The congruence value between PFGE and VNTR cluster analysis was low and did not show an obvious correlation. With two-step cluster analysis, the number of clustered epidemiologically unlinked strains by PFGE cluster analysis that were divided by subsequent VNTR cluster analysis was significantly higher than the number by VNTR cluster analysis that were divided by subsequent PFGE cluster analysis. These results indicate that VNTR cluster analysis is more efficient than PFGE cluster analysis as an epidemiological tool to trace the transmission of enterohemorrhagic E. coli O157.

Cluster-specific small airway modeling for imaging-based CFD analysis of pulmonary air flow and particle deposition in COPD smokers

NASA Astrophysics Data System (ADS)

Haghighi, Babak; Choi, Jiwoong; Choi, Sanghun; Hoffman, Eric A.; Lin, Ching-Long

2017-11-01

Accurate modeling of small airway diameters in patients with chronic obstructive pulmonary disease (COPD) is a crucial step toward patient-specific CFD simulations of regional airflow and particle transport. We proposed to use computed tomography (CT) imaging-based cluster membership to identify structural characteristics of airways in each cluster and use them to develop cluster-specific airway diameter models. We analyzed 284 COPD smokers with airflow limitation, and 69 healthy controls. We used multiscale imaging-based cluster analysis (MICA) to classify smokers into 4 clusters. With representative cluster patients and healthy controls, we performed multiple regressions to quantify variation of airway diameters by generation as well as by cluster. The cluster 2 and 4 showed more diameter decrease as generation increases than other clusters. The cluster 4 had more rapid decreases of airway diameters in the upper lobes, while cluster 2 in the lower lobes. We then used these regression models to estimate airway diameters in CT unresolved regions to obtain pressure-volume hysteresis curves using a 1D resistance model. These 1D flow solutions can be used to provide the patient-specific boundary conditions for 3D CFD simulations in COPD patients. Support for this study was provided, in part, by NIH Grants U01-HL114494, R01-HL112986 and S10-RR022421.

Cause-specific mortality trends in The Netherlands, 1875-1992: a formal analysis of the epidemiologic transition.

PubMed

Wolleswinkel-van den Bosch, J H; Looman, C W; Van Poppel, F W; Mackenbach, J P

1997-08-01

The objective of this study is to produce a detailed yet robust description of the epidemiologic transition in The Netherlands. National mortality data on sex, age, cause of death and calendar year (1875-1992) were extracted from official publications. For the entire period, 27 causes of death could be distinguished, while 65 causes (nested within the 27) could be studied from 1901 onwards. Cluster analysis was used to determine groups of causes of death with similar trend curves over a period of time with respect to age- and sex-standardized mortality rates. With respect to the 27 causes, three important clusters were found: (1) infectious diseases which declined rapidly in the late 19th century (e.g. typhoid fever), (2) infectious diseases which showed a less precipitous decline (e.g. respiratory tuberculosis), and (3) non-infectious diseases which showed an increasing trend during most of the period 1875-1992 (e.g. cancer). The 65 causes provided more detail. Seven important clusters were found: four consisted mainly of infectious diseases, including a new cluster that declined rapidly after the Second World War (WW2) (e.g. acute bronchitis/influenza) and a new cluster showing an increasing trend in the 1920s and 1930s before declining in the years thereafter (e.g. appendicitis). Three clusters mainly contained non-infectious diseases, including a new one that declined from 1900 onwards (e.g. cancer of the stomach) and a new one that increased until WW2 but declined thereafter (e.g. chronic rheumatic heart disease). The results suggest that the conventional interpretation of the epidemiologic transition, which assumes a uniform decline of infectious diseases and a uniform increase of non-infectious diseases, needs to be modified.

Application of Geostatistical Methods and Machine Learning for spatio-temporal Earthquake Cluster Analysis

NASA Astrophysics Data System (ADS)

Schaefer, A. M.; Daniell, J. E.; Wenzel, F.

2014-12-01

Earthquake clustering tends to be an increasingly important part of general earthquake research especially in terms of seismic hazard assessment and earthquake forecasting and prediction approaches. The distinct identification and definition of foreshocks, aftershocks, mainshocks and secondary mainshocks is taken into account using a point based spatio-temporal clustering algorithm originating from the field of classic machine learning. This can be further applied for declustering purposes to separate background seismicity from triggered seismicity. The results are interpreted and processed to assemble 3D-(x,y,t) earthquake clustering maps which are based on smoothed seismicity records in space and time. In addition, multi-dimensional Gaussian functions are used to capture clustering parameters for spatial distribution and dominant orientations. Clusters are further processed using methodologies originating from geostatistics, which have been mostly applied and developed in mining projects during the last decades. A 2.5D variogram analysis is applied to identify spatio-temporal homogeneity in terms of earthquake density and energy output. The results are mitigated using Kriging to provide an accurate mapping solution for clustering features. As a case study, seismic data of New Zealand and the United States is used, covering events since the 1950s, from which an earthquake cluster catalogue is assembled for most of the major events, including a detailed analysis of the Landers and Christchurch sequences.

Bayesian Analysis and Characterization of Multiple Populations in Galactic Globular Clusters

NASA Astrophysics Data System (ADS)

Wagner-Kaiser, Rachel A.; Stenning, David; Sarajedini, Ata; von Hippel, Ted; van Dyk, David A.; Robinson, Elliot; Stein, Nathan; Jefferys, William H.; BASE-9, HST UVIS Globular Cluster Treasury Program

2017-01-01

Globular clusters have long been important tools to unlock the early history of galaxies. Thus, it is crucial we understand the formation and characteristics of the globular clusters (GCs) themselves. Historically, GCs were thought to be simple and largely homogeneous populations, formed via collapse of a single molecular cloud. However, this classical view has been overwhelmingly invalidated by recent work. It is now clear that the vast majority of globular clusters in our Galaxy host two or more chemically distinct populations of stars, with variations in helium and light elements at discrete abundance levels. No coherent story has arisen that is able to fully explain the formation of multiple populations in globular clusters nor the mechanisms that drive stochastic variations from cluster to cluster.We use Cycle 21 Hubble Space Telescope (HST) observations and HST archival ACS Treasury observations of 30 Galactic Globular Clusters to characterize two distinct stellar populations. A sophisticated Bayesian technique is employed to simultaneously sample the joint posterior distribution of age, distance, and extinction for each cluster, as well as unique helium values for two populations within each cluster and the relative proportion of those populations. We find the helium differences among the two populations in the clusters fall in the range of 0.04 to 0.11. Because adequate models varying in CNO are not presently available, we view these spreads as upper limits and present them with statistical rather than observational uncertainties. Evidence supports previous studies suggesting an increase in helium content concurrent with increasing mass of the cluster. We also find that the proportion of the first population of stars increases with mass. Our results are examined in the context of proposed globular cluster formation scenarios.

Upgrading of the LGD cluster at JINR to support DLNP experiments

NASA Astrophysics Data System (ADS)

Bednyakov, I. V.; Dolbilov, A. G.; Ivanov, Yu. P.

2017-01-01

Since its construction in 2005, the Computing Cluster of the Dzhelepov Laboratory of Nuclear Problems has been mainly used to perform calculations (data analysis, simulation, etc.) for various scientific collaborations in which DLNP scientists take an active part. The Cluster also serves to train specialists. Much has changed in the past decades, and the necessity has arisen to upgrade the cluster, increasing its power and replacing the outdated equipment to maintain its reliability and modernity. In this work we describe the experience of performing this upgrading, which can be helpful for system administrators to put new equipment for clusters of this type into operation quickly and efficiently.

A Bimodal Hybrid Model for Time-Dependent Probabilistic Seismic Hazard Analysis

NASA Astrophysics Data System (ADS)

Yaghmaei-Sabegh, Saman; Shoaeifar, Nasser; Shoaeifar, Parva

2018-03-01

The evaluation of evidence provided by geological studies and historical catalogs indicates that in some seismic regions and faults, multiple large earthquakes occur in cluster. Then, the occurrences of large earthquakes confront with quiescence and only the small-to-moderate earthquakes take place. Clustering of large earthquakes is the most distinguishable departure from the assumption of constant hazard of random occurrence of earthquakes in conventional seismic hazard analysis. In the present study, a time-dependent recurrence model is proposed to consider a series of large earthquakes that occurs in clusters. The model is flexible enough to better reflect the quasi-periodic behavior of large earthquakes with long-term clustering, which can be used in time-dependent probabilistic seismic hazard analysis with engineering purposes. In this model, the time-dependent hazard results are estimated by a hazard function which comprises three parts. A decreasing hazard of last large earthquake cluster and an increasing hazard of the next large earthquake cluster, along with a constant hazard of random occurrence of small-to-moderate earthquakes. In the final part of the paper, the time-dependent seismic hazard of the New Madrid Seismic Zone at different time intervals has been calculated for illustrative purpose.

Space-time analysis of pneumonia hospitalisations in the Netherlands.

PubMed

Benincà, Elisa; van Boven, Michiel; Hagenaars, Thomas; van der Hoek, Wim

2017-01-01

Community acquired pneumonia is a major global public health problem. In the Netherlands there are 40,000-50,000 hospital admissions for pneumonia per year. In the large majority of these hospital admissions the etiologic agent is not determined and a real-time surveillance system is lacking. Localised and temporal increases in hospital admissions for pneumonia are therefore only detected retrospectively and the etiologic agents remain unknown. Here, we perform spatio-temporal analyses of pneumonia hospital admission data in the Netherlands. To this end, we scanned for spatial clusters on yearly and seasonal basis, and applied wavelet cluster analysis on the time series of five main regions. The pneumonia hospital admissions show strong clustering in space and time superimposed on a regular yearly cycle with high incidence in winter and low incidence in summer. Cluster analysis reveals a heterogeneous pattern, with most significant clusters occurring in the western, highly urbanised, and in the eastern, intensively farmed, part of the Netherlands. Quantitatively, the relative risk (RR) of the significant clusters for the age-standardised incidence varies from a minimum of 1.2 to a maximum of 2.2. We discuss possible underlying causes for the patterns observed, such as variations in air pollution.

Combinations of elevated tissue miRNA-17-92 cluster expression and serum prostate-specific antigen as potential diagnostic biomarkers for prostate cancer.

PubMed

Feng, Sujuan; Qian, Xiaosong; Li, Han; Zhang, Xiaodong

2017-12-01

The aim of the present study was to investigate the effectiveness of the miR-17-92 cluster as a disease progression marker in prostate cancer (PCa). Reverse transcription-quantitative polymerase chain reaction analysis was used to detect the microRNA (miR)-17-92 cluster expression levels in tissues from patients with PCa or benign prostatic hyperplasia (BPH), in addition to in PCa and BPH cell lines. Spearman correlation was used for comparison and estimation of correlations between miRNA expression levels and clinicopathological characteristics such as the Gleason score and prostate-specific antigen (PSA). Receiver operating curve (ROC) analysis was performed for evaluation of specificity and sensitivity of miR-17-92 cluster expression levels for discriminating patients with PCa from patients with BPH. Kaplan-Meier analysis was plotted to investigate the predictive potential of miR-17-92 cluster for PCa biochemical recurrence. Expression of the majority of miRNAs in the miR-17-92 cluster was identified to be significantly increased in PCa tissues and cell lines. Bivariate correlation analysis indicated that the high expression of unregulated miRNAs was positively correlated with Gleason grade, but had no significant association with PSA. ROC curves demonstrated that high expression of miR-17-92 cluster predicted a higher diagnostic accuracy compared with PSA. Improved discriminating quotients were observed when combinations of unregulated miRNAs with PSA were used. Survival analysis confirmed a high combined miRNA score of miR-17-92 cluster was associated with shorter biochemical recurrence interval. miR-17-92 cluster could be a potential diagnostic and prognostic biomarker for PCa, and the combination of the miR-17-92 cluster and serum PSA may enhance the accuracy for diagnosis of PCa.

An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China.

PubMed

Zou, Hui; Zou, Zhihong; Wang, Xiaojing

2015-11-12

The increase and the complexity of data caused by the uncertain environment is today's reality. In order to identify water quality effectively and reliably, this paper presents a modified fast clustering algorithm for water quality analysis. The algorithm has adopted a varying weights K-means cluster algorithm to analyze water monitoring data. The varying weights scheme was the best weighting indicator selected by a modified indicator weight self-adjustment algorithm based on K-means, which is named MIWAS-K-means. The new clustering algorithm avoids the margin of the iteration not being calculated in some cases. With the fast clustering analysis, we can identify the quality of water samples. The algorithm is applied in water quality analysis of the Haihe River (China) data obtained by the monitoring network over a period of eight years (2006-2013) with four indicators at seven different sites (2078 samples). Both the theoretical and simulated results demonstrate that the algorithm is efficient and reliable for water quality analysis of the Haihe River. In addition, the algorithm can be applied to more complex data matrices with high dimensionality.

Psychological profiles derived by cluster analysis of Minnesota Multiphasic Personality Inventory and long term clinical outcome after coronary artery by pass grafting.

PubMed

Modica, Maddalena; Carabalona, Roberta; Spezzaferri, Rosa; Tavanelli, Monica; Torri, A; Ripamonti, Vittorino; Castiglioni, Paolo; De Maria, Renata; Ferratini, Maurizio

2012-03-01

To evaluate the psychological characteristics of coronary heart disease (CHD) patients after coronary artery bypass grafting (CABG) by cluster analysis of Minnesota Multiphasic Personality Inventory (MMPI-2) questionnaires and to assess the impact of the profiles obtained on long-term outcome. 229 CHD patients admitted to cardiac rehabilitation filled in self-administered MMPI-2 questionnaires early after CABG. We assessed the relation between MMPI-2 profiles derived by cluster analysis, clinical characteristics and outcome at 3-year follow-up. Among the 215 patients (76% men, median age 66 years) with valid criteria in control scales, we identified 3 clusters (G) with homogenous psychological characteristics: G1 patients (N = 75) presented somatoform complaints but overall minimal psychological distress. G2 patients (N=72) presented type D personality traits. G3 subjects (N=68) showed a trend to cynicism, mild increases in anger, social introversion and hostility. Clusters overlapped for clinical characteristics such as smoking (G1 21%, G2 24%, G3 24%, p ns), previous myocardial infarction (G1 43%, G2 47%, G3 49% p ns), LV ejection fraction (G1 60 [51-60]; G2 58 [49-60]; G3 60 [55-60], p ns), 3-vessel-disease prevalence (G1 69%, G2 65%, G3 71%, p ns). Three-year event rates were comparable (G1 15%; G2 18%; G3 15%) and Kaplan-Meier curves overlapped among clusters (p ns). After CABG, the interpretation of MMPI-2 by cluster analysis is useful for the psychological and personological diagnosis to direct psychological assistance. Conversely, results from cluster analysis of MMPI-2 do not seem helpful to the clinician to predict long term outcome.

Cardiovascular reactivity patterns and pathways to hypertension: a multivariate cluster analysis.

PubMed

Brindle, R C; Ginty, A T; Jones, A; Phillips, A C; Roseboom, T J; Carroll, D; Painter, R C; de Rooij, S R

2016-12-01

Substantial evidence links exaggerated mental stress induced blood pressure reactivity to future hypertension, but the results for heart rate reactivity are less clear. For this reason multivariate cluster analysis was carried out to examine the relationship between heart rate and blood pressure reactivity patterns and hypertension in a large prospective cohort (age range 55-60 years). Four clusters emerged with statistically different systolic and diastolic blood pressure and heart rate reactivity patterns. Cluster 1 was characterised by a relatively exaggerated blood pressure and heart rate response while the blood pressure and heart rate responses of cluster 2 were relatively modest and in line with the sample mean. Cluster 3 was characterised by blunted cardiovascular stress reactivity across all variables and cluster 4, by an exaggerated blood pressure response and modest heart rate response. Membership to cluster 4 conferred an increased risk of hypertension at 5-year follow-up (hazard ratio=2.98 (95% CI: 1.50-5.90), P<0.01) that survived adjustment for a host of potential confounding variables. These results suggest that the cardiac reactivity plays a potentially important role in the link between blood pressure reactivity and hypertension and support the use of multivariate approaches to stress psychophysiology.

Robustness of cluster synchronous patterns in small-world networks with inter-cluster co-competition balance

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

Zhang, Jianbao; Ma, Zhongjun, E-mail: mzj1234402@163.com; Chen, Guanrong

All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding ormore » deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations.« less

Robustness of cluster synchronous patterns in small-world networks with inter-cluster co-competition balance

NASA Astrophysics Data System (ADS)

Zhang, Jianbao; Ma, Zhongjun; Chen, Guanrong

2014-06-01

All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding or deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations.

Spatiotemporal analysis of the agricultural drought risk in Heilongjiang Province, China

NASA Astrophysics Data System (ADS)

Pei, Wei; Fu, Qiang; Liu, Dong; Li, Tian-xiao; Cheng, Kun; Cui, Song

2017-06-01

Droughts are natural disasters that pose significant threats to agricultural production as well as living conditions, and a spatial-temporal difference analysis of agricultural drought risk can help determine the spatial distribution and temporal variation of the drought risk within a region. Moreover, this type of analysis can provide a theoretical basis for the identification, prevention, and mitigation of drought disasters. In this study, the overall dispersion and local aggregation of projection points were based on research by Friedman and Tukey (IEEE Trans on Computer 23:881-890, 1974). In this work, high-dimensional samples were clustered by cluster analysis. The clustering results were represented by the clustering matrix, which determined the local density in the projection index. This method avoids the problem of determining a cutoff radius. An improved projection pursuit model is proposed that combines cluster analysis and the projection pursuit model, which offer advantages for classification and assessment, respectively. The improved model was applied to analyze the agricultural drought risk of 13 cities in Heilongjiang Province over 6 years (2004, 2006, 2008, 2010, 2012, and 2014). The risk of an agricultural drought disaster was characterized by 14 indicators and the following four aspects: hazard, exposure, sensitivity, and resistance capacity. The spatial distribution and temporal variation characteristics of the agricultural drought risk in Heilongjiang Province were analyzed. The spatial distribution results indicated that Suihua, Qigihar, Daqing, Harbin, and Jiamusi are located in high-risk areas, Daxing'anling and Yichun are located in low-risk areas, and the differences among the regions were primarily caused by the aspects exposure and resistance capacity. The temporal variation results indicated that the risk of agricultural drought in most areas presented an initially increasing and then decreasing trend. A higher value for the exposure aspect increased the risk of drought, whereas a higher value for the resistance capacity aspect reduced the risk of drought. Over the long term, the exposure level of the region presented limited increases, whereas the resistance capacity presented considerable increases. Therefore, the risk of agricultural drought in Heilongjiang Province will continue to exhibit a decreasing trend.

Identifying clusters of falls-related hospital admissions to inform population targets for prioritising falls prevention programmes

PubMed Central

Finch, Caroline F; Stephan, Karen; Shee, Anna Wong; Hill, Keith; Haines, Terry P; Clemson, Lindy; Day, Lesley

2015-01-01

Background There has been limited research investigating the relationship between injurious falls and hospital resource use. The aims of this study were to identify clusters of community-dwelling older people in the general population who are at increased risk of being admitted to hospital following a fall and how those clusters differed in their use of hospital resources. Methods Analysis of routinely collected hospital admissions data relating to 45 374 fall-related admissions in Victorian community-dwelling older adults aged ≥65 years that occurred during 2008/2009 to 2010/2011. Fall-related admission episodes were identified based on being admitted from a private residence to hospital with a principal diagnosis of injury (International Classification of Diseases (ICD)-10-AM codes S00 to T75) and having a first external cause of a fall (ICD-10-AM codes W00 to W19). A cluster analysis was performed to identify homogeneous groups using demographic details of patients and information on the presence of comorbidities. Hospital length of stay (LOS) was compared across clusters using competing risks regression. Results Clusters based on area of residence, demographic factors (age, gender, marital status, country of birth) and the presence of comorbidities were identified. Clusters representing hospitalised fallers with comorbidities were associated with longer LOS compared with other cluster groups. Clusters delineated by demographic factors were also associated with increased LOS. Conclusions All patients with comorbidity, and older women without comorbidities, stay in hospital longer following a fall and hence consume a disproportionate share of hospital resources. These findings have important implications for the targeting of falls prevention interventions for community-dwelling older people. PMID:25618735

Ion mobility spectrometry-mass spectrometry examination of the structures, stabilities, and extents of hydration of dimethylamine-sulfuric acid clusters.

PubMed

Thomas, Jikku M; He, Siqin; Larriba-Andaluz, Carlos; DePalma, Joseph W; Johnston, Murray V; Hogan, Christopher J

2016-08-17

We applied an atmospheric pressure differential mobility analyzer (DMA) coupled to a time-of-flight mass spectrometer to examine the stability, mass-mobility relationship, and extent of hydration of dimethylamine-sulfuric acid cluster ions, which are of relevance to nucleation in ambient air. Cluster ions were generated by electrospray ionization and were of the form: [H((CH3)2NH)x(H2SO4)y](+) and [(HSO4)((CH3)2NH)x(H2SO4)y](-), where 4 ≤ x ≤ 8, and 5 ≤ y ≤ 12. Under dry conditions, we find that positively charged cluster ions dissociated via loss of both multiple dimethylamine and sulfuric acid molecules after mobility analysis but prior to mass analysis, and few parent ions were detected in the mass spectrometer. Dissociation also occurred for negative ions, but to a lesser extent than for positive ions for the same mass spectrometer inlet conditions. Under humidified conditions (relative humidities up to 30% in the DMA), positively charged cluster ion dissociation in the mass spectrometer inlet was mitigated and occurred primarily by H2SO4 loss from ions containing excess acid molecules. DMA measurements were used to infer collision cross sections (CCSs) for all identifiable cluster ions. Stokes-Millikan equation and diffuse/inelastic gas molecule scattering predicted CCSs overestimate measured CCSs by more than 15%, while elastic-specular collision model predictions are in good agreement with measurements. Finally, cluster ion hydration was examined by monitoring changes in CCSs with increasing relative humidity. All examined cluster ions showed a modest amount of water molecule adsorption, with percentage increases in CCS smaller than 10%. The extent of hydration correlates directly with cluster ion acidity for positive ions.

Development of deformation band clusters in porous quartz sandstones - Contribution from microstructural analysis and numerical modeling

NASA Astrophysics Data System (ADS)

Philit, S.; Soliva, R.; Chemenda, A. I.

2017-12-01

Because sandstones form good reservoirs for hydrocarbon, water or C02 storage, the understanding of the deformation processes in sandstones is major. The deformation band clusters result from the localization of the deformation in porous sandstones under the form of gathered low-permeability cataclastic deformation bands. It has recently been shown that this localization is favored in extensional tectonics. The clusters measure tens to hundreds of meters in extent and propagate vertically as long as the sandstone is clean. Because the clusters can form several kilometers long networks, they are likely to hamper fluid flow during reservoir exploitation. Yet, the processes of band accumulation linked to the evolution of the clusters to a potential faulting are poorly understood. An integrated study coupling a microscopic analysis of the deformed granular material in clusters from 7 sites in the world and distinct element numerical modeling permits to propose a model for cluster growth. Our microscopic analysis reveals that the clusters display varying degree of cataclasis, with the most important degrees in the bands. This cataclasis is accompanied by porosity reduction (more reduced in thrust Andersonian regime), and increased Particle Size Distribution. This testifies of an important packing and implies an increased number of particle coordination. During deformation, the grain shape is both smoothened and roughened; the averaged values of the roundness and circularity indicate a rapid roughening of the clasts at the first stages of deformation followed by a slight smoothening. The roughening of the clasts in densely packed material induces high friction and strengthens the material. High residual porosity at some band edges suggests a local dilatant behavior of sheared material. Our distinct element numerical models and other particle models in the literature confirm this observation. The development of force chains with low particle coordination at these locations would weaken the stress resistance at the contact points. Hence, the cluster growth would be promoted by the successive localization of bands the edges of preexisting bands. Faulting could occur at any stage of the cluster development, probably favored along interfaces of minimized strength with smooth geometry.

Successful ageing: A study of the literature using citation network analysis.

PubMed

Kusumastuti, Sasmita; Derks, Marloes G M; Tellier, Siri; Di Nucci, Ezio; Lund, Rikke; Mortensen, Erik Lykke; Westendorp, Rudi G J

2016-11-01

Ageing is accompanied by an increased risk of disease and a loss of functioning on several bodily and mental domains and some argue that maintaining health and functioning is essential for a successful old age. Paradoxically, studies have shown that overall wellbeing follows a curvilinear pattern with the lowest point at middle age but increases thereafter up to very old age. To shed further light on this paradox, we reviewed the existing literature on how scholars define successful ageing and how they weigh the contribution of health and functioning to define success. We performed a novel, hypothesis-free and quantitative analysis of citation networks exploring the literature on successful ageing that exists in the Web of Science Core Collection Database using the CitNetExplorer software. Outcomes were visualized using timeline-based citation patterns. The clusters and sub-clusters of citation networks identified were starting points for in-depth qualitative analysis. Within the literature from 1902 through 2015, two distinct citation networks were identified. The first cluster had 1146 publications and 3946 citation links. It focused on successful ageing from the perspective of older persons themselves. Analysis of the various sub-clusters emphasized the importance of coping strategies, psycho-social engagement, and cultural differences. The second cluster had 609 publications and 1682 citation links and viewed successful ageing based on the objective measurements as determined by researchers. Subsequent sub-clustering analysis pointed to different domains of functioning and various ways of assessment. In the current literature two mutually exclusive concepts of successful ageing are circulating that depend on whether the individual himself or an outsider judges the situation. These different points of view help to explain the disability paradox, as successful ageing lies in the eyes of the beholder. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Metallic-covalent bonding conversion and thermoelectric properties of Al-based icosahedral quasicrystals and approximants.

PubMed

Takagiwa, Yoshiki; Kimura, Kaoru

2014-08-01

In this article, we review the characteristic features of icosahedral cluster solids, metallic-covalent bonding conversion (MCBC), and the thermoelectric properties of Al-based icosahedral quasicrystals and approximants. MCBC is clearly distinguishable from and closely related to the well-known metal-insulator transition. This unique bonding conversion has been experimentally verified in 1/1-AlReSi and 1/0-Al 12 Re approximants by the maximum entropy method and Rietveld refinement for powder x-ray diffraction data, and is caused by a central atom inside the icosahedral clusters. This helps to understand pseudogap formation in the vicinity of the Fermi energy and establish a guiding principle for tuning the thermoelectric properties. From the electron density distribution analysis, rigid heavy clusters weakly bonded with glue atoms are observed in the 1/1-AlReSi approximant crystal, whose physical properties are close to icosahedral Al-Pd-TM (TM: Re, Mn) quasicrystals. They are considered to be an intermediate state among the three typical solids: metals, covalently bonded networks (semiconductor), and molecular solids. Using the above picture and detailed effective mass analysis, we propose a guiding principle of weakly bonded rigid heavy clusters to increase the thermoelectric figure of merit ( ZT ) by optimizing the bond strengths of intra- and inter-icosahedral clusters. Through element substitutions that mainly weaken the inter-cluster bonds, a dramatic increase of ZT from less than 0.01 to 0.26 was achieved. To further increase ZT , materials should form a real gap to obtain a higher Seebeck coefficient.

Structure and Stability of GeAu{sub n}, n = 1-10 clusters: A Density Functional Study

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

Priyanka,; Dharamvir, Keya; Sharma, Hitesh

2011-12-12

The structures of Germanium doped gold clusters GeAu{sub n} (n = 1-10) have been investigated using ab initio calculations based on density functional theory (DFT). We have obtained ground state geometries of GeAu{sub n} clusters and have it compared with Silicon doped gold clusters and pure gold clusters. The ground state geometries of the GeAu{sub n} clusters show patterns similar to silicon doped gold clusters except for n = 5, 6 and 9. The introduction of germanium atom increases the binding energy of gold clusters. The binding energy per atom of germanium doped cluster is smaller than the corresponding siliconmore » doped gold cluster. The HUMO-LOMO gap for Au{sub n}Ge clusters have been found to vary between 0.46 eV-2.09 eV. The mullikan charge analysis indicates that charge of order of 0.1e always transfers from germanium atom to gold atom.« less

Do beef risk perceptions or risk attitudes have a greater effect on the beef purchase decisions of Canadian consumers?

PubMed

Yang, Jun; Goddard, Ellen

2011-01-01

Cluster analysis is applied in this study to group Canadian households by two characteristics, their risk perceptions and risk attitudes toward beef. There are some similarities in demographic profiles, meat purchases, and bovine spongiform encephalopathy (BSE) media recall between the cluster that perceives beef to be the most risky and the cluster that has little willingness to accept the risks of eating beef. There are similarities between the medium risk perception cluster and the medium risk attitude cluster, as well as between the cluster that perceives beef to have little risk and the cluster that is most willing to accept the risks of eating beef. Regression analysis shows that risk attitudes have a larger impact on household-level beef purchasing decisions than do risk perceptions for all consumer clusters. This implies that it may be more effective to undertake policies that reduce the risks associated with eating beef, instead of enhancing risk communication to improve risk perceptions. Only for certain clusters with higher willingness to accept the risks of eating beef might enhancing risk communication increase beef consumption significantly. The different role of risk perceptions and risk attitudes in beef consumption needs to be recognized during the design of risk management policies.

Cluster Analysis of Acute Care Use Yields Insights for Tailored Pediatric Asthma Interventions.

PubMed

Abir, Mahshid; Truchil, Aaron; Wiest, Dawn; Nelson, Daniel B; Goldstick, Jason E; Koegel, Paul; Lozon, Marie M; Choi, Hwajung; Brenner, Jeffrey

2017-09-01

We undertake this study to understand patterns of pediatric asthma-related acute care use to inform interventions aimed at reducing potentially avoidable hospitalizations. Hospital claims data from 3 Camden city facilities for 2010 to 2014 were used to perform cluster analysis classifying patients aged 0 to 17 years according to their asthma-related hospital use. Clusters were based on 2 variables: asthma-related ED visits and hospitalizations. Demographics and a number of sociobehavioral and use characteristics were compared across clusters. Children who met the criteria (3,170) were included in the analysis. An examination of a scree plot showing the decline in within-cluster heterogeneity as the number of clusters increased confirmed that clusters of pediatric asthma patients according to hospital use exist in the data. Five clusters of patients with distinct asthma-related acute care use patterns were observed. Cluster 1 (62% of patients) showed the lowest rates of acute care use. These patients were least likely to have a mental health-related diagnosis, were less likely to have visited multiple facilities, and had no hospitalizations for asthma. Cluster 2 (19% of patients) had a low number of asthma ED visits and onetime hospitalization. Cluster 3 (11% of patients) had a high number of ED visits and low hospitalization rates, and the highest rates of multiple facility use. Cluster 4 (7% of patients) had moderate ED use for both asthma and other illnesses, and high rates of asthma hospitalizations; nearly one quarter received care at all facilities, and 1 in 10 had a mental health diagnosis. Cluster 5 (1% of patients) had extreme rates of acute care use. Differences observed between groups across multiple sociobehavioral factors suggest these clusters may represent children who differ along multiple dimensions, in addition to patterns of service use, with implications for tailored interventions. Copyright © 2017 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

Nano titania aided clustering and adhesion of beneficial bacteria to plant roots to enhance crop growth and stress management.

PubMed

Palmqvist, N G M; Bejai, S; Meijer, J; Seisenbaeva, G A; Kessler, V G

2015-05-13

A novel use of Titania nanoparticles as agents in the nano interface interaction between a beneficial plant growth promoting bacterium (Bacillus amyloliquefaciens UCMB5113) and oilseed rape plants (Brassica napus) for protection against the fungal pathogen Alternaria brassicae is presented. Two different TiO2 nanoparticle material were produced by the Sol-Gel approach, one using the patented Captigel method and the other one applying TiBALDH precursor. The particles were characterized by transmission electron microscopy, thermogravimetric analysis, X-ray diffraction, dynamic light scattering and nano particle tracking analysis. Scanning electron microscopy showed that the bacterium was living in clusters on the roots and the combined energy-dispersive X-ray spectroscopy analysis revealed that titanium was present in these cluster formations. Confocal laser scanning microscopy further demonstrated an increased bacterial colonization of Arabidopsis thaliana roots and a semi-quantitative microscopic assay confirmed an increased bacterial adhesion to the roots. An increased amount of adhered bacteria was further confirmed by quantitative fluorescence measurements. The degree of infection by the fungus was measured and quantified by real-time-qPCR. Results showed that Titania nanoparticles increased adhesion of beneficial bacteria on to the roots of oilseed rape and protected the plants against infection.

Nano titania aided clustering and adhesion of beneficial bacteria to plant roots to enhance crop growth and stress management

NASA Astrophysics Data System (ADS)

Palmqvist, N. G. M.; Bejai, S.; Meijer, J.; Seisenbaeva, G. A.; Kessler, V. G.

2015-05-01

A novel use of Titania nanoparticles as agents in the nano interface interaction between a beneficial plant growth promoting bacterium (Bacillus amyloliquefaciens UCMB5113) and oilseed rape plants (Brassica napus) for protection against the fungal pathogen Alternaria brassicae is presented. Two different TiO2 nanoparticle material were produced by the Sol-Gel approach, one using the patented Captigel method and the other one applying TiBALDH precursor. The particles were characterized by transmission electron microscopy, thermogravimetric analysis, X-ray diffraction, dynamic light scattering and nano particle tracking analysis. Scanning electron microscopy showed that the bacterium was living in clusters on the roots and the combined energy-dispersive X-ray spectroscopy analysis revealed that titanium was present in these cluster formations. Confocal laser scanning microscopy further demonstrated an increased bacterial colonization of Arabidopsis thaliana roots and a semi-quantitative microscopic assay confirmed an increased bacterial adhesion to the roots. An increased amount of adhered bacteria was further confirmed by quantitative fluorescence measurements. The degree of infection by the fungus was measured and quantified by real-time-qPCR. Results showed that Titania nanoparticles increased adhesion of beneficial bacteria on to the roots of oilseed rape and protected the plants against infection.

Nano titania aided clustering and adhesion of beneficial bacteria to plant roots to enhance crop growth and stress management

PubMed Central

Palmqvist, N. G. M.; Bejai, S.; Meijer, J.; Seisenbaeva, G. A.; Kessler, V. G.

2015-01-01

A novel use of Titania nanoparticles as agents in the nano interface interaction between a beneficial plant growth promoting bacterium (Bacillus amyloliquefaciens UCMB5113) and oilseed rape plants (Brassica napus) for protection against the fungal pathogen Alternaria brassicae is presented. Two different TiO2 nanoparticle material were produced by the Sol-Gel approach, one using the patented Captigel method and the other one applying TiBALDH precursor. The particles were characterized by transmission electron microscopy, thermogravimetric analysis, X-ray diffraction, dynamic light scattering and nano particle tracking analysis. Scanning electron microscopy showed that the bacterium was living in clusters on the roots and the combined energy-dispersive X-ray spectroscopy analysis revealed that titanium was present in these cluster formations. Confocal laser scanning microscopy further demonstrated an increased bacterial colonization of Arabidopsis thaliana roots and a semi-quantitative microscopic assay confirmed an increased bacterial adhesion to the roots. An increased amount of adhered bacteria was further confirmed by quantitative fluorescence measurements. The degree of infection by the fungus was measured and quantified by real-time-qPCR. Results showed that Titania nanoparticles increased adhesion of beneficial bacteria on to the roots of oilseed rape and protected the plants against infection. PMID:25970693

Athletic groin pain (part 2): a prospective cohort study on the biomechanical evaluation of change of direction identifies three clusters of movement patterns

PubMed Central

Franklyn-Miller, A; Richter, C; King, E; Gore, S; Moran, K; Strike, S; Falvey, E C

2017-01-01

Background Athletic groin pain (AGP) is prevalent in sports involving repeated accelerations, decelerations, kicking and change-of-direction movements. Clinical and radiological examinations lack the ability to assess pathomechanics of AGP, but three-dimensional biomechanical movement analysis may be an important innovation. Aim The primary aim was to describe and analyse movements used by patients with AGP during a maximum effort change-of-direction task. The secondary aim was to determine if specific anatomical diagnoses were related to a distinct movement strategy. Methods 322 athletes with a current symptom of chronic AGP participated. Structured and standardised clinical assessments and radiological examinations were performed on all participants. Additionally, each participant performed multiple repetitions of a planned maximum effort change-of-direction task during which whole body kinematics were recorded. Kinematic and kinetic data were examined using continuous waveform analysis techniques in combination with a subgroup design that used gap statistic and hierarchical clustering. Results Three subgroups (clusters) were identified. Kinematic and kinetic measures of the clusters differed strongly in patterns observed in thorax, pelvis, hip, knee and ankle. Cluster 1 (40%) was characterised by increased ankle eversion, external rotation and knee internal rotation and greater knee work. Cluster 2 (15%) was characterised by increased hip flexion, pelvis contralateral drop, thorax tilt and increased hip work. Cluster 3 (45%) was characterised by high ankle dorsiflexion, thorax contralateral drop, ankle work and prolonged ground contact time. No correlation was observed between movement clusters and clinically palpated location of the participant's pain. Conclusions We identified three distinct movement strategies among athletes with long-standing groin pain during a maximum effort change-of-direction task These movement strategies were not related to clinical assessment findings but highlighted targets for rehabilitation in response to possible propagative mechanisms. Trial registration number NCT02437942, pre results. PMID:28209597

Dynamical Organization of Syntaxin-1A at the Presynaptic Active Zone

PubMed Central

Ullrich, Alexander; Böhme, Mathias A.; Schöneberg, Johannes; Depner, Harald; Sigrist, Stephan J.; Noé, Frank

2015-01-01

Synaptic vesicle fusion is mediated by SNARE proteins forming in between synaptic vesicle (v-SNARE) and plasma membrane (t-SNARE), one of which is Syntaxin-1A. Although exocytosis mainly occurs at active zones, Syntaxin-1A appears to cover the entire neuronal membrane. By using STED super-resolution light microscopy and image analysis of Drosophila neuro-muscular junctions, we show that Syntaxin-1A clusters are more abundant and have an increased size at active zones. A computational particle-based model of syntaxin cluster formation and dynamics is developed. The model is parametrized to reproduce Syntaxin cluster-size distributions found by STED analysis, and successfully reproduces existing FRAP results. The model shows that the neuronal membrane is adjusted in a way to strike a balance between having most syntaxins stored in large clusters, while still keeping a mobile fraction of syntaxins free or in small clusters that can efficiently search the membrane or be traded between clusters. This balance is subtle and can be shifted toward almost no clustering and almost complete clustering by modifying the syntaxin interaction energy on the order of only 1 kBT. This capability appears to be exploited at active zones. The larger active-zone syntaxin clusters are more stable and provide regions of high docking and fusion capability, whereas the smaller clusters outside may serve as flexible reserve pool or sites of spontaneous ectopic release. PMID:26367029

Ambiguity and judgments of obese individuals: no news could be bad news.

PubMed

Ross, Kathryn M; Shivy, Victoria A; Mazzeo, Suzanne E

2009-08-01

Stigmatization towards obese individuals has not decreased despite the increasing prevalence of obesity. Nonetheless, stigmatization remains difficult to study, given concerns about social desirability. To address this issue, this study used paired comparisons and cluster analysis to examine how undergraduates (n=189) categorized scenarios describing the health-related behaviors of obese individuals. The cluster analysis found that the scenarios were categorized into two distinct clusters. The first cluster included all scenarios with health behaviors indicating high responsibility for body weight. These individuals were perceived as unattractive, lazy, less likeable, less disciplined, and more deserving of their condition compared to individuals in the second cluster, which included all scenarios with health behaviors indicating low responsibility for body weight. Four scenarios depicted obese individuals with ambiguous information regarding health behaviors; three out of these four individuals were categorized in the high-responsibility cluster. These findings suggested that participants viewed these individuals as negatively as those who were responsible for their condition. These results have practical implications for reducing obesity bias, as the etiology of obesity is typically not known in real-life situations.

Enhancement of deuterium retention in damaged tungsten by plasma-induced defect clustering

NASA Astrophysics Data System (ADS)

Jin, Younggil; Roh, Ki-Baek; Sheen, Mi-Hyang; Kim, Nam-Kyun; Song, Jaemin; Kim, Young-Woon; Kim, Gon-Ho

2017-12-01

The enhancement of deuterium retention was investigated for tungsten in the presence of both 2.8 MeV self-ion induced cascade damage and fuel hydrogen isotope plasma. Vacancy clustering in cascade damaged polycrystalline tungsten occurred due to deuterium irradiation and was observed near the grain boundary by using all-step transmission electron microscopy analysis. Analysis of the highest desorption temperature peak using thermal desorption spectroscopy supports reasonable evidence of defect clustering in the damaged polycrystalline tungsten. The defect clustering was neither observed on the damaged polycrystalline tungsten without deuterium irradiation nor on the damaged single-crystalline tungsten with deuterium irradiation. This result implies the synergetic role of deuterium and grain boundary on defect clustering. This study proposes a path for the defect transform from point defect to defect cluster, by the agglomeration between irradiated deuterium and cascade damage-induced defect. This agglomeration may induce more severe damage on the tungsten divertor at which the high fuel hydrogen ions, fast neutrons, and self-ions are irradiated simultaneously and it would increase the in-vessel tritium inventory.

Inflammatory endotypes of chronic rhinosinusitis based on cluster analysis of biomarkers.

PubMed

Tomassen, Peter; Vandeplas, Griet; Van Zele, Thibaut; Cardell, Lars-Olaf; Arebro, Julia; Olze, Heidi; Förster-Ruhrmann, Ulrike; Kowalski, Marek L; Olszewska-Ziąber, Agnieszka; Holtappels, Gabriele; De Ruyck, Natalie; Wang, Xiangdong; Van Drunen, Cornelis; Mullol, Joaquim; Hellings, Peter; Hox, Valerie; Toskala, Elina; Scadding, Glenis; Lund, Valerie; Zhang, Luo; Fokkens, Wytske; Bachert, Claus

2016-05-01

Current phenotyping of chronic rhinosinusitis (CRS) into chronic rhinosinusitis with nasal polyps (CRSwNP) and chronic rhinosinusitis without nasal polyps (CRSsNP) might not adequately reflect the pathophysiologic diversity within patients with CRS. We sought to identify inflammatory endotypes of CRS. Therefore we aimed to cluster patients with CRS based solely on immune markers in a phenotype-free approach. Secondarily, we aimed to match clusters to phenotypes. In this multicenter case-control study patients with CRS and control subjects underwent surgery, and tissue was analyzed for IL-5, IFN-γ, IL-17A, TNF-α, IL-22, IL-1β, IL-6, IL-8, eosinophilic cationic protein, myeloperoxidase, TGF-β1, IgE, Staphylococcus aureus enterotoxin-specific IgE, and albumin. We used partition-based clustering. Clustering of 173 cases resulted in 10 clusters, of which 4 clusters with low or undetectable IL-5, eosinophilic cationic protein, IgE, and albumin concentrations, and 6 clusters with high concentrations of those markers. The group of IL-5-negative clusters, 3 clusters clinically resembled a predominant chronic rhinosinusitis without nasal polyps (CRSsNP) phenotype without increased asthma prevalence, and 1 cluster had a TH17 profile and had mixed CRSsNP/CRSwNP. The IL-5-positive clusters were divided into a group with moderate IL-5 concentrations, a mixed CRSsNP/CRSwNP and increased asthma phenotype, and a group with high IL-5 levels, an almost exclusive nasal polyp phenotype with strongly increased asthma prevalence. In the latter group, 2 clusters demonstrated the highest concentrations of IgE and asthma prevalence, with all samples expressing Staphylococcus aureus enterotoxin-specific IgE. Distinct CRS clusters with diverse inflammatory mechanisms largely correlated with phenotypes and further differentiated them and provided a more accurate description of the inflammatory mechanisms involved than phenotype information only. Copyright © 2016 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

RSAT 2015: Regulatory Sequence Analysis Tools

PubMed Central

Medina-Rivera, Alejandra; Defrance, Matthieu; Sand, Olivier; Herrmann, Carl; Castro-Mondragon, Jaime A.; Delerce, Jeremy; Jaeger, Sébastien; Blanchet, Christophe; Vincens, Pierre; Caron, Christophe; Staines, Daniel M.; Contreras-Moreira, Bruno; Artufel, Marie; Charbonnier-Khamvongsa, Lucie; Hernandez, Céline; Thieffry, Denis; Thomas-Chollier, Morgane; van Helden, Jacques

2015-01-01

RSAT (Regulatory Sequence Analysis Tools) is a modular software suite for the analysis of cis-regulatory elements in genome sequences. Its main applications are (i) motif discovery, appropriate to genome-wide data sets like ChIP-seq, (ii) transcription factor binding motif analysis (quality assessment, comparisons and clustering), (iii) comparative genomics and (iv) analysis of regulatory variations. Nine new programs have been added to the 43 described in the 2011 NAR Web Software Issue, including a tool to extract sequences from a list of coordinates (fetch-sequences from UCSC), novel programs dedicated to the analysis of regulatory variants from GWAS or population genomics (retrieve-variation-seq and variation-scan), a program to cluster motifs and visualize the similarities as trees (matrix-clustering). To deal with the drastic increase of sequenced genomes, RSAT public sites have been reorganized into taxon-specific servers. The suite is well-documented with tutorials and published protocols. The software suite is available through Web sites, SOAP/WSDL Web services, virtual machines and stand-alone programs at http://www.rsat.eu/. PMID:25904632

The PhytoClust tool for metabolic gene clusters discovery in plant genomes

PubMed Central

Fuchs, Lisa-Maria

2017-01-01

Abstract The existence of Metabolic Gene Clusters (MGCs) in plant genomes has recently raised increased interest. Thus far, MGCs were commonly identified for pathways of specialized metabolism, mostly those associated with terpene type products. For efficient identification of novel MGCs, computational approaches are essential. Here, we present PhytoClust; a tool for the detection of candidate MGCs in plant genomes. The algorithm employs a collection of enzyme families related to plant specialized metabolism, translated into hidden Markov models, to mine given genome sequences for physically co-localized metabolic enzymes. Our tool accurately identifies previously characterized plant MGCs. An exhaustive search of 31 plant genomes detected 1232 and 5531 putative gene cluster types and candidates, respectively. Clustering analysis of putative MGCs types by species reflected plant taxonomy. Furthermore, enrichment analysis revealed taxa- and species-specific enrichment of certain enzyme families in MGCs. When operating through our web-interface, PhytoClust users can mine a genome either based on a list of known cluster types or by defining new cluster rules. Moreover, for selected plant species, the output can be complemented by co-expression analysis. Altogether, we envisage PhytoClust to enhance novel MGCs discovery which will in turn impact the exploration of plant metabolism. PMID:28486689

The PhytoClust tool for metabolic gene clusters discovery in plant genomes.

PubMed

Töpfer, Nadine; Fuchs, Lisa-Maria; Aharoni, Asaph

2017-07-07

The existence of Metabolic Gene Clusters (MGCs) in plant genomes has recently raised increased interest. Thus far, MGCs were commonly identified for pathways of specialized metabolism, mostly those associated with terpene type products. For efficient identification of novel MGCs, computational approaches are essential. Here, we present PhytoClust; a tool for the detection of candidate MGCs in plant genomes. The algorithm employs a collection of enzyme families related to plant specialized metabolism, translated into hidden Markov models, to mine given genome sequences for physically co-localized metabolic enzymes. Our tool accurately identifies previously characterized plant MGCs. An exhaustive search of 31 plant genomes detected 1232 and 5531 putative gene cluster types and candidates, respectively. Clustering analysis of putative MGCs types by species reflected plant taxonomy. Furthermore, enrichment analysis revealed taxa- and species-specific enrichment of certain enzyme families in MGCs. When operating through our web-interface, PhytoClust users can mine a genome either based on a list of known cluster types or by defining new cluster rules. Moreover, for selected plant species, the output can be complemented by co-expression analysis. Altogether, we envisage PhytoClust to enhance novel MGCs discovery which will in turn impact the exploration of plant metabolism. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Spatial distribution and cluster analysis of retail drug shop characteristics and antimalarial behaviors as reported by private medicine retailers in western Kenya: informing future interventions.

PubMed

Rusk, Andria; Highfield, Linda; Wilkerson, J Michael; Harrell, Melissa; Obala, Andrew; Amick, Benjamin

2016-02-19

Efforts to improve malaria case management in sub-Saharan Africa have shifted focus to private antimalarial retailers to increase access to appropriate treatment. Demands to decrease intervention cost while increasing efficacy requires interventions tailored to geographic regions with demonstrated need. Cluster analysis presents an opportunity to meet this demand, but has not been applied to the retail sector or antimalarial retailer behaviors. This research conducted cluster analysis on medicine retailer behaviors in Kenya, to improve malaria case management and inform future interventions. Ninety-seven surveys were collected from medicine retailers working in the Webuye Health and Demographic Surveillance Site. Survey items included retailer training, education, antimalarial drug knowledge, recommending behavior, sales, and shop characteristics, and were analyzed using Kulldorff's spatial scan statistic. The Bernoulli purely spatial model for binomial data was used, comparing cases to controls. Statistical significance of found clusters was tested with a likelihood ratio test, using the null hypothesis of no clustering, and a p value based on 999 Monte Carlo simulations. The null hypothesis was rejected with p values of 0.05 or less. A statistically significant cluster of fewer than expected pharmacy-trained retailers was found (RR = .09, p = .001) when compared to the expected random distribution. Drug recommending behavior also yielded a statistically significant cluster, with fewer than expected retailers recommending the correct antimalarial medication to adults (RR = .018, p = .01), and fewer than expected shops selling that medication more often than outdated antimalarials when compared to random distribution (RR = 0.23, p = .007). All three of these clusters were co-located, overlapping in the northwest of the study area. Spatial clustering was found in the data. A concerning amount of correlation was found in one specific region in the study area where multiple behaviors converged in space, highlighting a prime target for interventions. These results also demonstrate the utility of applying geospatial methods in the study of medicine retailer behaviors, making the case for expanding this approach to other regions.

The Productivity and Technical Efficiency of Textile Industry Clusters in India

NASA Astrophysics Data System (ADS)

Bhaskaran, E.

2013-09-01

The Indian textile industry is one the largest and oldest sectors in the country and among the most important in the economy in terms of output, investment and employment (E). The sector employs nearly 35 million people and after agriculture, is the second-highest employer in the country. Its importance is underlined by the fact that it accounts for around 4 % of Gross Domestic Product, 14 % of industrial production, 9 % of excise collections, 18 % of E in the industrial sector, and 16 % of the country's total exports (Ex) earnings. For inclusive growth and sustainable development most of the Textile Manufacturers has adopted the Cluster Development Approach. The objective is to study the physical and financial performance, correlation, regression and Data Envelopment Analysis by measuring technical efficiency (Ø), peer weights (λi), input slacks (S-), output slacks (S+) and return to scale of four textile clusters (TCs) namely IchalKaranji Textile Cluster, Maharashtra; Ludhiana Textile Cluster, Punjab; Tirupur Textile Cluster, Tamilnadu and Panipat Textile Cluster, Haryana in India. The methodology adopted is using Data Envelopment Analysis of Output Oriented Banker Charnes Cooper Model by taking number of units (U) and number of E as inputs and sales (S) and Ex in crores as an outputs. The non-zero λi's represents the weights for efficient clusters. The S > 0 obtained for one TC reveals the excess U (S-) and E (S-) and shortage in sales (S+) and Ex (S+). To conclude, for inclusive growth and sustainable development, the inefficient TC should increase their S/turnover and Ex, as decrease in number of enterprises and E is practically not possible. Moreover for sustainable development, the TC should strengthen infrastructure interrelationships, technology interrelationships, procurement interrelationships, production interrelationships and marketing interrelationships to decrease cost, increase productivity and efficiency to compete in the world market.

Symptom clusters in women with breast cancer: an analysis of data from social media and a research study

PubMed Central

Marshall, Sarah A.; Yang, Christopher C.; Ping, Qing; Zhao, Mengnan; Avis, Nancy E.

2016-01-01

Purpose User-generated content on social media sites, such as health-related online forums, offers researchers a tantalizing amount of information, but concerns regarding scientific application of such data remain. This paper compares and contrasts symptom cluster patterns derived from messages on a breast cancer forum with those from a symptom checklist completed by breast cancer survivors participating in a research study. Methods Over 50,000 messages generated by 12,991 users of the breast cancer forum on MedHelp.org were transformed into a standard form and examined for the co-occurrence of 25 symptoms. The k-medoid clustering method was used to determine appropriate placement of symptoms within clusters. Findings were compared with a similar analysis of a symptom checklist administered to 653 breast cancer survivors participating in a research study. Results The following clusters were identified using forum data: menopausal/psychological, pain/fatigue, gastrointestinal, and miscellaneous. Study data generated the clusters: menopausal, pain, fatigue/sleep/gastrointestinal, psychological, and increased weight/appetite. Although the clusters are somewhat different, many symptoms that clustered together in the social media analysis remained together in the analysis of the study participants. Density of connections between symptoms, as reflected by rates of co-occurrence and similarity, was higher in the study data. Conclusions The copious amount of data generated by social media outlets can augment findings from traditional data sources. When different sources of information are combined, areas of overlap and discrepancy can be detected, perhaps giving researchers a more accurate picture of reality. However, data derived from social media must be used carefully and with understanding of its limitations. PMID:26476836

Symptom clusters in women with breast cancer: an analysis of data from social media and a research study.

PubMed

Marshall, Sarah A; Yang, Christopher C; Ping, Qing; Zhao, Mengnan; Avis, Nancy E; Ip, Edward H

2016-03-01

User-generated content on social media sites, such as health-related online forums, offers researchers a tantalizing amount of information, but concerns regarding scientific application of such data remain. This paper compares and contrasts symptom cluster patterns derived from messages on a breast cancer forum with those from a symptom checklist completed by breast cancer survivors participating in a research study. Over 50,000 messages generated by 12,991 users of the breast cancer forum on MedHelp.org were transformed into a standard form and examined for the co-occurrence of 25 symptoms. The k-medoid clustering method was used to determine appropriate placement of symptoms within clusters. Findings were compared with a similar analysis of a symptom checklist administered to 653 breast cancer survivors participating in a research study. The following clusters were identified using forum data: menopausal/psychological, pain/fatigue, gastrointestinal, and miscellaneous. Study data generated the clusters: menopausal, pain, fatigue/sleep/gastrointestinal, psychological, and increased weight/appetite. Although the clusters are somewhat different, many symptoms that clustered together in the social media analysis remained together in the analysis of the study participants. Density of connections between symptoms, as reflected by rates of co-occurrence and similarity, was higher in the study data. The copious amount of data generated by social media outlets can augment findings from traditional data sources. When different sources of information are combined, areas of overlap and discrepancy can be detected, perhaps giving researchers a more accurate picture of reality. However, data derived from social media must be used carefully and with understanding of its limitations.

Clustering and Dimensionality Reduction to Discover Interesting Patterns in Binary Data

NASA Astrophysics Data System (ADS)

Palumbo, Francesco; D'Enza, Alfonso Iodice

The attention towards binary data coding increased consistently in the last decade due to several reasons. The analysis of binary data characterizes several fields of application, such as market basket analysis, DNA microarray data, image mining, text mining and web-clickstream mining. The paper illustrates two different approaches exploiting a profitable combination of clustering and dimensionality reduction for the identification of non-trivial association structures in binary data. An application in the Association Rules framework supports the theory with the empirical evidence.

Developmental analysis of the dopamine-containing neurons of the Drosophila brain

PubMed Central

Hartenstein, Volker; Cruz, Louie; Lovick, Jennifer K.; Guo, Ming

2016-01-01

The Drosophila dopaminergic (DA) system consists of a relatively small number of neurons clustered throughout the brain and ventral nerve cord. Previous work shows that clusters of DA neurons innervate different brain compartments, which in part accounts for functional diversity of the DA system. In this paper, we analyzed the association between DA neuron clusters and specific brain lineages, developmental and structural units of the Drosophila brain which provide a framework of connections that can be followed throughout development. The hatching larval brain contains six groups of primary DA neurons (born in the embryo), which we assign to six distinct lineages. We can show that all larval DA clusters persist into the adult brain. Some clusters increase in cell number during late larval stages while others do not become DA-positive until early pupa. Ablating neuroblasts with hydroxyurea (HU) prior to onset of larval proliferation (generates secondary neurons) confirms these added DA clusters are primary neurons born in the embryo, rather than secondary neurons. A single cluster that becomes DA-positive in the late pupa, PAM1/lineage DALcm1/2, forms part of a secondary lineage which can be ablated by larval HU application. By supplying lineage information for each DA cluster, our analysis promotes further developmental and functional analyses of this important system of neurons. PMID:27350102

Profiling nurses' job satisfaction, acculturation, work environment, stress, cultural values and coping abilities: A cluster analysis.

PubMed

Goh, Yong-Shian; Lee, Alice; Chan, Sally Wai-Chi; Chan, Moon Fai

2015-08-01

This study aimed to determine whether definable profiles existed in a cohort of nursing staff with regard to demographic characteristics, job satisfaction, acculturation, work environment, stress, cultural values and coping abilities. A survey was conducted in one hospital in Singapore from June to July 2012, and 814 full-time staff nurses completed a self-report questionnaire (89% response rate). Demographic characteristics, job satisfaction, acculturation, work environment, perceived stress, cultural values, ways of coping and intention to leave current workplace were assessed as outcomes. The two-step cluster analysis revealed three clusters. Nurses in cluster 1 (n = 222) had lower acculturation scores than nurses in cluster 3. Cluster 2 (n = 362) was a group of younger nurses who reported higher intention to leave (22.4%), stress level and job dissatisfaction than the other two clusters. Nurses in cluster 3 (n = 230) were mostly Singaporean and reported the lowest intention to leave (13.0%). Resources should be allocated to specifically address the needs of younger nurses and hopefully retain them in the profession. Management should focus their retention strategies on junior nurses and provide a work environment that helps to strengthen their intention to remain in nursing by increasing their job satisfaction. © 2014 Wiley Publishing Asia Pty Ltd.

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

PubMed Central

Xu, Rui; Zhen, Zonglei; Liu, Jia

2010-01-01

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies. PMID:21152081

Cluster-guided imaging-based CFD analysis of airflow and particle deposition in asthmatic human lungs

NASA Astrophysics Data System (ADS)

Choi, Jiwoong; Leblanc, Lawrence; Choi, Sanghun; Haghighi, Babak; Hoffman, Eric; Lin, Ching-Long

2017-11-01

The goal of this study is to assess inter-subject variability in delivery of orally inhaled drug products to small airways in asthmatic lungs. A recent multiscale imaging-based cluster analysis (MICA) of computed tomography (CT) lung images in an asthmatic cohort identified four clusters with statistically distinct structural and functional phenotypes associating with unique clinical biomarkers. Thus, we aimed to address inter-subject variability via inter-cluster variability. We selected a representative subject from each of the 4 asthma clusters as well as 1 male and 1 female healthy controls, and performed computational fluid and particle simulations on CT-based airway models of these subjects. The results from one severe and one non-severe asthmatic cluster subjects characterized by segmental airway constriction had increased particle deposition efficiency, as compared with the other two cluster subjects (one non-severe and one severe asthmatics) without airway constriction. Constriction-induced jets impinging on distal bifurcations led to excessive particle deposition. The results emphasize the impact of airway constriction on regional particle deposition rather than disease severity, demonstrating the potential of using cluster membership to tailor drug delivery. NIH Grants U01HL114494 and S10-RR022421, and FDA Grant U01FD005837. XSEDE.

Comparing population structure as inferred from genealogical versus genetic information.

PubMed

Colonna, Vincenza; Nutile, Teresa; Ferrucci, Ronald R; Fardella, Giulio; Aversano, Mario; Barbujani, Guido; Ciullo, Marina

2009-12-01

Algorithms for inferring population structure from genetic data (ie, population assignment methods) have shown to effectively recognize genetic clusters in human populations. However, their performance in identifying groups of genealogically related individuals, especially in scanty-differentiated populations, has not been tested empirically thus far. For this study, we had access to both genealogical and genetic data from two closely related, isolated villages in southern Italy. We found that nearly all living individuals were included in a single pedigree, with multiple inbreeding loops. Despite F(st) between villages being a low 0.008, genetic clustering analysis identified two clusters roughly corresponding to the two villages. Average kinship between individuals (estimated from genealogies) increased at increasing values of group membership (estimated from the genetic data), showing that the observed genetic clusters represent individuals who are more closely related to each other than to random members of the population. Further, average kinship within clusters and F(st) between clusters increases with increasingly stringent membership threshold requirements. We conclude that a limited number of genetic markers is sufficient to detect structuring, and that the results of genetic analyses faithfully mirror the structuring inferred from detailed analyses of population genealogies, even when F(st) values are low, as in the case of the two villages. We then estimate the impact of observed levels of population structure on association studies using simulated data.

Comparing population structure as inferred from genealogical versus genetic information

PubMed Central

Colonna, Vincenza; Nutile, Teresa; Ferrucci, Ronald R; Fardella, Giulio; Aversano, Mario; Barbujani, Guido; Ciullo, Marina

2009-01-01

Algorithms for inferring population structure from genetic data (ie, population assignment methods) have shown to effectively recognize genetic clusters in human populations. However, their performance in identifying groups of genealogically related individuals, especially in scanty-differentiated populations, has not been tested empirically thus far. For this study, we had access to both genealogical and genetic data from two closely related, isolated villages in southern Italy. We found that nearly all living individuals were included in a single pedigree, with multiple inbreeding loops. Despite Fst between villages being a low 0.008, genetic clustering analysis identified two clusters roughly corresponding to the two villages. Average kinship between individuals (estimated from genealogies) increased at increasing values of group membership (estimated from the genetic data), showing that the observed genetic clusters represent individuals who are more closely related to each other than to random members of the population. Further, average kinship within clusters and Fst between clusters increases with increasingly stringent membership threshold requirements. We conclude that a limited number of genetic markers is sufficient to detect structuring, and that the results of genetic analyses faithfully mirror the structuring inferred from detailed analyses of population genealogies, even when Fst values are low, as in the case of the two villages. We then estimate the impact of observed levels of population structure on association studies using simulated data. PMID:19550436

Sunlight Modulates Fruit Metabolic Profile and Shapes the Spatial Pattern of Compound Accumulation within the Grape Cluster.

PubMed

Reshef, Noam; Walbaum, Natasha; Agam, Nurit; Fait, Aaron

2017-01-01

Vineyards are characterized by their large spatial variability of solar irradiance (SI) and temperature, known to effectively modulate grape metabolism. To explore the role of sunlight in shaping fruit composition and cluster uniformity, we studied the spatial pattern of incoming irradiance, fruit temperature and metabolic profile within individual grape clusters under three levels of sunlight exposure. The experiment was conducted in a vineyard of Cabernet Sauvignon cv. located in the Negev Highlands, Israel, where excess SI and midday temperatures are known to degrade grape quality. Filtering SI lowered the surface temperature of exposed fruits and increased the uniformity of irradiance and temperature in the cluster zone. SI affected the overall levels and patterns of accumulation of sugars, organic acids, amino acids and phenylpropanoids, across the grape cluster. Increased exposure to sunlight was associated with lower accumulation levels of malate, aspartate, and maleate but with higher levels of valine, leucine, and serine, in addition to the stress-related proline and GABA. Flavan-3-ols metabolites showed a negative response to SI, whereas flavonols were highly induced. The overall levels of anthocyanins decreased with increased sunlight exposure; however, a hierarchical cluster analysis revealed that the members of this family were grouped into three distinct accumulation patterns, with malvidin anthocyanins and cyanidin-glucoside showing contrasting trends. The flavonol-glucosides, quercetin and kaempferol, exhibited a logarithmic response to SI, leading to improved cluster uniformity under high-light conditions. Comparing the within-cluster variability of metabolite accumulation highlighted the stability of sugars, flavan-3-ols, and cinnamic acid metabolites to SI, in contrast to the plasticity of flavonols. A correlation-based network analysis revealed that extended exposure to SI modified metabolic coordination, increasing the number of negative correlations between metabolites in both pulp and skin. This integrated study of micrometeorology and metabolomics provided insights into the grape-cluster pattern of accumulation of 70 primary and secondary metabolites as a function of spatial variations in SI. Studying compound-specific responses against an extended gradient of quantified conditions improved our knowledge regarding the modulation of berry metabolism by SI, with the aim of using sunlight regulation to accurately modulate fruit composition in warm and arid/semi-arid regions.

Sunlight Modulates Fruit Metabolic Profile and Shapes the Spatial Pattern of Compound Accumulation within the Grape Cluster

PubMed Central

Reshef, Noam; Walbaum, Natasha; Agam, Nurit; Fait, Aaron

2017-01-01

Vineyards are characterized by their large spatial variability of solar irradiance (SI) and temperature, known to effectively modulate grape metabolism. To explore the role of sunlight in shaping fruit composition and cluster uniformity, we studied the spatial pattern of incoming irradiance, fruit temperature and metabolic profile within individual grape clusters under three levels of sunlight exposure. The experiment was conducted in a vineyard of Cabernet Sauvignon cv. located in the Negev Highlands, Israel, where excess SI and midday temperatures are known to degrade grape quality. Filtering SI lowered the surface temperature of exposed fruits and increased the uniformity of irradiance and temperature in the cluster zone. SI affected the overall levels and patterns of accumulation of sugars, organic acids, amino acids and phenylpropanoids, across the grape cluster. Increased exposure to sunlight was associated with lower accumulation levels of malate, aspartate, and maleate but with higher levels of valine, leucine, and serine, in addition to the stress-related proline and GABA. Flavan-3-ols metabolites showed a negative response to SI, whereas flavonols were highly induced. The overall levels of anthocyanins decreased with increased sunlight exposure; however, a hierarchical cluster analysis revealed that the members of this family were grouped into three distinct accumulation patterns, with malvidin anthocyanins and cyanidin-glucoside showing contrasting trends. The flavonol-glucosides, quercetin and kaempferol, exhibited a logarithmic response to SI, leading to improved cluster uniformity under high-light conditions. Comparing the within-cluster variability of metabolite accumulation highlighted the stability of sugars, flavan-3-ols, and cinnamic acid metabolites to SI, in contrast to the plasticity of flavonols. A correlation-based network analysis revealed that extended exposure to SI modified metabolic coordination, increasing the number of negative correlations between metabolites in both pulp and skin. This integrated study of micrometeorology and metabolomics provided insights into the grape-cluster pattern of accumulation of 70 primary and secondary metabolites as a function of spatial variations in SI. Studying compound-specific responses against an extended gradient of quantified conditions improved our knowledge regarding the modulation of berry metabolism by SI, with the aim of using sunlight regulation to accurately modulate fruit composition in warm and arid/semi-arid regions. PMID:28203242

An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China

PubMed Central

Zou, Hui; Zou, Zhihong; Wang, Xiaojing

2015-01-01

The increase and the complexity of data caused by the uncertain environment is today’s reality. In order to identify water quality effectively and reliably, this paper presents a modified fast clustering algorithm for water quality analysis. The algorithm has adopted a varying weights K-means cluster algorithm to analyze water monitoring data. The varying weights scheme was the best weighting indicator selected by a modified indicator weight self-adjustment algorithm based on K-means, which is named MIWAS-K-means. The new clustering algorithm avoids the margin of the iteration not being calculated in some cases. With the fast clustering analysis, we can identify the quality of water samples. The algorithm is applied in water quality analysis of the Haihe River (China) data obtained by the monitoring network over a period of eight years (2006–2013) with four indicators at seven different sites (2078 samples). Both the theoretical and simulated results demonstrate that the algorithm is efficient and reliable for water quality analysis of the Haihe River. In addition, the algorithm can be applied to more complex data matrices with high dimensionality. PMID:26569283

Hydrogen bonding in water clusters and their ionized counterparts.

PubMed

Neela, Y Indra; Mahadevi, A Subha; Sastry, G Narahari

2010-12-30

Ab initio and DFT computations were carried out on four distinct hydrogen-bonded arrangements of water clusters (H(2)O)(n), n = 2-20, represented as W1D, W2D, W2DH, and W3D. The variation in the strength of hydrogen bond as a function of the chain length is studied. In all the four cases, there is a substantial cooperative interaction, albeit in different degrees. The effect of basis set superposition error (BSSE) on the complexation energy of water clusters has been analyzed. Atoms in molecules (AIM) analysis performed to evaluate the nature of the hydrogen bonding shows a high correlation between hydrogen bond strength and the trends in complexation energy. Solvated water clusters exhibit lower complexation energies compared to corresponding gas-phase geometries on PCM (polarized continuum model) optimization. The feasibility of stripping an electron or addition of an electron increases dramatically as the cluster size increases. Although W3D caged structures are stable for neutral clusters, the helical W2DH arrangement appeared to be an optimal choice for its ionized counterparts.

Electrostatic effects on clustering and ion dynamics in ionomer melts

NASA Astrophysics Data System (ADS)

Ma, Boran; Nguyen, Trung; Pryamitsyn, Victor; Olvera de La Cruz, Monica

An understanding of the relationships between ionomer chain morphology, dynamics and counter-ion mobility is a key factor in the design of ion conducting membranes for battery applications. In this study, we investigate the influence of electrostatic coupling between randomly charged copolymers (ionomers) and counter ions on the structural and dynamic features of a model system of ionomer melts. Using coarse-grained molecular dynamics (CGMD) simulations, we found that variations in electrostatic coupling strength (Γ) remarkably affect the formation of ion-counter ion clusters, ion mobility, and polymer dynamics for a range of charged monomer fractions. Specifically, an increase in Γ leads to larger ionic cluster sizes and reduced polymer and ion mobility. Analysis of the distribution of the radius of gyration of the clusters further reveals that the fractal dimension of the ion clusters is nearly independent from Γ for all the cases studied. Finally, at sufficiently high values of Γ, we observed arrested heterogeneous ions mobility, which is correlated with an increase in ion cluster size. These findings provide insight into the role of electrostatics in governing the nanostructures formed by ionomers.

Optimizing disinfection by-product monitoring points in a distribution system using cluster analysis.

PubMed

Delpla, Ianis; Florea, Mihai; Pelletier, Geneviève; Rodriguez, Manuel J

2018-06-04

Trihalomethanes (THMs) and Haloacetic Acids (HAAs) are the main groups detected in drinking water and are consequently strictly regulated. However, the increasing quantity of data for disinfection byproducts (DBPs) produced from research projects and regulatory programs remains largely unexploited, despite a great potential for its use in optimizing drinking water quality monitoring to meet specific objectives. In this work, we developed a procedure to optimize locations and periods for DBPs monitoring based on a set of monitoring scenarios using the cluster analysis technique. The optimization procedure used a robust set of spatio-temporal monitoring results on DBPs (THMs and HAAs) generated from intensive sampling campaigns conducted in a residential sector of a water distribution system. Results shows that cluster analysis allows for the classification of water quality in different groups of THMs and HAAs according to their similarities, and the identification of locations presenting water quality concerns. By using cluster analysis with different monitoring objectives, this work provides a set of monitoring solutions and a comparison between various monitoring scenarios for decision-making purposes. Finally, it was demonstrated that the data from intensive monitoring of free chlorine residual and water temperature as DBP proxy parameters, when processed using cluster analysis, could also help identify the optimal sampling points and periods for regulatory THMs and HAAs monitoring. Copyright © 2018 Elsevier Ltd. All rights reserved.

Altitude as a risk factor for the development of hypospadias. Geographical cluster distribution analysis in South America.

PubMed

Fernández, Nicolas; Lorenzo, Armando; Bägli, Darius; Zarante, Ignacio

2016-10-01

Hypospadias is the most common congenital anomaly affecting the genitals. It has been established as a multifactorial disease with increasing prevalence. Many risk factors have been identified such as prematurity, birth weight, mother's age, and exposure to endocrine disruptors. In recent decades multiple authors using surveillance systems have described an increase in prevalence of hypospadias, but most of the published literature comes from developed countries in Europe and North America and few of the published studies have involved cluster analysis. Few large-scale studies have been performed addressing the effect of altitude and other geographical aspects on the development of hypospadias. Acknowledging this limitation, we present novel results of a multinational spatial scan statistical analysis over a 30-year period in South America and an altitude analysis of hypospadias distribution on a continent level. A retrospective review was performed of the Latin American collaborative study of congenital malformations (ECLAMC). A total of 4,020,384 newborns was surveyed between 1982 and December 2011 in all participating centers. We selected all patients with hypospadias. All degrees of clinical severity were included in the analysis. Each participating center was geographically identified with its coordinates and altitude above sea level. A spatial scan statistical analysis was performed using Kulldorf's methodology and a prevalence trend analysis over time in centers below and above 2000 m. During the study period we found 159 hospitals in six different countries (Colombia, Bolivia, Brazil, Argentina, Chile, and Uruguay) with 4,537 cases of hypospadias and a global prevalence rate of 11.3/10,000 newborns. Trend analysis showed that centers below 2000 m had an increasing trend with an average of 10/10,000 newborns as opposed to those centers above 2000 m that showed a reducing trend with an average prevalence of 7.8 (p = 0.1246). We identified clusters with significant increases of prevalence in five centers along the coast at an average altitude of 219.8 m above sea level (p > 0.0000). Reduction in prevalence was found in clusters located in two centers on the Andes mountains. Altitude of 2,000 m was associated with hypospadias (Figure), with an OR 0.59 (0.5-0.69). There are ethnic arguments to support our results supported by protective polymorphism distribution in high lands. Altitude above 2,000 m is suggested to have a protective effect for hypospadias. Specific clusters have been identified with increased risk for hypospadias. Environmental risk factors in these areas need to be further studied given the association seen between altitude and the distribution of more severe cases. Copyright © 2016 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.

Finding approximate gene clusters with Gecko 3.

PubMed

Winter, Sascha; Jahn, Katharina; Wehner, Stefanie; Kuchenbecker, Leon; Marz, Manja; Stoye, Jens; Böcker, Sebastian

2016-11-16

Gene-order-based comparison of multiple genomes provides signals for functional analysis of genes and the evolutionary process of genome organization. Gene clusters are regions of co-localized genes on genomes of different species. The rapid increase in sequenced genomes necessitates bioinformatics tools for finding gene clusters in hundreds of genomes. Existing tools are often restricted to few (in many cases, only two) genomes, and often make restrictive assumptions such as short perfect conservation, conserved gene order or monophyletic gene clusters. We present Gecko 3, an open-source software for finding gene clusters in hundreds of bacterial genomes, that comes with an easy-to-use graphical user interface. The underlying gene cluster model is intuitive, can cope with low degrees of conservation as well as misannotations and is complemented by a sound statistical evaluation. To evaluate the biological benefit of Gecko 3 and to exemplify our method, we search for gene clusters in a dataset of 678 bacterial genomes using Synechocystis sp. PCC 6803 as a reference. We confirm detected gene clusters reviewing the literature and comparing them to a database of operons; we detect two novel clusters, which were confirmed by publicly available experimental RNA-Seq data. The computational analysis is carried out on a laptop computer in <40 min. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Cluster analysis to estimate the risk of preeclampsia in the high-risk Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction (PREDO) study.

PubMed

Villa, Pia M; Marttinen, Pekka; Gillberg, Jussi; Lokki, A Inkeri; Majander, Kerttu; Ordén, Maija-Riitta; Taipale, Pekka; Pesonen, Anukatriina; Räikkönen, Katri; Hämäläinen, Esa; Kajantie, Eero; Laivuori, Hannele

2017-01-01

Preeclampsia is divided into early-onset (delivery before 34 weeks of gestation) and late-onset (delivery at or after 34 weeks) subtypes, which may rise from different etiopathogenic backgrounds. Early-onset disease is associated with placental dysfunction. Late-onset disease develops predominantly due to metabolic disturbances, obesity, diabetes, lipid dysfunction, and inflammation, which affect endothelial function. Our aim was to use cluster analysis to investigate clinical factors predicting the onset and severity of preeclampsia in a cohort of women with known clinical risk factors. We recruited 903 pregnant women with risk factors for preeclampsia at gestational weeks 12+0-13+6. Each individual outcome diagnosis was independently verified from medical records. We applied a Bayesian clustering algorithm to classify the study participants to clusters based on their particular risk factor combination. For each cluster, we computed the risk ratio of each disease outcome, relative to the risk in the general population. The risk of preeclampsia increased exponentially with respect to the number of risk factors. Our analysis revealed 25 number of clusters. Preeclampsia in a previous pregnancy (n = 138) increased the risk of preeclampsia 8.1 fold (95% confidence interval (CI) 5.7-11.2) compared to a general population of pregnant women. Having a small for gestational age infant (n = 57) in a previous pregnancy increased the risk of early-onset preeclampsia 17.5 fold (95%CI 2.1-60.5). Cluster of those two risk factors together (n = 21) increased the risk of severe preeclampsia to 23.8-fold (95%CI 5.1-60.6), intermediate onset (delivery between 34+0-36+6 weeks of gestation) to 25.1-fold (95%CI 3.1-79.9) and preterm preeclampsia (delivery before 37+0 weeks of gestation) to 16.4-fold (95%CI 2.0-52.4). Body mass index over 30 kg/m2 (n = 228) as a sole risk factor increased the risk of preeclampsia to 2.1-fold (95%CI 1.1-3.6). Together with preeclampsia in an earlier pregnancy the risk increased to 11.4 (95%CI 4.5-20.9). Chronic hypertension (n = 60) increased the risk of preeclampsia 5.3-fold (95%CI 2.4-9.8), of severe preeclampsia 22.2-fold (95%CI 9.9-41.0), and risk of early-onset preeclampsia 16.7-fold (95%CI 2.0-57.6). If a woman had chronic hypertension combined with obesity, gestational diabetes and earlier preeclampsia, the risk of term preeclampsia increased 4.8-fold (95%CI 0.1-21.7). Women with type 1 diabetes mellitus had a high risk of all subgroups of preeclampsia. The risk of preeclampsia increases exponentially with respect to the number of risk factors. Early-onset preeclampsia and severe preeclampsia have different risk profile from term preeclampsia.

Correlation of shallow marine, deep marine, and coastal terrestrial records of central California: asynchronous responses to paleoceanographic and paleoclimatic change during the past 19,000 years

NASA Astrophysics Data System (ADS)

McGann, M.

2016-12-01

Benthic and planktic foraminiferal census data combined with pollen data acquired from the continental margin off central California (core S3-15G, 3491 m depth from the western levy of the Monterey Fan; 36°23.53'N, 123°20.52'W) provide a unique opportunity to document concurrent paleoceanographic and paleoclimatic changes in the region during the late Quaternary. Radiocarbon dates and the ratio of the planktic foraminiferal species Neogloboquardrina pachyderma (Ehrenberg) to Neogloboquardrina incompta (Cifelli) provide a good age-depth model for the last 19,000 years. Q-mode cluster analysis of the benthic foraminifera grouped the fauna into two clusters reflecting faunal adaptation to changing climatic conditions during the Pleistocene and Holocene, whereas the R-mode cluster analysis identified glacial (Uvigerina senticosa and Globobulimina auriculata) and interglacial (Melonis pompilioides and Gyroidina planulata) faunas. A slight increase in oxygen concentration in the deep sea across the Pleistocene-Holocene transition is suggested by a reduction in abundance of G. auriculata and increased frequency of M. pompilioides. Q-mode cluster analysis of the planktic foraminifera indicates a change in the surface water from a glacial subpolar fauna in the Pleistocene to a transitional fauna in the Holocene. The pollen flora separated into three clusters by Q-mode cluster analysis, two of Pleistocene age (glacial and transitional) and one in the Holocene (interglacial), reflecting adaptation of the flora in the California Coast Ranges of central California to the warmer climate in the Holocene. Decoupling is evident between the benthic foraminiferal, planktic foraminiferal, and terrestrial floral responses to changing oceanographic and climatic conditions. The floral response leads the surface-dwelling planktic fauna by several millennia, and is followed by the deep-dwelling benthic fauna a millennium later.

I. Excluded volume effects in Ising cluster distributions and nuclear multifragmentation. II. Multiple-chance effects in alpha-particle evaporation

NASA Astrophysics Data System (ADS)

Breus, Dimitry Eugene

In Part I, geometric clusters of the Ising model are studied as possible model clusters for nuclear multifragmentation. These clusters may not be considered as non-interacting (ideal gas) due to excluded volume effect which predominantly is the artifact of the cluster's finite size. Interaction significantly complicates the use of clusters in the analysis of thermodynamic systems. Stillinger's theory is used as a basis for the analysis, which within the RFL (Reiss, Frisch, Lebowitz) fluid-of-spheres approximation produces a prediction for cluster concentrations well obeyed by geometric clusters of the Ising model. If thermodynamic condition of phase coexistence is met, these concentrations can be incorporated into a differential equation procedure of moderate complexity to elucidate the liquid-vapor phase diagram of the system with cluster interaction included. The drawback of increased complexity is outweighted by the reward of greater accuracy of the phase diagram, as it is demonstrated by the Ising model. A novel nuclear-cluster analysis procedure is developed by modifying Fisher's model to contain cluster interaction and employing the differential equation procedure to obtain thermodynamic variables. With this procedure applied to geometric clusters, the guidelines are developed to look for excluded volume effect in nuclear multifragmentation. In Part II, an explanation is offered for the recently observed oscillations in the energy spectra of alpha-particles emitted from hot compound nuclei. Contrary to what was previously expected, the oscillations are assumed to be caused by the multiple-chance nature of alpha-evaporation. In a semi-empirical fashion this assumption is successfully confirmed by a technique of two-spectra decomposition which treats experimental alpha-spectra as having contributions from at least two independent emitters. Building upon the success of the multiple-chance explanation of the oscillations, Moretto's single-chance evaporation theory is augmented to include multiple-chance emission and tested on experimental data to yield positive results.

Microstructural and electrical properties of Al/n-type Si Schottky diodes with Au-CuPc nanocomposite films as interlayer

NASA Astrophysics Data System (ADS)

Reddy, P. R. Sekhar; Janardhanam, V.; Jyothi, I.; Chang, Han-Soo; Lee, Sung-Nam; Lee, Myung Sun; Reddy, V. Rajagopal; Choi, Chel-Jong

2017-11-01

Au-CuPc nanocomposite films were prepared by simultaneous evaporation of Au and CuPc with various Au and CuPc concentrations. Microstructural analysis of Au-CuPc films revealed elongated Au cluster formation from isolated Au nanoclusters with increasing Au concentration associated with coalescence of Au clusters. Au-CuPc films with different compositions were employed as interlayer in Al/n-Si Schottky diode. Barrier height and series resistance of the Al/n-Si Schottky diode with Au-CuPc interlayer decreased with increasing Au concentration. This could be associated with the enhancement of electron tunneling between neighboring clusters due to decrease in spacing of Au clusters and formation of conducting paths through the composite material. Interface state density of the Al/n-Si Schottky diode with Au-CuPc interlayer increased with increasing Au concentration. This might be because the inclusion of metal decreases the crystallinity and crystal size of the polymer matrix accompanied by the formation of local defect sites at the places of metal nucleation.

Analysis of gamma-ray energies for 56 excited superdeformed rotational bands of nuclei of lanthanons La to Dy and of Hg, Tl, and Pb on the basis of the two-revolving-cluster model, with evaluation of moments of inertia and radii of revolution and assignment of nucleonic compositions to the clusters and the central sphere.

PubMed

Pauling, L

1992-08-01

Analysis of the gamma-ray energies of 28 excited superdeformed bands of lanthanon nuclei by application of the two-revolving-cluster model yields the result that the central sphere for all 28 has the semimagic-magic composition p40n50, with the range p8n12 to p14n18 for the clusters and the radius of revolution increasing from 7.31 to 7.76 fm. Similar analysis of 28 excited bands of Hg, Tl, and Pb nuclei leads to p56n82 (semimagic-magic) for the central sphere of 24 bands, p64n82 (semimagic-magic) for 2, and p64n90 (doubly semimagic) for 2, with cluster range p8n12 to p14n16 and values of the radius of revolution from 8.70 to 8.92 fm for 26 bands and 9.2 fm for 2.

Analysis of gamma-ray energies for 56 excited superdeformed rotational bands of nuclei of lanthanons La to Dy and of Hg, Tl, and Pb on the basis of the two-revolving-cluster model, with evaluation of moments of inertia and radii of revolution and assignment of nucleonic compositions to the clusters and the central sphere.

PubMed Central

Pauling, L

1992-01-01

Analysis of the gamma-ray energies of 28 excited superdeformed bands of lanthanon nuclei by application of the two-revolving-cluster model yields the result that the central sphere for all 28 has the semimagic-magic composition p40n50, with the range p8n12 to p14n18 for the clusters and the radius of revolution increasing from 7.31 to 7.76 fm. Similar analysis of 28 excited bands of Hg, Tl, and Pb nuclei leads to p56n82 (semimagic-magic) for the central sphere of 24 bands, p64n82 (semimagic-magic) for 2, and p64n90 (doubly semimagic) for 2, with cluster range p8n12 to p14n16 and values of the radius of revolution from 8.70 to 8.92 fm for 26 bands and 9.2 fm for 2. PMID:11607313

Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale

PubMed Central

Kobourov, Stephen; Gallant, Mike; Börner, Katy

2016-01-01

Overview Notions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms—Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. Cluster Quality Metrics We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Network Clustering Algorithms Smart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters. PMID:27391786

A clustering method of Chinese medicine prescriptions based on modified firefly algorithm.

PubMed

Yuan, Feng; Liu, Hong; Chen, Shou-Qiang; Xu, Liang

2016-12-01

This paper is aimed to study the clustering method for Chinese medicine (CM) medical cases. The traditional K-means clustering algorithm had shortcomings such as dependence of results on the selection of initial value, trapping in local optimum when processing prescriptions form CM medical cases. Therefore, a new clustering method based on the collaboration of firefly algorithm and simulated annealing algorithm was proposed. This algorithm dynamically determined the iteration of firefly algorithm and simulates sampling of annealing algorithm by fitness changes, and increased the diversity of swarm through expansion of the scope of the sudden jump, thereby effectively avoiding premature problem. The results from confirmatory experiments for CM medical cases suggested that, comparing with traditional K-means clustering algorithms, this method was greatly improved in the individual diversity and the obtained clustering results, the computing results from this method had a certain reference value for cluster analysis on CM prescriptions.

Effects of additional data on Bayesian clustering.

PubMed

Yamazaki, Keisuke

2017-10-01

Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the latent variable is estimated, and it is expected that additional information will improve the accuracy of the estimation of the latent variable. Many proposed learning methods are able to use additional data; these include semi-supervised learning and transfer learning. However, from a statistical point of view, a complex probabilistic model that encompasses both the initial and additional data might be less accurate due to having a higher-dimensional parameter. The present paper presents a theoretical analysis of the accuracy of such a model and clarifies which factor has the greatest effect on its accuracy, the advantages of obtaining additional data, and the disadvantages of increasing the complexity. Copyright © 2017 Elsevier Ltd. All rights reserved.

The adsorption of Run (n = 1-4) on γ-Al2O3 Surface: A DFT study

NASA Astrophysics Data System (ADS)

Liu, Zhe; Guo, Yafei; Chen, Yu; Shen, Rong

2018-05-01

The density functional theory (DFT) was adopted to study the adsorption and growth of Run (n = 1-4) clusters on γ-Al2O3 surface, which is of great significances for the design of many important catalysts, especially for carbon dioxide methanation. It is found that both the Rusbnd Ru bond length and adsorption energy Eads of Ru clusters with the surface increase with the Run clusters increasing. The growth ability of the supported Run cluster is weaker than the gas phase Run clusters through comparing their respective growth process, which ascribes to the stabilization of γ-Al2O3 support. An interesting discovery is that the basin structure was supposed to be the most favorable adsorption geometry for Run clusters. Additionally, the distances between Ru atoms in the adsorbed clusters are longer than that in their isolated counterparts. Bader charge analysis was conducted for the most stable configurations of Run (n = 1-4) clusters on γ-Al2O3 surface as well. And the results suggest that Run (n = 1-4) clusters serve as the electron donators. The result of projected density of states (PDOS) shows that strong adsorption of Ru atom on the γ-Al2O3 surface correlates with strong interaction between d orbital of Ru atom and p orbital of Al or O atom of the Al2O3 support.

Termination of seizure clusters is related to the duration of focal seizures.

PubMed

Ferastraoaru, Victor; Schulze-Bonhage, Andreas; Lipton, Richard B; Dümpelmann, Matthias; Legatt, Alan D; Blumberg, Julie; Haut, Sheryl R

2016-06-01

Clustered seizures are characterized by shorter than usual interseizure intervals and pose increased morbidity risk. This study examines the characteristics of seizures that cluster, with special attention to the final seizure in a cluster. This is a retrospective analysis of long-term inpatient monitoring data from the EPILEPSIAE project. Patients underwent presurgical evaluation from 2002 to 2009. Seizure clusters were defined by the occurrence of at least two consecutive seizures with interseizure intervals of <4 h. Other definitions of seizure clustering were examined in a sensitivity analysis. Seizures were classified into three contextually defined groups: isolated seizures (not meeting clustering criteria), terminal seizure (last seizure in a cluster), and intracluster seizures (any other seizures within a cluster). Seizure characteristics were compared among the three groups in terms of duration, type (focal seizures remaining restricted to one hemisphere vs. evolving bilaterally), seizure origin, and localization concordance among pairs of consecutive seizures. Among 92 subjects, 77 (83%) had at least one seizure cluster. The intracluster seizures were significantly shorter than the last seizure in a cluster (p = 0.011), whereas the last seizure in a cluster resembled the isolated seizures in terms of duration. Although focal only (unilateral), seizures were shorter than seizures that evolved bilaterally and there was no correlation between the seizure type and the seizure position in relation to a cluster (p = 0.762). Frontal and temporal lobe seizures were more likely to cluster compared with other localizations (p = 0.009). Seizure pairs that are part of a cluster were more likely to have a concordant origin than were isolated seizures. Results were similar for the 2 h definition of clustering, but not for the 8 h definition of clustering. We demonstrated that intracluster seizures are short relative to isolated seizures and terminal seizures. Frontal and temporal lobe seizures are more likely to cluster. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

The Antenatal Corticosteroids Trial (ACT): a secondary analysis to explore site differences in a multi-country trial.

PubMed

Klein, Karen; McClure, Elizabeth M; Colaci, Daniela; Thorsten, Vanessa; Hibberd, Patricia L; Esamai, Fabian; Garces, Ana; Patel, Archana; Saleem, Sarah; Pasha, Omrana; Chomba, Elwyn; Carlo, Waldemar A; Krebs, Nancy F; Goudar, Shivaprasad; Derman, Richard J; Liechty, Edward A; Koso-Thomas, Marion; Buekens, Pierre M; Belizán, José M; Goldenberg, Robert L; Althabe, Fernando

2016-05-24

The Antenatal Corticosteroid Trial (ACT) assessed the feasibility, effectiveness, and safety of a multifaceted intervention to increase the use of antenatal corticosteroids (ACS) in mothers at risk of preterm birth at all levels of care in low and middle-income countries. The intervention effectively increased the use of ACS but had no overall impact on neonatal mortality in the targeted <5(th) percentile birth weight infants. Being in the intervention clusters was also associated with an overall increase in neonatal deaths. We sought to explore plausible pathways through which this intervention increased neonatal mortality. We conducted secondary analyses to assess site differences in outcome and potential explanations for the differences in outcomes if found. By site, and in the intervention and control clusters, we evaluated characteristics of the mothers and care systems, the proportion of the <5(th) percentile infants and the overall population that received ACS, the rates of possible severe bacterial infection (pSBI), determined from clinical signs, and neonatal mortality rates. There were substantial differences between the sites in both participant and health system characteristics, with Guatemala and Argentina generally having the highest levels of care. In some sites there were substantial differences in the health system characteristics between the intervention and control clusters. The increase in ACS in the intervention clusters was similar among the sites. While overall, there was no difference in neonatal mortality among <5(th) percentile births between the intervention and control clusters, Guatemala and Pakistan both had significant reductions in neonatal mortality in the <5(th) percentile infants in the intervention clusters. The improvement in neonatal mortality in the Guatemalan site in the <5(th) percentile infants was associated with a higher level of care at the site and an improvement in care in the intervention clusters. There was a significant increase overall in neonatal mortality in the intervention clusters compared to the control. Across sites, this increase in neonatal mortality was statistically significant and most apparent in the African sites. This increase in neonatal mortality was accompanied by a significant increase in pSBI in the African sites. The improvement in neonatal mortality in the Guatemalan site in the <5(th) percentile infants was associated with a higher level of care and an improvement in care in the intervention clusters. The increase in neonatal mortality in the intervention clusters across all sites was largely driven by the poorer outcomes in the African sites, which also had an increase in pSBI in the intervention clusters. We emphasize that these results come from secondary analyses. Additional prospective studies are needed to assess the effectiveness and safety of ACS on neonatal health in low resource settings. clinicaltrials.gov (NCT01084096).

Nature of bonding and cooperativity in linear DMSO clusters: A DFT, AIM and NCI analysis.

PubMed

Venkataramanan, Natarajan Sathiyamoorthy; Suvitha, Ambigapathy

2018-05-01

This study aims to cast light on the nature of interactions and cooperativity that exists in linear dimethyl sulfoxide (DMSO) clusters using dispersion corrected density functional theory. In the linear DMSO, DMSO molecules in the middle of the clusters are bound strongly than at the terminal. The plot of the total binding energy of the clusters vs the cluster size and mean polarizabilities vs cluster size shows an excellent linearity demonstrating the presence of cooperativity effect. The computed incremental binding energy of the clusters remains nearly constant, implying that DMSO addition at the terminal site can happen to form an infinite chain. In the linear clusters, two σ-hole at the terminal DMSO molecules were found and the value on it was found to increase with the increase in cluster size. The quantum theory of atoms in molecules topography shows the existence of hydrogen and SO⋯S type in linear tetramer and larger clusters. In the dimer and trimer SO⋯OS type of interaction exists. In 2D non-covalent interactions plot, additional peaks in the regions which contribute to the stabilization of the clusters were observed and it splits in the trimer and intensifies in the larger clusters. In the trimer and larger clusters in addition to the blue patches due to hydrogen bonds, additional, light blue patches were seen between the hydrogen atom of the methyl groups and the sulphur atom of the nearby DMSO molecule. Thus, in addition to the strong H-bonds, strong electrostatic interactions between the sulphur atom and methyl hydrogens exists in the linear clusters. Copyright © 2018 Elsevier Inc. All rights reserved.

Clustering P-Wave Receiver Functions To Constrain Subsurface Seismic Structure

NASA Astrophysics Data System (ADS)

Chai, C.; Larmat, C. S.; Maceira, M.; Ammon, C. J.; He, R.; Zhang, H.

2017-12-01

The acquisition of high-quality data from permanent and temporary dense seismic networks provides the opportunity to apply statistical and machine learning techniques to a broad range of geophysical observations. Lekic and Romanowicz (2011) used clustering analysis on tomographic velocity models of the western United States to perform tectonic regionalization and the velocity-profile clusters agree well with known geomorphic provinces. A complementary and somewhat less restrictive approach is to apply cluster analysis directly to geophysical observations. In this presentation, we apply clustering analysis to teleseismic P-wave receiver functions (RFs) continuing efforts of Larmat et al. (2015) and Maceira et al. (2015). These earlier studies validated the approach with surface waves and stacked EARS RFs from the USArray stations. In this study, we experiment with both the K-means and hierarchical clustering algorithms. We also test different distance metrics defined in the vector space of RFs following Lekic and Romanowicz (2011). We cluster data from two distinct data sets. The first, corresponding to the western US, was by smoothing/interpolation of receiver-function wavefield (Chai et al. 2015). Spatial coherence and agreement with geologic region increase with this simpler, spatially smoothed set of observations. The second data set is composed of RFs for more than 800 stations of the China Digital Seismic Network (CSN). Preliminary results show a first order agreement between clusters and tectonic region and each region cluster includes a distinct Ps arrival, which probably reflects differences in crustal thickness. Regionalization remains an important step to characterize a model prior to application of full waveform and/or stochastic imaging techniques because of the computational expense of these types of studies. Machine learning techniques can provide valuable information that can be used to design and characterize formal geophysical inversion, providing information on spatial variability in the subsurface geology.

Untangling Magmatic Processes and Hydrothermal Alteration of in situ Superfast Spreading Ocean Crust at ODP/IODP Site 1256 with Fuzzy c-means Cluster Analysis of Rock Magnetic Properties

NASA Astrophysics Data System (ADS)

Dekkers, M. J.; Heslop, D.; Herrero-Bervera, E.; Acton, G.; Krasa, D.

2014-12-01

Ocean Drilling Program (ODP)/Integrated ODP (IODP) Hole 1256D (6.44.1' N, 91.56.1' W) on the Cocos Plate occurs in 15.2 Ma oceanic crust generated by superfast seafloor spreading. Presently, it is the only drill hole that has sampled all three oceanic crust layers in a tectonically undisturbed setting. Here we interpret down-hole trends in several rock-magnetic parameters with fuzzy c-means cluster analysis, a multivariate statistical technique. The parameters include the magnetization ratio, the coercivity ratio, the coercive force, the low-field susceptibility, and the Curie temperature. By their combined, multivariate, analysis the effects of magmatic and hydrothermal processes can be evaluated. The optimal number of clusters - a key point in the analysis because there is no a priori information on this - was determined through a combination of approaches: by calculation of several cluster validity indices, by testing for coherent cluster distributions on non-linear-map plots, and importantly by testing for stability of the cluster solution from all possible starting points. Here, we consider a solution robust if the cluster allocation is independent of the starting configuration. The five-cluster solution appeared to be robust. Three clusters are distinguished in the extrusive segment of the Hole that express increasing hydrothermal alteration of the lavas. The sheeted dike and gabbro portions are characterized by two clusters, both with higher coercivities than in lava samples. Extensive alteration, however, can obliterate magnetic property differences between lavas, dikes, and gabbros. The imprint of thermochemical alteration on the iron-titanium oxides is only partially related to the porosity of the rocks. All clusters display rock magnetic characteristics in line with a stable NRM. This implies that the entire sampled sequence of ocean crust can contribute to marine magnetic anomalies. Determination of the absolute paleointensity with thermal techniques is not straightforward because of the propensity of oxyexsolution during laboratory heating and/or the presence of intergrowths. The upper part of the extrusive sequence, the granoblastic portion of the dikes, and moderately altered gabbros may contain a comparatively uncontaminated thermoremanent magnetization.

Primary radiation damage characterization of α-iron under irradiation temperature for various PKA energies

NASA Astrophysics Data System (ADS)

Sahi, Qurat-ul-ain; Kim, Yong-Soo

2018-04-01

The understanding of radiation-induced microstructural defects in body-centered cubic (BCC) iron is of major interest to those using advanced steel under extreme conditions in nuclear reactors. In this study, molecular dynamics (MD) simulations were implemented to examine the primary radiation damage in BCC iron with displacement cascades of energy 1, 5, 10, 20, and 30 keV at temperatures ranging from 100 to 1000 K. Statistical analysis of eight MD simulations of collision cascades were carried out along each [110], [112], [111] and a high index [135] direction and the temperature dependence of the surviving number of point defects and the in-cascade clustering of vacancies and interstitials were studied. The peak time and the corresponding number of defects increase with increasing irradiation temperature and primary knock-on atom (PKA) energy. However, the final number of surviving point defects decreases with increasing lattice temperature. This is associated with the increase of thermal spike at high PKA energy and its long timespan at higher temperatures. Defect production efficiency (i.e., surviving MD defects, per Norgett-Robinson-Torrens displacements) also showed a continuous decrease with the increasing irradiation temperature and PKA energy. The number of interstitial clusters increases with both irradiation temperature and PKA energy. However, the increase in the number of vacancy clusters with PKA energy is minimal-to-constant and decreases as the irradiation temperature increases. Similarly, the probability and cluster size distribution for larger interstitials increase with temperature, whereas only smaller size vacancy clusters were observed at higher temperatures.

Tweets clustering using latent semantic analysis

NASA Astrophysics Data System (ADS)

Rasidi, Norsuhaili Mahamed; Bakar, Sakhinah Abu; Razak, Fatimah Abdul

2017-04-01

Social media are becoming overloaded with information due to the increasing number of information feeds. Unlike other social media, Twitter users are allowed to broadcast a short message called as `tweet". In this study, we extract tweets related to MH370 for certain of time. In this paper, we present overview of our approach for tweets clustering to analyze the users' responses toward tragedy of MH370. The tweets were clustered based on the frequency of terms obtained from the classification process. The method we used for the text classification is Latent Semantic Analysis. As a result, there are two types of tweets that response to MH370 tragedy which is emotional and non-emotional. We show some of our initial results to demonstrate the effectiveness of our approach.

Large-Scale Genomic Analysis of Codon Usage in Dengue Virus and Evaluation of Its Phylogenetic Dependence

PubMed Central

Lara-Ramírez, Edgar E.; Salazar, Ma Isabel; López-López, María de Jesús; Salas-Benito, Juan Santiago; Sánchez-Varela, Alejandro

2014-01-01

The increasing number of dengue virus (DENV) genome sequences available allows identifying the contributing factors to DENV evolution. In the present study, the codon usage in serotypes 1–4 (DENV1–4) has been explored for 3047 sequenced genomes using different statistics methods. The correlation analysis of total GC content (GC) with GC content at the three nucleotide positions of codons (GC1, GC2, and GC3) as well as the effective number of codons (ENC, ENCp) versus GC3 plots revealed mutational bias and purifying selection pressures as the major forces influencing the codon usage, but with distinct pressure on specific nucleotide position in the codon. The correspondence analysis (CA) and clustering analysis on relative synonymous codon usage (RSCU) within each serotype showed similar clustering patterns to the phylogenetic analysis of nucleotide sequences for DENV1–4. These clustering patterns are strongly related to the virus geographic origin. The phylogenetic dependence analysis also suggests that stabilizing selection acts on the codon usage bias. Our analysis of a large scale reveals new feature on DENV genomic evolution. PMID:25136631

Temperature Dependence in Heterogeneous Nucleation with Application to the Direct Determination of Cluster Energy on Nearly Molecular Scale

DOE PAGES

McGraw, Robert L.; Winkler, Paul M.; Wagner, Paul E.

2017-12-04

A re-examination of measurements of heterogeneous nucleation of water vapor on silver nanoparticles is presented here using a model-free framework that derives the energy of critical cluster formation directly from measurements of nucleation probability. Temperature dependence is correlated with cluster stabilization by the nanoparticle seed and previously found cases of unusual increasing nucleation onset saturation ratio with increasing temperature are explained. A necessary condition for the unusual positive temperature dependence is identified, namely that the critical cluster be more stable, on a per molecule basis, than the bulk liquid to exhibit the effect. Temperature dependence is next examined in themore » classical Fletcher model, modified here to make the energy of cluster formation explicit in the model. The contact angle used in the Fletcher model is identified as the microscopic contact angle, which can be directly obtained from heterogeneous nucleation experimental data by a recently developed analysis method. Here an equivalent condition, increasing contact angle with temperature, is found necessary for occurrence of unusual temperature dependence. Our findings have immediate applications to atmospheric particle formation and nanoparticle detection in condensation particle counters (CPCs).« less

Temperature Dependence in Heterogeneous Nucleation with Application to the Direct Determination of Cluster Energy on Nearly Molecular Scale.

PubMed

McGraw, Robert L; Winkler, Paul M; Wagner, Paul E

2017-12-04

A re-examination of measurements of heterogeneous nucleation of water vapor on silver nanoparticles is presented here using a model-free framework that derives the energy of critical cluster formation directly from measurements of nucleation probability. Temperature dependence is correlated with cluster stabilization by the nanoparticle seed and previously found cases of unusual increasing nucleation onset saturation ratio with increasing temperature are explained. A necessary condition for the unusual positive temperature dependence is identified, namely that the critical cluster be more stable, on a per molecule basis, than the bulk liquid to exhibit the effect. Temperature dependence is next examined in the classical Fletcher model, modified here to make the energy of cluster formation explicit in the model.  The contact angle used in the Fletcher model is identified as the microscopic contact angle, which can be directly obtained from heterogeneous nucleation experimental data by a recently developed analysis method. Here an equivalent condition, increasing contact angle with temperature, is found necessary for occurrence of unusual temperature dependence. Our findings have immediate applications to atmospheric particle formation and nanoparticle detection in condensation particle counters (CPCs).

Comparative genomics reveals phylogenetic distribution patterns of secondary metabolites in Amycolatopsis species.

PubMed

Adamek, Martina; Alanjary, Mohammad; Sales-Ortells, Helena; Goodfellow, Michael; Bull, Alan T; Winkler, Anika; Wibberg, Daniel; Kalinowski, Jörn; Ziemert, Nadine

2018-06-01

Genome mining tools have enabled us to predict biosynthetic gene clusters that might encode compounds with valuable functions for industrial and medical applications. With the continuously increasing number of genomes sequenced, we are confronted with an overwhelming number of predicted clusters. In order to guide the effective prioritization of biosynthetic gene clusters towards finding the most promising compounds, knowledge about diversity, phylogenetic relationships and distribution patterns of biosynthetic gene clusters is necessary. Here, we provide a comprehensive analysis of the model actinobacterial genus Amycolatopsis and its potential for the production of secondary metabolites. A phylogenetic characterization, together with a pan-genome analysis showed that within this highly diverse genus, four major lineages could be distinguished which differed in their potential to produce secondary metabolites. Furthermore, we were able to distinguish gene cluster families whose distribution correlated with phylogeny, indicating that vertical gene transfer plays a major role in the evolution of secondary metabolite gene clusters. Still, the vast majority of the diverse biosynthetic gene clusters were derived from clusters unique to the genus, and also unique in comparison to a database of known compounds. Our study on the locations of biosynthetic gene clusters in the genomes of Amycolatopsis' strains showed that clusters acquired by horizontal gene transfer tend to be incorporated into non-conserved regions of the genome thereby allowing us to distinguish core and hypervariable regions in Amycolatopsis genomes. Using a comparative genomics approach, it was possible to determine the potential of the genus Amycolatopsis to produce a huge diversity of secondary metabolites. Furthermore, the analysis demonstrates that horizontal and vertical gene transfer play an important role in the acquisition and maintenance of valuable secondary metabolites. Our results cast light on the interconnections between secondary metabolite gene clusters and provide a way to prioritize biosynthetic pathways in the search and discovery of novel compounds.

Hierarchical Star Formation in Turbulent Media: Evidence from Young Star Clusters

NASA Astrophysics Data System (ADS)

Grasha, K.; Elmegreen, B. G.; Calzetti, D.; Adamo, A.; Aloisi, A.; Bright, S. N.; Cook, D. O.; Dale, D. A.; Fumagalli, M.; Gallagher, J. S., III; Gouliermis, D. A.; Grebel, E. K.; Kahre, L.; Kim, H.; Krumholz, M. R.; Lee, J. C.; Messa, M.; Ryon, J. E.; Ubeda, L.

2017-06-01

We present an analysis of the positions and ages of young star clusters in eight local galaxies to investigate the connection between the age difference and separation of cluster pairs. We find that star clusters do not form uniformly but instead are distributed so that the age difference increases with the cluster pair separation to the 0.25-0.6 power, and that the maximum size over which star formation is physically correlated ranges from ˜200 pc to ˜1 kpc. The observed trends between age difference and separation suggest that cluster formation is hierarchical both in space and time: clusters that are close to each other are more similar in age than clusters born further apart. The temporal correlations between stellar aggregates have slopes that are consistent with predictions of turbulence acting as the primary driver of star formation. The velocity associated with the maximum size is proportional to the galaxy’s shear, suggesting that the galactic environment influences the maximum size of the star-forming structures.

Track structure in radiation biology: theory and applications.

PubMed

Nikjoo, H; Uehara, S; Wilson, W E; Hoshi, M; Goodhead, D T

1998-04-01

A brief review is presented of the basic concepts in track structure and the relative merit of various theoretical approaches adopted in Monte-Carlo track-structure codes are examined. In the second part of the paper, a formal cluster analysis is introduced to calculate cluster-distance distributions. Total experimental ionization cross-sections were least-square fitted and compared with the calculation by various theoretical methods. Monte-Carlo track-structure code Kurbuc was used to examine and compare the spectrum of the secondary electrons generated by using functions given by Born-Bethe, Jain-Khare, Gryzinsky, Kim-Rudd, Mott and Vriens' theories. The cluster analysis in track structure was carried out using the k-means method and Hartigan algorithm. Data are presented on experimental and calculated total ionization cross-sections: inverse mean free path (IMFP) as a function of electron energy used in Monte-Carlo track-structure codes; the spectrum of secondary electrons generated by different functions for 500 eV primary electrons; cluster analysis for 4 MeV and 20 MeV alpha-particles in terms of the frequency of total cluster energy to the root-mean-square (rms) radius of the cluster and differential distance distributions for a pair of clusters; and finally relative frequency distribution for energy deposited in DNA, single-strand break and double-strand breaks for 10MeV/u protons, alpha-particles and carbon ions. There are a number of Monte-Carlo track-structure codes that have been developed independently and the bench-marking presented in this paper allows a better choice of the theoretical method adopted in a track-structure code to be made. A systematic bench-marking of cross-sections and spectra of the secondary electrons shows differences between the codes at atomic level, but such differences are not significant in biophysical modelling at the macromolecular level. Clustered-damage evaluation shows: that a substantial proportion of dose ( 30%) is deposited by low-energy electrons; the majority of DNA damage lesions are of simple type; the complexity of damage increases with increased LET, while the total yield of strand breaks remains constant; and at high LET values nearly 70% of all double-strand breaks are of complex type.

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

Using Cluster Analysis to Examine Husband-Wife Decision Making

ERIC Educational Resources Information Center

Bonds-Raacke, Jennifer M.

2006-01-01

Cluster analysis has a rich history in many disciplines and although cluster analysis has been used in clinical psychology to identify types of disorders, its use in other areas of psychology has been less popular. The purpose of the current experiments was to use cluster analysis to investigate husband-wife decision making. Cluster analysis was…

Nitrogen efficiency of eastern Canadian dairy herds: Effect on production performance and farm profitability.

PubMed

Fadul-Pacheco, L; Pellerin, D; Chouinard, P Y; Wattiaux, M A; Duplessis, M; Charbonneau, É

2017-08-01

Nitrogen efficiency (milk N/dietary N; NE) can be used as a tool for the nutritional, economic, and environmental management of dairy farms. The aim of this study was to identify the characteristics of herds with varying NE and assess the effect on farm profitability. One hundred dairy herds located in Québec, Canada, comprising on average 42 ± 18 cows in lactation were visited from October 2014 to June 2015. Feed intake was measured over 24 h. Samples of each feedstuff were taken and sent to a commercial laboratory for analysis of chemical composition. Feeding management and feed prices were recorded. Milk yield was recorded and milk samples were collected over 2 consecutive milkings. Fat, protein, and milk urea N were analyzed. Balances of metabolizable protein (MP; MP supply - MP requirements) and rumen degradable protein (RDP; RDP supply - RDP requirement) were calculated. A hierarchical cluster analysis was conducted and allowed grouping the farms by their NE. Four clusters were identified with an average NE of 22.1 (NE22), 26.9 (NE27), 30.0 (NE30), and 35.8% (NE36). Herds in clusters NE30 and NE36 were fed diets with greater concentrations of starch, net energy for lactation, and nonfiber carbohydrates than those in the other 2 clusters. Moreover, the average proportion of corn silage was lower for herds in cluster NE22 compared with NE30 and NE36 (8.23 vs. 31.8 and 31.3% of total forages, respectively). In addition, crude protein of the diets declined from an average of 16.0 to 14.9% with increasing NE among clusters. Average dry matter intake declined from 26.1 to 22.5 kg/d as NE of clusters increased. Herds in cluster NE22 had lower yields of milk (28.7 vs. 31.8 kg/d), fat (1.15 vs. 1.29 kg/d), and protein (0.94 vs. 1.05 kg/d) than the other clusters. Also, milk urea N was greater for farms in cluster NE22 (13.2 mg/dL) than for farms in the other clusters (11.4 mg/dL). Furthermore, MP and RDP balances decreased from 263.2 to -153.7 g/d and from 594.7 to 486.9 g/d, respectively, with increasing NE among clusters. Income over feed cost increased from $14.3 to $17.3/cow per day (Can$) as NE among clusters augmented. Results from this study showed that some farms were able to achieve high NE by using lower levels of dietary N and having cows with lower DMI while maintaining milk performance. These farms had a potentially lower environmental impact, and they were more profitable. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Rising prevalence of non-B HIV-1 subtypes in North Carolina and evidence for local onward transmission.

PubMed

Dennis, Ann M; Hué, Stephane; Learner, Emily; Sebastian, Joseph; Miller, William C; Eron, Joseph J

2017-01-01

HIV-1 diversity is increasing in North American and European cohorts which may have public health implications. However, little is known about non-B subtype diversity in the southern United States, despite the region being the epicenter of the nation's epidemic. We characterized HIV-1 diversity and transmission clusters to identify the extent to which non-B strains are transmitted locally. We conducted cross-sectional analyses of HIV-1 partial pol sequences collected from 1997 to 2014 from adults accessing routine clinical care in North Carolina (NC). Subtypes were evaluated using COMET and phylogenetic analysis. Putative transmission clusters were identified using maximum-likelihood trees. Clusters involving non-B strains were confirmed and their dates of origin were estimated using Bayesian phylogenetics. Data were combined with demographic information collected at the time of sample collection and country of origin for a subset of patients. Among 24,972 sequences from 15,246 persons, the non-B subtype prevalence increased from 0% to 3.46% over the study period. Of 325 persons with non-B subtypes, diversity was high with over 15 pure subtypes and recombinants; subtype C (28.9%) and CRF02_AG (24.0%) were most common. While identification of transmission clusters was lower for persons with non-B versus B subtypes, several local transmission clusters (≥3 persons) involving non-B subtypes were identified and all were presumably due to heterosexual transmission. Prevalence of non-B subtype diversity remains low in NC but a statistically significant rise was identified over time which likely reflects multiple importation. However, the combined phylogenetic clustering analysis reveals evidence for local onward transmission. Detection of these non-B clusters suggests heterosexual transmission and may guide diagnostic and prevention interventions.

Emergence of clusters of CRF02_AG and B human immunodeficiency viral strains among men having sex with men exhibiting HIV primary infection in southeastern France.

PubMed

Tamalet, Catherine; Ravaux, Isabelle; Moreau, Jacques; Brégigeon, Sylvie; Tourres, Christian; Richet, Hervé; Abat, Cedric; Colson, Philippe

2015-08-01

The number of new HIV diagnoses is increasing in the western world and transmission clusters have been recently identified among men having sex with men despite Highly Active Antiretroviral Therapy efficacy. The objective of this study was to assess temporal trends, epidemiological, clinical and virological characteristics of primary HIV infections. A retrospective analysis of 79 patients presenting primary HIV infections from 2005 to 2012 was performed in Marseille University Hospitals, southeastern France. Clinical, epidemiological and immunovirological data including phylogeny based on the polymerase gene were collected. 65 males and 14 females were enrolled. The main transmission route was homosexual contact (60.8%). Patients were mostly infected with subtype B (73.4%) and CRF02_AG (21.5%) HIV-1 strains. An increase in the annual number of HIV seroconversions among new HIV diagnoses from 5% in 2005 to 11.2% in 2012 (P = 0.06) and of the proportion of CRF02_AG HIV strains among primary HIV infections in 2011-2012 as compared to 2005-2010 (P = 0.055) was observed. Phylogenetic analysis revealed four transmission clusters including three transmission clusters among men having sex with men: two large clusters of nine CRF02_AG, six B HIV strains; and one small cluster of three B HIV strains. Clusters involved more frequently men (P = 0.01) belonging to caucasian ethicity (P = 0.05), with a higher HIV RNA load at inclusion (P = 0.03). These data highlight the importance of improving epidemiological surveillance and of implementing suitable prevention strategies to control the spread of HIV transmission among men having sex with men. © 2015 Wiley Periodicals, Inc.

Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling.

PubMed

Nguyen, Hien D; Ullmann, Jeremy F P; McLachlan, Geoffrey J; Voleti, Venkatakaushik; Li, Wenze; Hillman, Elizabeth M C; Reutens, David C; Janke, Andrew L

2018-02-01

Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescent proteins that bind to calcium ions. These techniques generate large volumes of spatially correlated time series. A model-based functional data analysis methodology via Gaussian mixtures is suggested for the clustering of data from such visualizations is proposed. The methodology is theoretically justified and a computationally efficient approach to estimation is suggested. An example analysis of a zebrafish imaging experiment is presented.

First-principles investigation of the dissociation and coupling of methane on small copper clusters: Interplay of collision dynamics and geometric and electronic effects

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

Varghese, Jithin J.; Mushrif, Samir H., E-mail: shmushrif@ntu.edu.sg

Small metal clusters exhibit unique size and morphology dependent catalytic activity. The search for alternate minimum energy pathways and catalysts to transform methane to more useful chemicals and carbon nanomaterials led us to investigate collision induced dissociation of methane on small Cu clusters. We report here for the first time, the free energy barriers for the collision induced activation, dissociation, and coupling of methane on small Cu clusters (Cu{sub n} where n = 2–12) using ab initio molecular dynamics and metadynamics simulations. The collision induced activation of the stretching and bending vibrations of methane significantly reduces the free energy barriermore » for its dissociation. Increase in the cluster size reduces the barrier for dissociation of methane due to the corresponding increase in delocalisation of electron density within the cluster, as demonstrated using the electron localisation function topology analysis. This enables higher probability of favourable alignment of the C–H stretching vibration of methane towards regions of high electron density within the cluster and makes higher number of sites available for the chemisorption of CH{sub 3} and H upon dissociation. These characteristics contribute in lowering the barrier for dissociation of methane. Distortion and reorganisation of cluster geometry due to high temperature collision dynamics disturb electron delocalisation within them and increase the barrier for dissociation. Coupling reactions of CH{sub x} (x = 1–3) species and recombination of H with CH{sub x} have free energy barriers significantly lower than complete dehydrogenation of methane to carbon. Thus, competition favours the former reactions at high hydrogen saturation on the clusters.« less

Outbreaks of syphilis among men who have sex with men attending STI clinics between 2007 and 2015 in the Netherlands: a space-time clustering study.

PubMed

van Aar, F; den Daas, C; van der Sande, M A B; Soetens, L C; de Vries, H J C; van Benthem, B H B

2017-09-01

Infectious syphilis (syphilis) is diagnosed predominantly among men who have sex with men (MSM) in the Netherlands and is a strong indicator for sexual risk behaviour. Therefore, an increase in syphilis can be an early indicator of resurgence of other STIs, including HIV. National and worldwide outbreaks of syphilis, as well as potential changes in sexual networks were reason to explore syphilis trends and clusters in more depth. National STI/HIV surveillance data were used, containing epidemiological, behavioural and clinical data from STI clinics. We examined syphilis positivity rates stratified by HIV status and year. Additionally, we performed space-time cluster analysis on municipality level between 2007 and 2015, using SaTScan to evaluate whether or not there was a higher than expected syphilis incidence in a certain area and time period, using the maximum likelihood ratio test statistic. Among HIV-positive MSM, the syphilis positivity rate decreased between 2007 (12.3%) and 2011 (4.5%), followed by an increasing trend (2015: 8.0%). Among HIV-negative MSM, the positivity rate decreased between 2007 (2.8%) and 2011 also (1.4%) and started to increase from 2013 onwards (2015: 1.8%). In addition, we identified three geospatial clusters. The first cluster consisted of MSM sex workers in the South of the Netherlands (July 2009-September 2010, n=10, p<0.001). The second cluster were mostly HIV-positive MSM (58.5%) (Amsterdam; July 2011-December 2015; n=1123, p<0.001), although the proportion of HIV-negative MSM increased over time. The third cluster was large in space (predominantly the city of Rotterdam; April-September 2015, n=72, p=0.014) and were mostly HIV-negative MSM (62.5%). Using SaTScan analysis, we observed several not yet recognised outbreaks and a rapid resurgence of syphilis among known HIV-positive MSM first, but more recently, also among HIV-negative MSM. The three identified clusters revealed locations, periods and specific characteristics of the involved MSM that could be used when developing targeted interventions. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Chemical structural analysis of diamondlike carbon films: II. Raman analysis

NASA Astrophysics Data System (ADS)

Takabayashi, Susumu; Ješko, Radek; Shinohara, Masanori; Hayashi, Hiroyuki; Sugimoto, Rintaro; Ogawa, Shuichi; Takakuwa, Yuji

2018-02-01

The chemical structure of diamondlike carbon (DLC) films, synthesized by photoemission-assisted glow discharge, has been analyzed by Raman spectroscopy. Raman analysis in conjunction with the sp2 cluster model clarified the film structure. The sp2 clusters in DLC films synthesized at low temperature preferred various aliphatic structures. Sufficient argon-ion assist allowed for formation of less strained DLC films containing large amounts of hydrogen. As the synthesis temperature was increased, thermal desorption of hydrogen left carbon dangling bonds with active unpaired electrons in the films, and the reactions that followed created strained films containing aromatic sp2 clusters. In parallel, the desorption of methane molecules from the growing surface by chemisorption of hydrogen radicals prevented the action of argon ions, promoting internal strain of the films. However, in synthesis at very high temperature, where sp2 clusters are sufficiently dominant, the strain was dissolved gradually. In contrast, the DLC films synthesized at low temperature were more stable than other films synthesized at the same temperature because of stable hydrogen-carbon bonds in the films.

How Much are Built Environments Changing, and Where?: Patterns of Change by Neighborhood Sociodemographic Characteristics across Seven U.S. Metropolitan Areas

PubMed Central

Hirsch, Jana A.; Grengs, Joe; Schulz, Amy; Adar, Sara D.; Rodriguez, Daniel A.; Brines, Shannon J.; Diez Roux, Ana V.

2016-01-01

Investments in neighborhood built environments could increase physical activity and overall health. Disproportionate distribution of these changes in advantaged neighborhoods could inflate health disparities. Little information exists on where changes are occurring. This paper aims to 1) identify changes in the built environment in neighborhoods and 2) investigate associations between high levels of change and sociodemographic characteristics. Using Geographic Information Systems, neighborhood land-use, local destinations (for walking, social engagement, and physical activity), and sociodemographics were characterized in 2000 and 2010 for seven U.S. cities. Linear and change on change models estimated associations of built environment changes with baseline (2000) and change (2010–2000) in sociodemographics. Spatial patterns were assessed using Global Moran’s I to measure overall clustering of change and Local Moran’s I to identify statistically significant clusters of high increases surrounded by high increases (HH). Sociodemographic characteristics were compared between HH cluster and other tracts using Analysis of Variance (ANOVA). We observed small land-use changes but increases in the destination types. Greater increases in destinations were associated with higher percentage non-Hispanic whites, percentage households with no vehicle, and median household income. Associations were present for both baseline sociodemographics and changes over time. Greater increases in destinations were associated with lower baseline percentage over 65 but higher increases in percentage over 65 between 2000 and 2010. Global Moran’s indicated changes were spatially clustered. HH cluster tracts started with a higher percentage non-Hispanic whites and higher percentage of households without vehicles. Between 2000 and 2010, HH cluster tracts experienced increases in percent non-Hispanic white, greater increases in median household income, and larger decreases in percent of households without a vehicle. Changes in the built environment are occurring in neighborhoods across a diverse set of U.S. metropolitan areas, but are patterned such that they may lead to increased health disparities over time. PMID:27701020

A highly efficient multi-core algorithm for clustering extremely large datasets

PubMed Central

2010-01-01

Background In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches for parallelizing algorithms largely rely on network communication protocols connecting and requiring multiple computers. One answer to this problem is to utilize the intrinsic capabilities in current multi-core hardware to distribute the tasks among the different cores of one computer. Results We introduce a multi-core parallelization of the k-means and k-modes cluster algorithms based on the design principles of transactional memory for clustering gene expression microarray type data and categorial SNP data. Our new shared memory parallel algorithms show to be highly efficient. We demonstrate their computational power and show their utility in cluster stability and sensitivity analysis employing repeated runs with slightly changed parameters. Computation speed of our Java based algorithm was increased by a factor of 10 for large data sets while preserving computational accuracy compared to single-core implementations and a recently published network based parallelization. Conclusions Most desktop computers and even notebooks provide at least dual-core processors. Our multi-core algorithms show that using modern algorithmic concepts, parallelization makes it possible to perform even such laborious tasks as cluster sensitivity and cluster number estimation on the laboratory computer. PMID:20370922

Identification of "binge-prone" women: an experimentally and psychometrically validated cluster analysis in a college population.

PubMed

Beebe, D W; Holmbeck, G N; Albright, J S; Noga, K; DeCastro, B

1995-01-01

This study investigated the escape model of binge eating through a cluster analysis using standardized measures. A sample of 126 undergraduate women underwent a manipulation of their level of cognition and were asked to "taste-test" several flavors of ice cream. Questionnaire data from these women were entered into a cluster analysis. Two groups emerged: women in the "binge-prone" group were significantly more depressed, had lower self-esteem, had more chaotic and extreme eating patterns, and were more self-conscious than those in the control group. In validation work, binge-prone women were shown to report elevated levels of bulimic symptomatology and, when in the presence of a food they enjoyed, to respond to increases in level of cognition by eating more. These results were consistent with some, but not all, of the components of the escape model.

Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale.

PubMed

Emmons, Scott; Kobourov, Stephen; Gallant, Mike; Börner, Katy

2016-01-01

Notions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms-Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Smart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters.

Validating clustering of molecular dynamics simulations using polymer models.

PubMed

Phillips, Joshua L; Colvin, Michael E; Newsam, Shawn

2011-11-14

Molecular dynamics (MD) simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our knowledge, our framework is the first to utilize model polymers to rigorously test the utility of clustering algorithms for studying biopolymers.

Validating clustering of molecular dynamics simulations using polymer models

PubMed Central

2011-01-01

Background Molecular dynamics (MD) simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. Results We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. Conclusions We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our knowledge, our framework is the first to utilize model polymers to rigorously test the utility of clustering algorithms for studying biopolymers. PMID:22082218

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

PubMed

Ju, Chunhua; Xu, Chonghuan

2013-01-01

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

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

PubMed Central

Ju, Chunhua

2013-01-01

Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods. PMID:24381525

RSAT 2015: Regulatory Sequence Analysis Tools.

PubMed

Medina-Rivera, Alejandra; Defrance, Matthieu; Sand, Olivier; Herrmann, Carl; Castro-Mondragon, Jaime A; Delerce, Jeremy; Jaeger, Sébastien; Blanchet, Christophe; Vincens, Pierre; Caron, Christophe; Staines, Daniel M; Contreras-Moreira, Bruno; Artufel, Marie; Charbonnier-Khamvongsa, Lucie; Hernandez, Céline; Thieffry, Denis; Thomas-Chollier, Morgane; van Helden, Jacques

2015-07-01

RSAT (Regulatory Sequence Analysis Tools) is a modular software suite for the analysis of cis-regulatory elements in genome sequences. Its main applications are (i) motif discovery, appropriate to genome-wide data sets like ChIP-seq, (ii) transcription factor binding motif analysis (quality assessment, comparisons and clustering), (iii) comparative genomics and (iv) analysis of regulatory variations. Nine new programs have been added to the 43 described in the 2011 NAR Web Software Issue, including a tool to extract sequences from a list of coordinates (fetch-sequences from UCSC), novel programs dedicated to the analysis of regulatory variants from GWAS or population genomics (retrieve-variation-seq and variation-scan), a program to cluster motifs and visualize the similarities as trees (matrix-clustering). To deal with the drastic increase of sequenced genomes, RSAT public sites have been reorganized into taxon-specific servers. The suite is well-documented with tutorials and published protocols. The software suite is available through Web sites, SOAP/WSDL Web services, virtual machines and stand-alone programs at http://www.rsat.eu/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Three estimates of the association between linear growth failure and cognitive ability.

PubMed

Cheung, Y B; Lam, K F

2009-09-01

To compare three estimators of association between growth stunting as measured by height-for-age Z-score and cognitive ability in children, and to examine the extent statistical adjustment for covariates is useful for removing confounding due to socio-economic status. Three estimators, namely random-effects, within- and between-cluster estimators, for panel data were used to estimate the association in a survey of 1105 pairs of siblings who were assessed for anthropometry and cognition. Furthermore, a 'combined' model was formulated to simultaneously provide the within- and between-cluster estimates. Random-effects and between-cluster estimators showed strong association between linear growth and cognitive ability, even after adjustment for a range of socio-economic variables. In contrast, the within-cluster estimator showed a much more modest association: For every increase of one Z-score in linear growth, cognitive ability increased by about 0.08 standard deviation (P < 0.001). The combined model verified that the between-cluster estimate was significantly larger than the within-cluster estimate (P = 0.004). Residual confounding by socio-economic situations may explain a substantial proportion of the observed association between linear growth and cognition in studies that attempt to control the confounding by means of multivariable regression analysis. The within-cluster estimator provides more convincing and modest results about the strength of association.

Prediction of strontium bromide laser efficiency using cluster and decision tree analysis

NASA Astrophysics Data System (ADS)

Iliev, Iliycho; Gocheva-Ilieva, Snezhana; Kulin, Chavdar

2018-01-01

Subject of investigation is a new high-powered strontium bromide (SrBr2) vapor laser emitting in multiline region of wavelengths. The laser is an alternative to the atom strontium lasers and electron free lasers, especially at the line 6.45 μm which line is used in surgery for medical processing of biological tissues and bones with minimal damage. In this paper the experimental data from measurements of operational and output characteristics of the laser are statistically processed by means of cluster analysis and tree-based regression techniques. The aim is to extract the more important relationships and dependences from the available data which influence the increase of the overall laser efficiency. There are constructed and analyzed a set of cluster models. It is shown by using different cluster methods that the seven investigated operational characteristics (laser tube diameter, length, supplied electrical power, and others) and laser efficiency are combined in 2 clusters. By the built regression tree models using Classification and Regression Trees (CART) technique there are obtained dependences to predict the values of efficiency, and especially the maximum efficiency with over 95% accuracy.

Using data mining to segment healthcare markets from patients' preference perspectives.

PubMed

Liu, Sandra S; Chen, Jie

2009-01-01

This paper aims to provide an example of how to use data mining techniques to identify patient segments regarding preferences for healthcare attributes and their demographic characteristics. Data were derived from a number of individuals who received in-patient care at a health network in 2006. Data mining and conventional hierarchical clustering with average linkage and Pearson correlation procedures are employed and compared to show how each procedure best determines segmentation variables. Data mining tools identified three differentiable segments by means of cluster analysis. These three clusters have significantly different demographic profiles. The study reveals, when compared with traditional statistical methods, that data mining provides an efficient and effective tool for market segmentation. When there are numerous cluster variables involved, researchers and practitioners need to incorporate factor analysis for reducing variables to clearly and meaningfully understand clusters. Interests and applications in data mining are increasing in many businesses. However, this technology is seldom applied to healthcare customer experience management. The paper shows that efficient and effective application of data mining methods can aid the understanding of patient healthcare preferences.

Analysis of the effects of the global financial crisis on the Turkish economy, using hierarchical methods

NASA Astrophysics Data System (ADS)

Kantar, Ersin; Keskin, Mustafa; Deviren, Bayram

2012-04-01

We have analyzed the topology of 50 important Turkish companies for the period 2006-2010 using the concept of hierarchical methods (the minimal spanning tree (MST) and hierarchical tree (HT)). We investigated the statistical reliability of links between companies in the MST by using the bootstrap technique. We also used the average linkage cluster analysis (ALCA) technique to observe the cluster structures much better. The MST and HT are known as useful tools to perceive and detect global structure, taxonomy, and hierarchy in financial data. We obtained four clusters of companies according to their proximity. We also observed that the Banks and Holdings cluster always forms in the centre of the MSTs for the periods 2006-2007, 2008, and 2009-2010. The clusters match nicely with their common production activities or their strong interrelationship. The effects of the Automobile sector increased after the global financial crisis due to the temporary incentives provided by the Turkish government. We find that Turkish companies were not very affected by the global financial crisis.

On the Surface Mapping using Individual Cluster Impacts

PubMed Central

Fernandez-Lima, F.A.; Eller, M.J.; DeBord, J.D.; Verkhoturov, S.V.; Della-Negra, S.; Schweikert, E.A.

2011-01-01

This paper describes the advantages of using single impacts of large cluster projectiles (e.g. C60 and Au400) for surface mapping and characterization. The analysis of co-emitted time-resolved photon spectra, electron distributions and characteristic secondary ions shows that they can be used as surface fingerprints for target composition, morphology and structure. Photon, electron and secondary ion emission increases with the projectile cluster size and energy. The observed, high abundant secondary ion emission makes cluster projectiles good candidates for surface mapping of atomic and fragment ions (e.g., yield >1 per nominal mass) and molecular ions (e.g., few tens of percent in the 500 < m/z < 1500 range). PMID:22393269

Clustangles: An Open Library for Clustering Angular Data.

PubMed

Sargsyan, Karen; Hua, Yun Hao; Lim, Carmay

2015-08-24

Dihedral angles are good descriptors of the numerous conformations visited by large, flexible systems, but their analysis requires directional statistics. A single package including the various multivariate statistical methods for angular data that accounts for the distinct topology of such data does not exist. Here, we present a lightweight standalone, operating-system independent package called Clustangles to fill this gap. Clustangles will be useful in analyzing the ever-increasing number of structures in the Protein Data Bank and clustering the copious conformations from increasingly long molecular dynamics simulations.

Investigating the usefulness of a cluster-based trend analysis to detect visual field progression in patients with open-angle glaucoma.

PubMed

Aoki, Shuichiro; Murata, Hiroshi; Fujino, Yuri; Matsuura, Masato; Miki, Atsuya; Tanito, Masaki; Mizoue, Shiro; Mori, Kazuhiko; Suzuki, Katsuyoshi; Yamashita, Takehiro; Kashiwagi, Kenji; Hirasawa, Kazunori; Shoji, Nobuyuki; Asaoka, Ryo

2017-12-01

To investigate the usefulness of the Octopus (Haag-Streit) EyeSuite's cluster trend analysis in glaucoma. Ten visual fields (VFs) with the Humphrey Field Analyzer (Carl Zeiss Meditec), spanning 7.7 years on average were obtained from 728 eyes of 475 primary open angle glaucoma patients. Mean total deviation (mTD) trend analysis and EyeSuite's cluster trend analysis were performed on various series of VFs (from 1st to 10th: VF1-10 to 6th to 10th: VF6-10). The results of the cluster-based trend analysis, based on different lengths of VF series, were compared against mTD trend analysis. Cluster-based trend analysis and mTD trend analysis results were significantly associated in all clusters and with all lengths of VF series. Between 21.2% and 45.9% (depending on VF series length and location) of clusters were deemed to progress when the mTD trend analysis suggested no progression. On the other hand, 4.8% of eyes were observed to progress using the mTD trend analysis when cluster trend analysis suggested no progression in any two (or more) clusters. Whole field trend analysis can miss local VF progression. Cluster trend analysis appears as robust as mTD trend analysis and useful to assess both sectorial and whole field progression. Cluster-based trend analyses, in particular the definition of two or more progressing cluster, may help clinicians to detect glaucomatous progression in a timelier manner than using a whole field trend analysis, without significantly compromising specificity. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Formation of fivefold axes in the FCC-metal nanoclusters

NASA Astrophysics Data System (ADS)

Myasnichenko, Vladimir S.; Starostenkov, Mikhail D.

2012-11-01

Formation of atomistic structures of metallic Cu, Au, Ag clusters and bimetallic Cu-Au clusters was studied with the help of molecular dynamics using the many-body tight-binding interatomic potential. The simulation of the crystallization process of clusters with the number of atoms ranging from 300 to 1092 was carried out. The most stable configurations of atoms in the system, corresponding to the minimum of potential energy, was found during super-fast cooling from 1000 K. Atoms corresponding to fcc, hcp, and Ih phases were identified by the method of common neighbor analysis. Incomplete icosahedral core can be discovered at the intersection of one of the Ih axes with the surface of monometallic cluster. The decahedron-shaped structure of bimetallic Cu-Au cluster with seven completed icosahedral cores was obtained. The principles of the construction of small bimetallic clusters with icosahedral symmetry and increased fractal dimensionality were offered.

Time-resolved x-ray imaging of a laser-induced nanoplasma and its neutral residuals

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

Fluckiger, L.; Rupp, D.; Adolph, M.

The evolution of individual, large gas-phase xenon clusters, turned into a nanoplasma by a high power infrared laser pulse, is tracked from femtoseconds up to nanoseconds after laser excitation via coherent diffractive imaging, using ultra-short soft x-ray free electron laser pulses. A decline of scattering signal at high detection angles with increasing time delay indicates a softening of the cluster surface. Here we demonstrate, for the first time a representative speckle pattern of a new stage of cluster expansion for xenon clusters after a nanosecond irradiation. The analysis of the measured average speckle size and the envelope of the intensitymore » distribution reveals a mean cluster size and length scale of internal density fluctuations. Furthermore, the measured diffraction patterns were reproduced by scattering simulations which assumed that the cluster expands with pronounced internal density fluctuations hundreds of picoseconds after excitation.« less

Computer simulations of dendrimer-polyelectrolyte complexes.

PubMed

Pandav, Gunja; Ganesan, Venkat

2014-08-28

We carry out a systematic analysis of static properties of the clusters formed by complexation between charged dendrimers and linear polyelectrolyte (LPE) chains in a dilute solution under good solvent conditions. We use single chain in mean-field simulations and analyze the structure of the clusters through radial distribution functions of the dendrimer, cluster size, and charge distributions. The effects of LPE length, charge ratio between LPE and dendrimer, the influence of salt concentration, and the dendrimer generation number are examined. Systems with short LPEs showed a reduced propensity for aggregation with dendrimers, leading to formation of smaller clusters. In contrast, larger dendrimers and longer LPEs lead to larger clusters with significant bridging. Increasing salt concentration was seen to reduce aggregation between dendrimers as a result of screening of electrostatic interactions. Generally, maximum complexation was observed in systems with an equal amount of net dendrimer and LPE charges, whereas either excess LPE or dendrimer concentrations resulted in reduced clustering between dendrimers.

Time-resolved x-ray imaging of a laser-induced nanoplasma and its neutral residuals

DOE PAGES

Fluckiger, L.; Rupp, D.; Adolph, M.; ...

2016-04-13

The evolution of individual, large gas-phase xenon clusters, turned into a nanoplasma by a high power infrared laser pulse, is tracked from femtoseconds up to nanoseconds after laser excitation via coherent diffractive imaging, using ultra-short soft x-ray free electron laser pulses. A decline of scattering signal at high detection angles with increasing time delay indicates a softening of the cluster surface. Here we demonstrate, for the first time a representative speckle pattern of a new stage of cluster expansion for xenon clusters after a nanosecond irradiation. The analysis of the measured average speckle size and the envelope of the intensitymore » distribution reveals a mean cluster size and length scale of internal density fluctuations. Furthermore, the measured diffraction patterns were reproduced by scattering simulations which assumed that the cluster expands with pronounced internal density fluctuations hundreds of picoseconds after excitation.« less

Analysis of β-Subgroup Proteobacterial Ammonia Oxidizer Populations in Soil by Denaturing Gradient Gel Electrophoresis Analysis and Hierarchical Phylogenetic Probing

PubMed Central

Stephen, John R.; Kowalchuk, George A.; Bruns, Mary-Ann V.; McCaig, Allison E.; Phillips, Carol J.; Embley, T. Martin; Prosser, James I.

1998-01-01

A combination of denaturing gradient gel electrophoresis (DGGE) and oligonucleotide probing was used to investigate the influence of soil pH on the compositions of natural populations of autotrophic β-subgroup proteobacterial ammonia oxidizers. PCR primers specific to this group were used to amplify 16S ribosomal DNA (rDNA) from soils maintained for 36 years at a range of pH values, and PCR products were analyzed by DGGE. Genus- and cluster-specific probes were designed to bind to sequences within the region amplified by these primers. A sequence specific to all β-subgroup ammonia oxidizers could not be identified, but probes specific for Nitrosospira clusters 1 to 4 and Nitrosomonas clusters 6 and 7 (J. R. Stephen, A. E. McCaig, Z. Smith, J. I. Prosser, and T. M. Embley, Appl. Environ. Microbiol. 62:4147–4154, 1996) were designed. Elution profiles of probes against target sequences and closely related nontarget sequences indicated a requirement for high-stringency hybridization conditions to distinguish between different clusters. DGGE banding patterns suggested the presence of Nitrosomonas cluster 6a and Nitrosospira clusters 2, 3, and 4 in all soil plots, but results were ambiguous because of overlapping banding patterns. Unambiguous band identification of the same clusters was achieved by combined DGGE and probing of blots with the cluster-specific radiolabelled probes. The relative intensities of hybridization signals provided information on the apparent selection of different Nitrosospira genotypes in samples of soil of different pHs. The signal from the Nitrosospira cluster 3 probe decreased significantly, relative to an internal control probe, with decreasing soil pH in the range of 6.6 to 3.9, while Nitrosospira cluster 2 hybridization signals increased with increasing soil acidity. Signals from Nitrosospira cluster 4 were greatest at pH 5.5, decreasing at lower and higher values, while Nitrosomonas cluster 6a signals did not vary significantly with pH. These findings are in agreement with a previous molecular study (J. R. Stephen, A. E. McCaig, Z. Smith, J. I. Prosser, and T. M. Embley, Appl. Environ. Microbiol 62:4147–4154, 1996) of the same sites, which demonstrated the presence of the same four clusters of ammonia oxidizers and indicated that selection might be occurring for clusters 2 and 3 at acid and neutral pHs, respectively. The two studies used different sets of PCR primers for amplification of 16S rDNA sequences from soil, and the similar findings suggest that PCR bias was unlikely to be a significant factor. The present study demonstrates the value of DGGE and probing for rapid analysis of natural soil communities of β-subgroup proteobacterial ammonia oxidizers, indicates significant pH-associated differences in Nitrosospira populations, and suggests that Nitrosospira cluster 2 may be of significance for ammonia-oxidizing activity in acid soils. PMID:9687457

The observed clustering of damaging extratropical cyclones in Europe

NASA Astrophysics Data System (ADS)

Cusack, Stephen

2016-04-01

The clustering of severe European windstorms on annual timescales has substantial impacts on the (re-)insurance industry. Our knowledge of the risk is limited by large uncertainties in estimates of clustering from typical historical storm data sets covering the past few decades. Eight storm data sets are gathered for analysis in this study in order to reduce these uncertainties. Six of the data sets contain more than 100 years of severe storm information to reduce sampling errors, and observational errors are reduced by the diversity of information sources and analysis methods between storm data sets. All storm severity measures used in this study reflect damage, to suit (re-)insurance applications. The shortest storm data set of 42 years provides indications of stronger clustering with severity, particularly for regions off the main storm track in central Europe and France. However, clustering estimates have very large sampling and observational errors, exemplified by large changes in estimates in central Europe upon removal of one stormy season, 1989/1990. The extended storm records place 1989/1990 into a much longer historical context to produce more robust estimates of clustering. All the extended storm data sets show increased clustering between more severe storms from return periods (RPs) of 0.5 years to the longest measured RPs of about 20 years. Further, they contain signs of stronger clustering off the main storm track, and weaker clustering for smaller-sized areas, though these signals are more uncertain as they are drawn from smaller data samples. These new ultra-long storm data sets provide new information on clustering to improve our management of this risk.

Changing the paradigm: messages for hand hygiene education and audit from cluster analysis.

PubMed

Gould, D J; Navaie, D; Purssell, E; Drey, N S; Creedon, S

2018-04-01

Hand hygiene is considered to be the foremost infection prevention measure. How healthcare workers accept and make sense of the hand hygiene message is likely to contribute to the success and sustainability of initiatives to improve performance, which is often poor. A survey of nurses in critical care units in three National Health Service trusts in England was undertaken to explore opinions about hand hygiene, use of alcohol hand rubs, audit with performance feedback, and other key hand-hygiene-related issues. Data were analysed descriptively and subjected to cluster analysis. Three main clusters of opinion were visualized, each forming a significant group: positive attitudes, pragmatism and scepticism. A smaller cluster suggested possible guilt about ability to perform hand hygiene. Cluster analysis identified previously unsuspected constellations of beliefs about hand hygiene that offer a plausible explanation for behaviour. Healthcare workers might respond to education and audit differently according to these beliefs. Those holding predominantly positive opinions might comply with hand hygiene policy and perform well as infection prevention link nurses and champions. Those holding pragmatic attitudes are likely to respond favourably to the need for professional behaviour and need to protect themselves from infection. Greater persuasion may be needed to encourage those who are sceptical about the importance of hand hygiene to comply with guidelines. Interventions to increase compliance should be sufficiently broad in scope to tackle different beliefs. Alternatively, cluster analysis of hand hygiene beliefs could be used to identify the most effective educational and monitoring strategies for a particular clinical setting. Copyright © 2017 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

Validity analysis on merged and averaged data using within and between analysis: focus on effect of qualitative social capital on self-rated health.

PubMed

Shin, Sang Soo; Shin, Young-Jeon

2016-01-01

With an increasing number of studies highlighting regional social capital (SC) as a determinant of health, many studies are using multi-level analysis with merged and averaged scores of community residents' survey responses calculated from community SC data. Sufficient examination is required to validate if the merged and averaged data can represent the community. Therefore, this study analyzes the validity of the selected indicators and their applicability in multi-level analysis. Within and between analysis (WABA) was performed after creating community variables using merged and averaged data of community residents' responses from the 2013 Community Health Survey in Korea, using subjective self-rated health assessment as a dependent variable. Further analysis was performed following the model suggested by WABA result. Both E-test results (1) and WABA results (2) revealed that single-level analysis needs to be performed using qualitative SC variable with cluster mean centering. Through single-level multivariate regression analysis, qualitative SC with cluster mean centering showed positive effect on self-rated health (0.054, p<0.001), although there was no substantial difference in comparison to analysis using SC variables without cluster mean centering or multi-level analysis. As modification in qualitative SC was larger within the community than between communities, we validate that relational analysis of individual self-rated health can be performed within the group, using cluster mean centering. Other tests besides the WABA can be performed in the future to confirm the validity of using community variables and their applicability in multi-level analysis.

Cluster analysis to estimate the risk of preeclampsia in the high-risk Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction (PREDO) study

PubMed Central

Marttinen, Pekka; Gillberg, Jussi; Lokki, A. Inkeri; Majander, Kerttu; Ordén, Maija-Riitta; Taipale, Pekka; Pesonen, Anukatriina; Räikkönen, Katri; Hämäläinen, Esa; Kajantie, Eero; Laivuori, Hannele

2017-01-01

Objectives Preeclampsia is divided into early-onset (delivery before 34 weeks of gestation) and late-onset (delivery at or after 34 weeks) subtypes, which may rise from different etiopathogenic backgrounds. Early-onset disease is associated with placental dysfunction. Late-onset disease develops predominantly due to metabolic disturbances, obesity, diabetes, lipid dysfunction, and inflammation, which affect endothelial function. Our aim was to use cluster analysis to investigate clinical factors predicting the onset and severity of preeclampsia in a cohort of women with known clinical risk factors. Methods We recruited 903 pregnant women with risk factors for preeclampsia at gestational weeks 12+0–13+6. Each individual outcome diagnosis was independently verified from medical records. We applied a Bayesian clustering algorithm to classify the study participants to clusters based on their particular risk factor combination. For each cluster, we computed the risk ratio of each disease outcome, relative to the risk in the general population. Results The risk of preeclampsia increased exponentially with respect to the number of risk factors. Our analysis revealed 25 number of clusters. Preeclampsia in a previous pregnancy (n = 138) increased the risk of preeclampsia 8.1 fold (95% confidence interval (CI) 5.7–11.2) compared to a general population of pregnant women. Having a small for gestational age infant (n = 57) in a previous pregnancy increased the risk of early-onset preeclampsia 17.5 fold (95%CI 2.1–60.5). Cluster of those two risk factors together (n = 21) increased the risk of severe preeclampsia to 23.8-fold (95%CI 5.1–60.6), intermediate onset (delivery between 34+0–36+6 weeks of gestation) to 25.1-fold (95%CI 3.1–79.9) and preterm preeclampsia (delivery before 37+0 weeks of gestation) to 16.4-fold (95%CI 2.0–52.4). Body mass index over 30 kg/m2 (n = 228) as a sole risk factor increased the risk of preeclampsia to 2.1-fold (95%CI 1.1–3.6). Together with preeclampsia in an earlier pregnancy the risk increased to 11.4 (95%CI 4.5–20.9). Chronic hypertension (n = 60) increased the risk of preeclampsia 5.3-fold (95%CI 2.4–9.8), of severe preeclampsia 22.2-fold (95%CI 9.9–41.0), and risk of early-onset preeclampsia 16.7-fold (95%CI 2.0–57.6). If a woman had chronic hypertension combined with obesity, gestational diabetes and earlier preeclampsia, the risk of term preeclampsia increased 4.8-fold (95%CI 0.1–21.7). Women with type 1 diabetes mellitus had a high risk of all subgroups of preeclampsia. Conclusion The risk of preeclampsia increases exponentially with respect to the number of risk factors. Early-onset preeclampsia and severe preeclampsia have different risk profile from term preeclampsia. PMID:28350823

Amplification of the entire kanamycin biosynthetic gene cluster during empirical strain improvement of Streptomyces kanamyceticus.

PubMed

Yanai, Koji; Murakami, Takeshi; Bibb, Mervyn

2006-06-20

Streptomyces kanamyceticus 12-6 is a derivative of the wild-type strain developed for industrial kanamycin (Km) production. Southern analysis and DNA sequencing revealed amplification of a large genomic segment including the entire Km biosynthetic gene cluster in the chromosome of strain 12-6. At 145 kb, the amplifiable unit of DNA (AUD) is the largest AUD reported in Streptomyces. Striking repetitive DNA sequences belonging to the clustered regularly interspaced short palindromic repeats family were found in the AUD and may play a role in its amplification. Strain 12-6 contains a mixture of different chromosomes with varying numbers of AUDs, sometimes exceeding 36 copies and producing an amplified region >5.7 Mb. The level of Km production depended on the copy number of the Km biosynthetic gene cluster, suggesting that DNA amplification occurred during strain improvement as a consequence of selection for increased Km resistance. Amplification of DNA segments including entire antibiotic biosynthetic gene clusters might be a common mechanism leading to increased antibiotic production in industrial strains.

[Spatial analysis of autumn-winter type scrub typhus in Shandong province, 2006-2014].

PubMed

Yang, H; Bi, Z W; Kou, Z Q; Zheng, L; Zhao, Z T

2016-05-01

To discuss the spatial-temporal distribution and epidemic trends of autumn-winter type scrub typhus in Shandong province, and provide scientific evidence for further study for the prevention and control of the disease. The scrub typhus surveillance data during 2006-2014 were collected from Shandong Disease Reporting Information System. The data was analyzed by using software ArcGIS 9.3(ESRI Inc., Redlands, CA, USA), GeoDa 0.9.5-i and SatScan 9.1.1. The Moran' s I, log-likelihood ratio(LLR), relative risk(RR)were calculated and the incidence choropleth maps, local indicators of spatial autocorrelation cluster maps and space scaning cluster maps were drawn. A total of 4 453 scrub typhus cases were reported during 2006-2014, and the annual incidence increased with year. Among the 17 prefectures(municipality)in Shandong, 13 were affected by scrub typhus. The global Moran's I index was 0.501 5(P<0.01). The differences in local Moran' s I index among 16 prefectures were significant(P<0.01). The " high-high" clustering areas were mainly Wulian county, Lanshan district and Juxian county of Rizhao, Xintai county of Tai' an, Gangcheng and Laicheng districts of Laiwu, Yiyuan county of Zibo and Mengyin county of Linyi. Spatial scan analysis showed that an eastward moving trend of high-risk clusters and two new high-risk clusters were found in Zaozhuang in 2014. The centers of the most likely clusters were in the south central mountainous areas during 2006-2010 and in 2012, eastern hilly areas in 2011, 2013 and 2014, and the size of the clusters expanded in 2008, 2011, 2013 and 2014. One spatial-temporal cluster was detected from October 1, 2014 to November 30, 2014, the center of the cluster was in Rizhao and the radius was 222.34 kilometers. A positive spatial correlation and spatial agglomerations were found in the distribution of autumn-winter type scrub typhus in Shandong. Since 2006, the epidemic area of the disease has expanded and the number of high-risk areas has increased. Moreover, the eastward moving and periodically expanding trends of high-risk clusters were detected.

Networking between community health programs: a case study outlining the effectiveness, barriers and enablers

PubMed Central

2012-01-01

Background In India, since the 1990s, there has been a burgeoning of NGOs involved in providing primary health care. This has resulted in a complex NGO-Government interface which is difficult for lone NGOs to navigate. The Uttarakhand Cluster, India, links such small community health programs together to build NGO capacity, increase visibility and better link to the government schemes and the formal healthcare system. This research, undertaken between 1998 and 2011, aims to examine barriers and facilitators to such linking, or clustering, and the effectiveness of this clustering approach. Methods Interviews, indicator surveys and participant observation were used to document the process and explore the enablers, the barriers and the effectiveness of networks improving community health. Results The analysis revealed that when activating, framing, mobilising and synthesizing the Uttarakhand Cluster, key brokers and network players were important in bridging between organisations. The ties (or relationships) that held the cluster together included homophily around common faith, common friendships and geographical location and common mission. Self interest whereby members sought funds, visibility, credibility, increased capacity and access to trainings was also a commonly identified motivating factor for networking. Barriers to network synthesizing included lack of funding, poor communication, limited time and lack of human resources. Risk aversion and mistrust remained significant barriers to overcome for such a network. Conclusions In conclusion, specific enabling factors allowed the clustering approach to be effective at increasing access to resources, creating collaborative opportunities and increasing visibility, credibility and confidence of the cluster members. These findings add to knowledge regarding social network formation and collaboration, and such knowledge will assist in the conceptualisation, formation and success of potential health networks in India and other developing world countries. PMID:22812627

Determination of clusters and factors associated with dengue dispersion during the first epidemic related to Dengue virus serotype 4 in Vitória, Brazil

PubMed Central

Herbinger, Karl-Heinz; Cerutti Junior, Crispim; Malta Romano, Camila; de Souza Areias Cabidelle, Aline; Fröschl, Günter

2017-01-01

Dengue occurrence is partially influenced by the immune status of the population. Consequently, the introduction of a new Dengue virus serotype can trigger explosive epidemics in susceptible populations. The determination of clusters in this scenario can help to identify hotspots and understand the disease dispersion regardless of the influence of the population herd immunity. The present study evaluated the pattern and factors associated with dengue dispersion during the first epidemic related to Dengue virus serotype 4 in Vitória, Espírito Santo state, Brazil. Data on 18,861 dengue cases reported in Vitória from September 2012 to June 2013 were included in the study. The analysis of spatial variation in temporal trend was performed to detect clusters that were compared by their respective relative risk, house index, population density, and income in an ecological study. Overall, 11 clusters were detected. The time trend increase of dengue incidence in the overall study population was 636%. The five clusters that showed a lower time trend increase than the overall population presented a higher incidence in the beginning of the epidemic and, compared to the six clusters with higher time trend increase, they presented higher relative risk for their inhabitants to acquire dengue infection (P-value = 0.02) and a lower income (P-value <0.01). House index and population density did not differ between the clusters. Early increase of dengue incidence and higher relative risk for acquiring dengue infection were favored in low-income areas. Preventive actions and improvement of infrastructure in low-income areas should be prioritized in order to diminish the magnitude of dengue dispersion after the introduction of a new serotype. PMID:28388694

Networking between community health programs: a case study outlining the effectiveness, barriers and enablers.

PubMed

Grills, Nathan J; Robinson, Priscilla; Phillip, Maneesh

2012-07-19

In India, since the 1990s, there has been a burgeoning of NGOs involved in providing primary health care. This has resulted in a complex NGO-Government interface which is difficult for lone NGOs to navigate. The Uttarakhand Cluster, India, links such small community health programs together to build NGO capacity, increase visibility and better link to the government schemes and the formal healthcare system. This research, undertaken between 1998 and 2011, aims to examine barriers and facilitators to such linking, or clustering, and the effectiveness of this clustering approach. Interviews, indicator surveys and participant observation were used to document the process and explore the enablers, the barriers and the effectiveness of networks improving community health. The analysis revealed that when activating, framing, mobilising and synthesizing the Uttarakhand Cluster, key brokers and network players were important in bridging between organisations. The ties (or relationships) that held the cluster together included homophily around common faith, common friendships and geographical location and common mission. Self interest whereby members sought funds, visibility, credibility, increased capacity and access to trainings was also a commonly identified motivating factor for networking. Barriers to network synthesizing included lack of funding, poor communication, limited time and lack of human resources. Risk aversion and mistrust remained significant barriers to overcome for such a network. In conclusion, specific enabling factors allowed the clustering approach to be effective at increasing access to resources, creating collaborative opportunities and increasing visibility, credibility and confidence of the cluster members. These findings add to knowledge regarding social network formation and collaboration, and such knowledge will assist in the conceptualisation, formation and success of potential health networks in India and other developing world countries.

Subtypes of female juvenile offenders: a cluster analysis of the Millon Adolescent Clinical Inventory.

PubMed

Stefurak, Tres; Calhoun, Georgia B

2007-01-01

The current study sought to explore subtypes of adolescents within a sample of female juvenile offenders. Using the Millon Adolescent Clinical Inventory with 101 female juvenile offenders, a two-step cluster analysis was performed beginning with a Ward's method hierarchical cluster analysis followed by a K-Means iterative partitioning cluster analysis. The results suggest an optimal three-cluster solution, with cluster profiles leading to the following group labels: Externalizing Problems, Depressed/Interpersonally Ambivalent, and Anxious Prosocial. Analysis along the factors of age, race, offense typology and offense chronicity were conducted to further understand the nature of found clusters. Only the effect for race was significant with the Anxious Prosocial and Depressed Intepersonally Ambivalent clusters appearing disproportionately comprised of African American girls. To establish external validity, clusters were compared across scales of the Behavioral Assessment System for Children - Self Report of Personality, and corroborative distinctions between clusters were found here.

Common factor analysis versus principal component analysis: choice for symptom cluster research.

PubMed

Kim, Hee-Ju

2008-03-01

The purpose of this paper is to examine differences between two factor analytical methods and their relevance for symptom cluster research: common factor analysis (CFA) versus principal component analysis (PCA). Literature was critically reviewed to elucidate the differences between CFA and PCA. A secondary analysis (N = 84) was utilized to show the actual result differences from the two methods. CFA analyzes only the reliable common variance of data, while PCA analyzes all the variance of data. An underlying hypothetical process or construct is involved in CFA but not in PCA. PCA tends to increase factor loadings especially in a study with a small number of variables and/or low estimated communality. Thus, PCA is not appropriate for examining the structure of data. If the study purpose is to explain correlations among variables and to examine the structure of the data (this is usual for most cases in symptom cluster research), CFA provides a more accurate result. If the purpose of a study is to summarize data with a smaller number of variables, PCA is the choice. PCA can also be used as an initial step in CFA because it provides information regarding the maximum number and nature of factors. In using factor analysis for symptom cluster research, several issues need to be considered, including subjectivity of solution, sample size, symptom selection, and level of measure.

[Cluster analysis in biomedical researches].

PubMed

Akopov, A S; Moskovtsev, A A; Dolenko, S A; Savina, G D

2013-01-01

Cluster analysis is one of the most popular methods for the analysis of multi-parameter data. The cluster analysis reveals the internal structure of the data, group the separate observations on the degree of their similarity. The review provides a definition of the basic concepts of cluster analysis, and discusses the most popular clustering algorithms: k-means, hierarchical algorithms, Kohonen networks algorithms. Examples are the use of these algorithms in biomedical research.

The observed clustering of damaging extra-tropical cyclones in Europe

NASA Astrophysics Data System (ADS)

Cusack, S.

2015-12-01

The clustering of severe European windstorms on annual timescales has substantial impacts on the re/insurance industry. Management of the risk is impaired by large uncertainties in estimates of clustering from historical storm datasets typically covering the past few decades. The uncertainties are unusually large because clustering depends on the variance of storm counts. Eight storm datasets are gathered for analysis in this study in order to reduce these uncertainties. Six of the datasets contain more than 100~years of severe storm information to reduce sampling errors, and the diversity of information sources and analysis methods between datasets sample observational errors. All storm severity measures used in this study reflect damage, to suit re/insurance applications. It is found that the shortest storm dataset of 42 years in length provides estimates of clustering with very large sampling and observational errors. The dataset does provide some useful information: indications of stronger clustering for more severe storms, particularly for southern countries off the main storm track. However, substantially different results are produced by removal of one stormy season, 1989/1990, which illustrates the large uncertainties from a 42-year dataset. The extended storm records place 1989/1990 into a much longer historical context to produce more robust estimates of clustering. All the extended storm datasets show a greater degree of clustering with increasing storm severity and suggest clustering of severe storms is much more material than weaker storms. Further, they contain signs of stronger clustering in areas off the main storm track, and weaker clustering for smaller-sized areas, though these signals are smaller than uncertainties in actual values. Both the improvement of existing storm records and development of new historical storm datasets would help to improve management of this risk.

Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Sosnowski, Scott; Lane, Terran

2012-01-01

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers-to-classifiers. The new capability has the potential to greatly expand the in situ analysis abilities of sensor networks. Classifiers seeking to categorize incoming data into different types of events can operate in tandem with clusterers that are sensitive to the occurrence of new kinds of events not known to the classifiers. In contrast to current approaches that treat these operations as independent components, a hybrid collaborative learning system can enable them to learn from each other.

Space-Time Analysis of Testicular Cancer Clusters Using Residential Histories: A Case-Control Study in Denmark

PubMed Central

Sloan, Chantel D.; Nordsborg, Rikke B.; Jacquez, Geoffrey M.; Raaschou-Nielsen, Ole; Meliker, Jaymie R.

2015-01-01

Though the etiology is largely unknown, testicular cancer incidence has seen recent significant increases in northern Europe and throughout many Western regions. The most common cancer in males under age 40, age period cohort models have posited exposures in the in utero environment or in early childhood as possible causes of increased risk of testicular cancer. Some of these factors may be tied to geography through being associated with behavioral, cultural, sociodemographic or built environment characteristics. If so, this could result in detectable geographic clusters of cases that could lead to hypotheses regarding environmental targets for intervention. Given a latency period between exposure to an environmental carcinogen and testicular cancer diagnosis, mobility histories are beneficial for spatial cluster analyses. Nearest-neighbor based Q-statistics allow for the incorporation of changes in residency in spatial disease cluster detection. Using these methods, a space-time cluster analysis was conducted on a population-wide case-control population selected from the Danish Cancer Registry with mobility histories since 1971 extracted from the Danish Civil Registration System. Cases (N=3297) were diagnosed between 1991 and 2003, and two sets of controls (N=3297 for each set) matched on sex and date of birth were included in the study. We also examined spatial patterns in maternal residential history for those cases and controls born in 1971 or later (N= 589 case-control pairs). Several small clusters were detected when aligning individuals by year prior to diagnosis, age at diagnosis and calendar year of diagnosis. However, the largest of these clusters contained only 2 statistically significant individuals at their center, and were not replicated in SaTScan spatial-only analyses which are less susceptible to multiple testing bias. We found little evidence of local clusters in residential histories of testicular cancer cases in this Danish population. PMID:25756204

Space-time analysis of testicular cancer clusters using residential histories: a case-control study in Denmark.

PubMed

Sloan, Chantel D; Nordsborg, Rikke B; Jacquez, Geoffrey M; Raaschou-Nielsen, Ole; Meliker, Jaymie R

2015-01-01

Though the etiology is largely unknown, testicular cancer incidence has seen recent significant increases in northern Europe and throughout many Western regions. The most common cancer in males under age 40, age period cohort models have posited exposures in the in utero environment or in early childhood as possible causes of increased risk of testicular cancer. Some of these factors may be tied to geography through being associated with behavioral, cultural, sociodemographic or built environment characteristics. If so, this could result in detectable geographic clusters of cases that could lead to hypotheses regarding environmental targets for intervention. Given a latency period between exposure to an environmental carcinogen and testicular cancer diagnosis, mobility histories are beneficial for spatial cluster analyses. Nearest-neighbor based Q-statistics allow for the incorporation of changes in residency in spatial disease cluster detection. Using these methods, a space-time cluster analysis was conducted on a population-wide case-control population selected from the Danish Cancer Registry with mobility histories since 1971 extracted from the Danish Civil Registration System. Cases (N=3297) were diagnosed between 1991 and 2003, and two sets of controls (N=3297 for each set) matched on sex and date of birth were included in the study. We also examined spatial patterns in maternal residential history for those cases and controls born in 1971 or later (N= 589 case-control pairs). Several small clusters were detected when aligning individuals by year prior to diagnosis, age at diagnosis and calendar year of diagnosis. However, the largest of these clusters contained only 2 statistically significant individuals at their center, and were not replicated in SaTScan spatial-only analyses which are less susceptible to multiple testing bias. We found little evidence of local clusters in residential histories of testicular cancer cases in this Danish population.

Tobacco price increases and population interest in smoking cessation in Japan between 2004 and 2016: a Google Trends analysis.

PubMed

Tabuchi, Takahiro; Fukui, Keisuke; Gallus, Silvano

2018-01-31

Tobacco price increases can generate increased public interest in smoking cessation, but it is not clear how long this interest lasts. Our objective was to evaluate the duration of the impact of cigarette price increase in Japan using Google search data. Monthly or daily aggregated Google search volume for smoking cessation in Japan from 2004 to 2016 was collected in terms of relative search volume (RSV) ranging from 0-100. Using monthly RSV data, we evaluated possible relationships between the RSVs and tobacco control measures in Japan. Time periods within which the impact of search volume significantly increased were identified by cluster detection test, using daily RSV data. A spike in RSV preceding the enforcement of a cigarette price increase revealed an anticipation effect. Between 2004 and 2016 the three highest monthly RSV spikes were observed in July 2006 (RSV=66), when cigarette prices increased by 11%, and in September (RSV=90) and October 2010 (RSV=100), when cigarette prices increased by 37%. Regarding daily RSV, the detected cluster size around the price increase in 2010 (52 days) was longer than that in 2006 (17 days). In 2010, a cluster period of 25 days before the date of the price increase was observed, suggesting an anticipation effect. After the onset of the price increase, a cluster of 27 days was detected. When the cigarette price increased due to consumption tax in April 2014, almost no anticipation effect was observed. The population impact of tobacco price increases on smoking cessation may be assessed using Google Trends data. The cluster indicates that a higher cigarette price increase had a higher and longer-lasting effect on population interest in cessation, but the impact may continue for a relatively short time. To examine the duration of the impact of cigarette price increases on population interest in smoking cessation in Japan, Google search data for smoking cessation was analyzed. Between 2004 and 2016 the three highest spikes of monthly relative search volume (RSV) were observed in October 2010, when cigarette prices increased by 37%. Analyzing daily RSV data, the detected cluster size around the price increase in 2010 was 52 days and a cluster period of 25 days before the date of the price increase was observed, suggesting an anticipation effect. The cluster indicates that a higher cigarette price increase had a higher and longer-lasting effect, but the population impact continues for a relatively short time. Further increases in the price of cigarettes are necessary. © The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

On the Distribution of Orbital Poles of Milky Way Satellites

NASA Astrophysics Data System (ADS)

Palma, Christopher; Majewski, Steven R.; Johnston, Kathryn V.

2002-01-01

In numerous studies of the outer Galactic halo some evidence for accretion has been found. If the outer halo did form in part or wholly through merger events, we might expect to find coherent streams of stars and globular clusters following orbits similar to those of their parent objects, which are assumed to be present or former Milky Way dwarf satellite galaxies. We present a study of this phenomenon by assessing the likelihood of potential descendant ``dynamical families'' in the outer halo. We conduct two analyses: one that involves a statistical analysis of the spatial distribution of all known Galactic dwarf satellite galaxies (DSGs) and globular clusters, and a second, more specific analysis of those globular clusters and DSGs for which full phase space dynamical data exist. In both cases our methodology is appropriate only to members of descendant dynamical families that retain nearly aligned orbital poles today. Since the Sagittarius dwarf (Sgr) is considered a paradigm for the type of merger/tidal interaction event for which we are searching, we also undertake a case study of the Sgr system and identify several globular clusters that may be members of its extended dynamical family. In our first analysis, the distribution of possible orbital poles for the entire sample of outer (Rgc>8 kpc) halo globular clusters is tested for statistically significant associations among globular clusters and DSGs. Our methodology for identifying possible associations is similar to that used by Lynden-Bell & Lynden-Bell, but we put the associations on a more statistical foundation. Moreover, we study the degree of possible dynamical clustering among various interesting ensembles of globular clusters and satellite galaxies. Among the ensembles studied, we find the globular cluster subpopulation with the highest statistical likelihood of association with one or more of the Galactic DSGs to be the distant, outer halo (Rgc>25 kpc), second-parameter globular clusters. The results of our orbital pole analysis are supported by the great circle cell count methodology of Johnston, Hernquist, & Bolte. The space motions of the clusters Pal 4, NGC 6229, NGC 7006, and Pyxis are predicted to be among those most likely to show the clusters to be following stream orbits, since these clusters are responsible for the majority of the statistical significance of the association between outer halo, second-parameter globular clusters and the Milky Way DSGs. In our second analysis, we study the orbits of the 41 globular clusters and six Milky Way-bound DSGs having measured proper motions to look for objects with both coplanar orbits and similar angular momenta. Unfortunately, the majority of globular clusters with measured proper motions are inner halo clusters that are less likely to retain memory of their original orbit. Although four potential globular cluster/DSG associations are found, we believe three of these associations involving inner halo clusters to be coincidental. While the present sample of objects with complete dynamical data is small and does not include many of the globular clusters that are more likely to have been captured by the Milky Way, the methodology we adopt will become increasingly powerful as more proper motions are measured for distant Galactic satellites and globular clusters, and especially as results from the Space Interferometry Mission (SIM) become available.

Prevalence and risk factors of seizure clusters in adult patients with epilepsy.

PubMed

Chen, Baibing; Choi, Hyunmi; Hirsch, Lawrence J; Katz, Austen; Legge, Alexander; Wong, Rebecca A; Jiang, Alfred; Kato, Kenneth; Buchsbaum, Richard; Detyniecki, Kamil

2017-07-01

In the current study, we explored the prevalence of physician-confirmed seizure clusters. We also investigated potential clinical factors associated with the occurrence of seizure clusters overall and by epilepsy type. We reviewed medical records of 4116 adult (≥16years old) outpatients with epilepsy at our centers for documentation of seizure clusters. Variables including patient demographics, epilepsy details, medical and psychiatric history, AED history, and epilepsy risk factors were then tested against history of seizure clusters. Patients were then divided into focal epilepsy, idiopathic generalized epilepsy (IGE), or symptomatic generalized epilepsy (SGE), and the same analysis was run. Overall, seizure clusters were independently associated with earlier age of seizure onset, symptomatic generalized epilepsy (SGE), central nervous system (CNS) infection, cortical dysplasia, status epilepticus, absence of 1-year seizure freedom, and having failed 2 or more AEDs (P<0.0026). Patients with SGE (27.1%) were more likely to develop seizure clusters than patients with focal epilepsy (16.3%) and IGE (7.4%; all P<0.001). Analysis by epilepsy type showed that absence of 1-year seizure freedom since starting treatment at one of our centers was associated with seizure clustering in patients across all 3 epilepsy types. In patients with SGE, clusters were associated with perinatal/congenital brain injury. In patients with focal epilepsy, clusters were associated with younger age of seizure onset, complex partial seizures, cortical dysplasia, status epilepticus, CNS infection, and having failed 2 or more AEDs. In patients with IGE, clusters were associated with presence of an aura. Only 43.5% of patients with seizure clusters were prescribed rescue medications. Patients with intractable epilepsy are at a higher risk of developing seizure clusters. Factors such as having SGE, CNS infection, cortical dysplasia, status epilepticus or an early seizure onset, can also independently increase one's chance of having seizure clusters. Copyright © 2017. Published by Elsevier B.V.

Probing the History of Galaxy Clusters with Metallicity and Entropy Measurements

NASA Astrophysics Data System (ADS)

Elkholy, Tamer Yohanna

Galaxy clusters are the largest gravitationally bound objects found today in our Universe. The gas they contain, the intra-cluster medium (ICM), is heated to temperatures in the approximate range of 1 to 10 keV, and thus emits X-ray radiation. Studying the ICM through the spatial and spectral analysis of its emission returns the richest information about both the overall cosmological context which governs the formation of clusters, as well as the physical processes occurring within. The aim of this thesis is to learn about the history of the physical processes that drive the evolution of galaxy clusters, through careful, spatially resolved measurements of their metallicity and entropy content. A sample of 45 nearby clusters observed with Chandra is analyzed to produce radial density, temperature, entropy and metallicity profiles. The entropy profiles are computed to larger radial extents than in previous Chandra analyses. The results of this analysis are made available to the scientific community in an electronic database. Comparing metallicity and entropy in the outskirts of clusters, we find no signature on the entropy profiles of the ensemble of supernovae that produced the observed metals. In the centers of clusters, we find that the metallicities of high-mass clusters are much less dispersed than those of low-mass clusters. A comparison of metallicity with the regularity of the X-ray emission morphology suggests that metallicities in low-mass clusters are more susceptible to increase from violent events such as mergers. We also find that the variation in the stellar-to-gas mass ratio as a function of cluster mass can explain the variation of central metallicity with cluster mass, only if we assume that there is a constant level of metallicity for clusters of all masses, above which the observed galaxies add more metals in proportion to their mass. (Copies available exclusively from MIT Libraries, libraries.mit.edu/docs - docs mit.edu)

Condom Use among Immigrant Latino Sexual Minorities: Multilevel Analysis after Respondent-Driven Sampling

PubMed Central

Rhodes, Scott D.; McCoy, Thomas P.

2014-01-01

This study explored correlates of condom use within a respondent-driven sample of 190 Spanish-speaking immigrant Latino sexual minorities, including gay and bisexual men, other men who have sex with men (MSM), and transgender person, in North Carolina. Five analytic approaches for modeling data collected using respondent-driven sampling (RDS) were compared. Across most approaches, knowledge of HIV and sexually transmitted infections (STIs) and increased condom use self-efficacy predicted consistent condom use and increased homophobia predicted decreased consistent condom use. The same correlates were not significant in all analyses but were consistent in most. Clustering due to recruitment chains was low, while clustering due to recruiter was substantial. This highlights the importance accounting for clustering when analyzing RDS data. PMID:25646728

The full-length microRNA cluster in the intron of large latency transcript is associated with the virulence of pseudorabies virus.

PubMed

Wang, Xin; Zhang, Mei-Mei; Yan, Kai; Tang, Qi; Wu, Yi-Quan; He, Wen-Bo; Chen, Huan-Chun; Liu, Zheng-Fei

2018-07-01

Pseudorabies virus (PRV), the etiological pathogen of Aujeszky's disease, belongs to the Alphaherpesvirus subfamily. Large latency transcript (LLT), the most abundant PRV transcript, harbors a ~ 4.6 kb microRNA (miRNA) cluster-encoding intron. To investigate the function of the LLT miRNA cluster during the life cycle of PRV, we generated a miRNA cluster mutation virus (PRV-∆miR cluster) and revertant virus. Analysis of the growth kinetics of PRV-ΔmiR cluster-infected cells revealed significantly smaller plaques and lower titers than the wild-type and revertant viruses. The mutation virus exhibited increased IE180 and decreased EP0 expression. The clinical symptoms observed in mice infected with PRV-ΔmiR cluster revealed that the miRNA cluster is involved in the pathogenesis of PRV. Physical parameters, virus shedding assays, and the SN 50 titers revealed that the miRNA cluster enhances PRV virulence in pigs. Collectively, our findings suggest that the full-length miRNA cluster is involved in PRV replication and virulence. Copyright © 2018 Elsevier Inc. All rights reserved.

Transcriptional regulation of gene expression clusters in motor neurons following spinal cord injury.

PubMed

Ryge, Jesper; Winther, Ole; Wienecke, Jacob; Sandelin, Albin; Westerdahl, Ann-Charlotte; Hultborn, Hans; Kiehn, Ole

2010-06-09

Spinal cord injury leads to neurological dysfunctions affecting the motor, sensory as well as the autonomic systems. Increased excitability of motor neurons has been implicated in injury-induced spasticity, where the reappearance of self-sustained plateau potentials in the absence of modulatory inputs from the brain correlates with the development of spasticity. Here we examine the dynamic transcriptional response of motor neurons to spinal cord injury as it evolves over time to unravel common gene expression patterns and their underlying regulatory mechanisms. For this we use a rat-tail-model with complete spinal cord transection causing injury-induced spasticity, where gene expression profiles are obtained from labeled motor neurons extracted with laser microdissection 0, 2, 7, 21 and 60 days post injury. Consensus clustering identifies 12 gene clusters with distinct time expression profiles. Analysis of these gene clusters identifies early immunological/inflammatory and late developmental responses as well as a regulation of genes relating to neuron excitability that support the development of motor neuron hyper-excitability and the reappearance of plateau potentials in the late phase of the injury response. Transcription factor motif analysis identifies differentially expressed transcription factors involved in the regulation of each gene cluster, shaping the expression of the identified biological processes and their associated genes underlying the changes in motor neuron excitability. This analysis provides important clues to the underlying mechanisms of transcriptional regulation responsible for the increased excitability observed in motor neurons in the late chronic phase of spinal cord injury suggesting alternative targets for treatment of spinal cord injury. Several transcription factors were identified as potential regulators of gene clusters containing elements related to motor neuron hyper-excitability, the manipulation of which potentially could be used to alter the transcriptional response to prevent the motor neurons from entering a state of hyper-excitability.

Transcriptional regulation of gene expression clusters in motor neurons following spinal cord injury

PubMed Central

2010-01-01

Background Spinal cord injury leads to neurological dysfunctions affecting the motor, sensory as well as the autonomic systems. Increased excitability of motor neurons has been implicated in injury-induced spasticity, where the reappearance of self-sustained plateau potentials in the absence of modulatory inputs from the brain correlates with the development of spasticity. Results Here we examine the dynamic transcriptional response of motor neurons to spinal cord injury as it evolves over time to unravel common gene expression patterns and their underlying regulatory mechanisms. For this we use a rat-tail-model with complete spinal cord transection causing injury-induced spasticity, where gene expression profiles are obtained from labeled motor neurons extracted with laser microdissection 0, 2, 7, 21 and 60 days post injury. Consensus clustering identifies 12 gene clusters with distinct time expression profiles. Analysis of these gene clusters identifies early immunological/inflammatory and late developmental responses as well as a regulation of genes relating to neuron excitability that support the development of motor neuron hyper-excitability and the reappearance of plateau potentials in the late phase of the injury response. Transcription factor motif analysis identifies differentially expressed transcription factors involved in the regulation of each gene cluster, shaping the expression of the identified biological processes and their associated genes underlying the changes in motor neuron excitability. Conclusions This analysis provides important clues to the underlying mechanisms of transcriptional regulation responsible for the increased excitability observed in motor neurons in the late chronic phase of spinal cord injury suggesting alternative targets for treatment of spinal cord injury. Several transcription factors were identified as potential regulators of gene clusters containing elements related to motor neuron hyper-excitability, the manipulation of which potentially could be used to alter the transcriptional response to prevent the motor neurons from entering a state of hyper-excitability. PMID:20534130

Primary syphilis cases in Guangdong Province 1995-2008: opportunities for linking syphilis control and regional development.

PubMed

Yang, Li-Gang; Tucker, Joseph D; Yang, Bin; Shen, Song-Ying; Sun, Xi-Feng; Chen, Yong-Feng; Chen, Xiang-Sheng

2010-12-30

Syphilis cases have risen in many parts of China, with developed regions reporting the greatest share of cases. Since syphilis increases in these areas are likely driven by both increased screening and changes in sexual behaviours, distinguishing between these two factors is important. Examining municipal-level primary syphilis cases with spatial analysis allows a more direct understanding of changing sexual behaviours at a more policy-relevant level. In this study we examined all reported primary syphilis cases from Guangdong Province, a southern province in China, since the disease was first incorporated into the mandatory reporting system in 1995. Spatial autocorrelation statistics were used to correlate municipal-level clustering of reported primary syphilis cases and gross domestic product (GDP). A total of 52,036 primary syphilis cases were reported over the period 1995-2008, and the primary syphilis cases increased from 0.88 per 100,000 population in 1995 to 7.61 per 100,000 in 2008. The Pearl River Delta region has a disproportionate share (44.7%) of syphilis cases compared to other regions. Syphilis cases were spatially clustered (p = 0.01) and Moran's I analysis found that syphilis cases were clustered in municipalities with higher GDP (p = 0.004). Primary syphilis cases continue to increase in Guangdong Province, especially in the Pearl River Delta region. Considering the economic impact of syphilis and its tendency to spatially cluster, expanded syphilis testing in specific municipalities and further investigating the costs and benefits of syphilis screening are critical next steps.

Editing ERTS-1 data to exclude land aids cluster analysis of water targets

NASA Technical Reports Server (NTRS)

Erb, R. B. (Principal Investigator)

1973-01-01

The author has identified the following significant results. It has been determined that an increase in the number of spectrally distinct coastal water types is achieved when data values over the adjacent land areas are excluded from the processing routine. This finding resulted from an automatic clustering analysis of ERTS-1 system corrected MSS scene 1002-18134 of 25 July 1972 over Monterey Bay, California. When the entire study area data set was submitted to the clustering only two distinct water classes were extracted. However, when the land area data points were removed from the data set and resubmitted to the clustering routine, four distinct groupings of water features were identified. Additionally, unlike the previous separation, the four types could be correlated to features observable in the associated ERTS-1 imagery. This exercise demonstrates that by proper selection of data submitted to the processing routine, based upon the specific application of study, additional information may be extracted from the ERTS-1 MSS data.

A Cross-Cultural Comparison of Symptom Reporting and Symptom Clusters in Heart Failure.

PubMed

Park, Jumin; Johantgen, Mary E

2017-07-01

An understanding of symptoms in heart failure (HF) among different cultural groups has become increasingly important. The purpose of this study was to compare symptom reporting and symptom clusters in HF patients between a Western (the United States) and an Eastern Asian sample (China and Taiwan). A secondary analysis of a cross-sectional observational study was conducted. The data were obtained from a matched HF patient sample from the United States and China/Taiwan ( N = 240 in each). Eight selective items related to HF symptoms from the Minnesota Living with Heart Failure Questionnaire were analyzed. Compared with the U.S. sample, HF patients from China/Taiwan reported a lower level of symptom distress. Analysis of two different regional groups did not result in the same number of clusters using latent class approach: the United States (four classes) and China/Taiwan (three classes). The study demonstrated that symptom reporting and identification of symptom clusters might be influenced by cultural factors.

Statistical analysis and handling of missing data in cluster randomized trials: a systematic review.

PubMed

Fiero, Mallorie H; Huang, Shuang; Oren, Eyal; Bell, Melanie L

2016-02-09

Cluster randomized trials (CRTs) randomize participants in groups, rather than as individuals and are key tools used to assess interventions in health research where treatment contamination is likely or if individual randomization is not feasible. Two potential major pitfalls exist regarding CRTs, namely handling missing data and not accounting for clustering in the primary analysis. The aim of this review was to evaluate approaches for handling missing data and statistical analysis with respect to the primary outcome in CRTs. We systematically searched for CRTs published between August 2013 and July 2014 using PubMed, Web of Science, and PsycINFO. For each trial, two independent reviewers assessed the extent of the missing data and method(s) used for handling missing data in the primary and sensitivity analyses. We evaluated the primary analysis and determined whether it was at the cluster or individual level. Of the 86 included CRTs, 80 (93%) trials reported some missing outcome data. Of those reporting missing data, the median percent of individuals with a missing outcome was 19% (range 0.5 to 90%). The most common way to handle missing data in the primary analysis was complete case analysis (44, 55%), whereas 18 (22%) used mixed models, six (8%) used single imputation, four (5%) used unweighted generalized estimating equations, and two (2%) used multiple imputation. Fourteen (16%) trials reported a sensitivity analysis for missing data, but most assumed the same missing data mechanism as in the primary analysis. Overall, 67 (78%) trials accounted for clustering in the primary analysis. High rates of missing outcome data are present in the majority of CRTs, yet handling missing data in practice remains suboptimal. Researchers and applied statisticians should carry out appropriate missing data methods, which are valid under plausible assumptions in order to increase statistical power in trials and reduce the possibility of bias. Sensitivity analysis should be performed, with weakened assumptions regarding the missing data mechanism to explore the robustness of results reported in the primary analysis.

Limited overlap between phylogenetic HIV and hepatitis C virus clusters illustrates the dynamic sexual network structure of Dutch HIV-infected MSM.

PubMed

Vanhommerig, Joost W; Bezemer, Daniela; Molenkamp, Richard; Van Sighem, Ard I; Smit, Colette; Arends, Joop E; Lauw, Fanny N; Brinkman, Kees; Rijnders, Bart J; Newsum, Astrid M; Bruisten, Sylvia M; Prins, Maria; Van Der Meer, Jan T; Van De Laar, Thijs J; Schinkel, Janke

2017-09-24

MSM are at increased risk for infection with HIV-1 and hepatitis C virus (HCV). Is HIV/HCV coinfection confined to specific HIV transmission networks? A HIV phylogenetic tree was constructed for 5038 HIV-1 subtype B polymerase (pol) sequences obtained from MSM in the AIDS therapy evaluation in the Netherlands cohort. We investigated the existence of HIV clusters with increased HCV prevalence, the HIV phylogenetic density (i.e. the number of potential HIV transmission partners) of HIV/HCV-coinfected MSM compared with HIV-infected MSM without HCV, and the overlap in HIV and HCV phylogenies using HCV nonstructural protein 5B sequences from 183 HIV-infected MSM with acute HCV infection. Five hundred and sixty-three of 5038 (11.2%) HIV-infected MSM tested HCV positive. Phylogenetic analysis revealed 93 large HIV clusters (≥10 MSM), 370 small HIV clusters (2-9 MSM), and 867 singletons with a median HCV prevalence of 11.5, 11.6, and 9.3%, respectively. We identified six large HIV clusters with elevated HCV prevalence (range 23.5-46.2%). Median HIV phylogenetic densities for MSM with HCV (3, interquartile range 1-7) and without HCV (3, interquartile range 1-8) were similar. HCV phylogeny showed 12 MSM-specific HCV clusters (clustersize: 2-39 HCV sequences); 12.7% of HCV infections were part of the same HIV and HCV cluster. We observed few HIV clusters with elevated HCV prevalence, no increase in the HIV phylogenetic density of HIV/HCV-coinfected MSM compared to HIV-infected MSM without HCV, and limited overlap between HIV and HCV phylogenies among HIV/HCV-coinfected MSM. Our data do not support the existence of MSM-specific sexual networks that fuel both the HIV and HCV epidemic.

Phenotypes of comorbidity in OSAS patients: combining categorical principal component analysis with cluster analysis.

PubMed

Vavougios, George D; George D, George; Pastaka, Chaido; Zarogiannis, Sotirios G; Gourgoulianis, Konstantinos I

2016-02-01

Phenotyping obstructive sleep apnea syndrome's comorbidity has been attempted for the first time only recently. The aim of our study was to determine phenotypes of comorbidity in obstructive sleep apnea syndrome patients employing a data-driven approach. Data from 1472 consecutive patient records were recovered from our hospital's database. Categorical principal component analysis and two-step clustering were employed to detect distinct clusters in the data. Univariate comparisons between clusters included one-way analysis of variance with Bonferroni correction and chi-square tests. Predictors of pairwise cluster membership were determined via a binary logistic regression model. The analyses revealed six distinct clusters: A, 'healthy, reporting sleeping related symptoms'; B, 'mild obstructive sleep apnea syndrome without significant comorbidities'; C1: 'moderate obstructive sleep apnea syndrome, obesity, without significant comorbidities'; C2: 'moderate obstructive sleep apnea syndrome with severe comorbidity, obesity and the exclusive inclusion of stroke'; D1: 'severe obstructive sleep apnea syndrome and obesity without comorbidity and a 33.8% prevalence of hypertension'; and D2: 'severe obstructive sleep apnea syndrome with severe comorbidities, along with the highest Epworth Sleepiness Scale score and highest body mass index'. Clusters differed significantly in apnea-hypopnea index, oxygen desaturation index; arousal index; age, body mass index, minimum oxygen saturation and daytime oxygen saturation (one-way analysis of variance P < 0.0001). Binary logistic regression indicated that older age, greater body mass index, lower daytime oxygen saturation and hypertension were associated independently with an increased risk of belonging in a comorbid cluster. Six distinct phenotypes of obstructive sleep apnea syndrome and its comorbidities were identified. Mapping the heterogeneity of the obstructive sleep apnea syndrome may help the early identification of at-risk groups. Finally, determining predictors of comorbidity for the moderate and severe strata of these phenotypes implies a need to take these factors into account when considering obstructive sleep apnea syndrome treatment options. © 2015 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.

Classification of frailty using the Kihon checklist: A cluster analysis of older adults in urban areas.

PubMed

Kera, Takeshi; Kawai, Hisashi; Yoshida, Hideyo; Hirano, Hirohiko; Kojima, Motonaga; Fujiwara, Yoshinori; Ihara, Kazushige; Obuchi, Shuichi

2017-01-01

Frailty is an important predictor of the need for long-term care and hospitalization. Our aim was to categorize frailty in community-dwelling older adults. The present study was carried out in 2011-2013, and consisted of 1380 individuals over 65 years of age. Participants completed the Kihon checklist, which is widely used to assess frailty in Japan, and their physical, cognitive and social function was evaluated. Non-hierarchical cluster analysis was used to statistically categorize frailty. The optimum number of clusters was determined as the point at which the external reference values (instrumental activity of daily living score, grip power, 10-m walk time, body mass index, portable fall risk index, occlusal force and Mini-Mental State Examination score) differed. According to the Kihon checklist, 369 (26.7%) of the 1380 study participants were considered frail. When the cluster number was increased from two to six, the scores in each subdomain of the Kihon checklist significantly differed. The estimated minimum number of clusters was five, and each of the five cluster groups had distinct characteristics. The numbers of participants in cluster groups 1-5 were 105, 78, 62, 71 and 53, respectively. We identified five types of frailty in community-dwelling older adults in Japan: "experience of falling," "pre-frailty," "oral frailty," "housebound" and "severe frailty." Geriatr Gerontol Int 2017; 17: 69-77. © 2016 Japan Geriatrics Society.

Optical spectroscopy and velocity dispersions of galaxy clusters from the SPT-SZ survey

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

Ruel, J.; Bayliss, M.; Bazin, G.

2014-09-01

We present optical spectroscopy of galaxies in clusters detected through the Sunyaev-Zel'dovich (SZ) effect with the South Pole Telescope (SPT). We report our own measurements of 61 spectroscopic cluster redshifts, and 48 velocity dispersions each calculated with more than 15 member galaxies. This catalog also includes 19 dispersions of SPT-observed clusters previously reported in the literature. The majority of the clusters in this paper are SPT-discovered; of these, most have been previously reported in other SPT cluster catalogs, and five are reported here as SPT discoveries for the first time. By performing a resampling analysis of galaxy velocities, we findmore » that unbiased velocity dispersions can be obtained from a relatively small number of member galaxies (≲ 30), but with increased systematic scatter. We use this analysis to determine statistical confidence intervals that include the effect of membership selection. We fit scaling relations between the observed cluster velocity dispersions and mass estimates from SZ and X-ray observables. In both cases, the results are consistent with the scaling relation between velocity dispersion and mass expected from dark-matter simulations. We measure a ∼30% log-normal scatter in dispersion at fixed mass, and a ∼10% offset in the normalization of the dispersion-mass relation when compared to the expectation from simulations, which is within the expected level of systematic uncertainty.« less

Towards Tunable Consensus Clustering for Studying Functional Brain Connectivity During Affective Processing.

PubMed

Liu, Chao; Abu-Jamous, Basel; Brattico, Elvira; Nandi, Asoke K

2017-03-01

In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results. To validate the method, we applied it to a complex fMRI experiment involving affective processing of hundreds of music clips. We found that brain structures related to visual, reward, and auditory processing have intrinsic spatial patterns of coherent neuroactivity during affective processing. The comparisons between the results obtained from our method and those from each individual clustering algorithm demonstrate that our paradigm has notable advantages over traditional single clustering algorithms in being able to evidence robust connectivity patterns even with complex neuroimaging data involving a variety of stimuli and affective evaluations of them. The consensus clustering method is implemented in the R package "UNCLES" available on http://cran.r-project.org/web/packages/UNCLES/index.html .

Conformational Clusters of Phosphorylated Tyrosine.

PubMed

Abdelrasoul, Maha; Ponniah, Komala; Mao, Alice; Warden, Meghan S; Elhefnawy, Wessam; Li, Yaohang; Pascal, Steven M

2017-12-06

Tyrosine phosphorylation plays an important role in many cellular and intercellular processes including signal transduction, subcellular localization, and regulation of enzymatic activity. In 1999, Blom et al., using the limited number of protein data bank (PDB) structures available at that time, reported that the side chain structures of phosphorylated tyrosine (pY) are partitioned into two conserved conformational clusters ( Blom, N.; Gammeltoft, S.; Brunak, S. J. Mol. Biol. 1999 , 294 , 1351 - 1362 ). We have used the spectral clustering algorithm to cluster the increasingly growing number of protein structures with pY sites, and have found that the pY residues cluster into three distinct side chain conformations. Two of these pY conformational clusters associate strongly with a narrow range of tyrosine backbone conformation. The novel cluster also highly correlates with the identity of the n + 1 residue, and is strongly associated with a sequential pYpY conformation which places two adjacent pY side chains in a specific relative orientation. Further analysis shows that the three pY clusters are associated with distinct distributions of cognate protein kinases.

Room-temperature isolation of V(benzene)2 sandwich clusters via soft-landing into n-alkanethiol self-assembled monolayers.

PubMed

Nagaoka, Shuhei; Matsumoto, Takeshi; Okada, Eiji; Mitsui, Masaaki; Nakajima, Atsushi

2006-08-17

The adsorption state and thermal stability of V(benzene)2 sandwich clusters soft-landed onto a self-assembled monolayer of different chain-length n-alkanethiols (Cn-SAM, n = 8, 12, 16, 18, and 22) were studied by means of infrared reflection absorption spectroscopy (IRAS) and temperature-programmed desorption (TPD). The IRAS measurement confirmed that V(benzene)2 clusters are molecularly adsorbed and maintain a sandwich structure on all of the SAM substrates. In addition, the clusters supported on the SAM substrates are oriented with their molecular axes tilted 70-80 degrees off the surface normal. An Arrhenius analysis of the TPD spectra reveals that the activation energy for the desorption of the supported clusters increases linearly with the chain length of the SAMs. For the longest chain C22-SAM, the activation energy reaches approximately 150 kJ/mol, and the thermal desorption of the supported clusters can be considerably suppressed near room temperature. The clear chain-length-dependent thermal stability of the supported clusters observed here can be explained well in terms of the cluster penetration into the SAM matrixes.

Hierarchical Star Formation in Turbulent Media: Evidence from Young Star Clusters

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

Grasha, K.; Calzetti, D.; Elmegreen, B. G.

We present an analysis of the positions and ages of young star clusters in eight local galaxies to investigate the connection between the age difference and separation of cluster pairs. We find that star clusters do not form uniformly but instead are distributed so that the age difference increases with the cluster pair separation to the 0.25–0.6 power, and that the maximum size over which star formation is physically correlated ranges from ∼200 pc to ∼1 kpc. The observed trends between age difference and separation suggest that cluster formation is hierarchical both in space and time: clusters that are closemore » to each other are more similar in age than clusters born further apart. The temporal correlations between stellar aggregates have slopes that are consistent with predictions of turbulence acting as the primary driver of star formation. The velocity associated with the maximum size is proportional to the galaxy’s shear, suggesting that the galactic environment influences the maximum size of the star-forming structures.« less

SNARE-mediated rapid lysosome fusion in membrane raft clustering and dysfunction of bovine coronary arterial endothelium

PubMed Central

Han, Wei-Qing; Xia, Min; Zhang, Chun; Zhang, Fan; Xu, Ming; Li, Ning-Jun

2011-01-01

The present study attempted to evaluate whether soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) mediate lysosome fusion in response to death receptor activation and contribute to membrane raft (MR) clustering and consequent endothelial dysfunction in coronary arterial endothelial cells. By immunohistochemical analysis, vesicle-associated membrane proteins 2 (VAMP-2, vesicle-SNAREs) were found to be abundantly expressed in the endothelium of bovine coronary arteries. Direct lysosome fusion monitoring by N-(3-triethylammoniumpropyl)-4-[4-(dibutylamino)styryl]pyridinium dibromide (FM1-43) quenching demonstrated that the inhibition of VAMP-2 with tetanus toxin or specific small interfering ribonucleic acid (siRNA) almost completely blocked lysosome fusion to plasma membrane induced by Fas ligand (FasL), a well-known MR clustering stimulator. The involvement of SNAREs was further confirmed by an increased interaction of VAMP-2 with a target-SNARE protein syntaxin-4 after FasL stimulation in coimmunoprecipitation analysis. Also, the inhibition of VAMP-2 with tetanus toxin or VAMP-2 siRNA abolished FasL-induced MR clustering, its colocalization with a NADPH oxidase unit gp91phox, and increased superoxide production. Finally, FasL-induced impairment of endothelium-dependent vasodilation was reversed by the treatment of bovine coronary arteries with tetanus toxin or VAMP-2 siRNA. VAMP-2 is critical to lysosome fusion in MR clustering, and this VAMP-2-mediated lysosome-MR signalosomes contribute to redox regulation of coronary endothelial function. PMID:21926345

An analysis of pilot error-related aircraft accidents

NASA Technical Reports Server (NTRS)

Kowalsky, N. B.; Masters, R. L.; Stone, R. B.; Babcock, G. L.; Rypka, E. W.

1974-01-01

A multidisciplinary team approach to pilot error-related U.S. air carrier jet aircraft accident investigation records successfully reclaimed hidden human error information not shown in statistical studies. New analytic techniques were developed and applied to the data to discover and identify multiple elements of commonality and shared characteristics within this group of accidents. Three techniques of analysis were used: Critical element analysis, which demonstrated the importance of a subjective qualitative approach to raw accident data and surfaced information heretofore unavailable. Cluster analysis, which was an exploratory research tool that will lead to increased understanding and improved organization of facts, the discovery of new meaning in large data sets, and the generation of explanatory hypotheses. Pattern recognition, by which accidents can be categorized by pattern conformity after critical element identification by cluster analysis.

EXPLORING FUNCTIONAL CONNECTIVITY IN FMRI VIA CLUSTERING.

PubMed

Venkataraman, Archana; Van Dijk, Koene R A; Buckner, Randy L; Golland, Polina

2009-04-01

In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the K-Means and Spectral Clustering algorithms as alternatives to the commonly used Seed-Based Analysis. To enable clustering of the entire brain volume, we use the Nyström Method to approximate the necessary spectral decompositions. We apply K-Means, Spectral Clustering and Seed-Based Analysis to resting-state fMRI data collected from 45 healthy young adults. Without placing any a priori constraints, both clustering methods yield partitions that are associated with brain systems previously identified via Seed-Based Analysis. Our empirical results suggest that clustering provides a valuable tool for functional connectivity analysis.

Analysis of Basis Weight Uniformity of Microfiber Nonwovens and Its Impact on Permeability and Filtration Properties

NASA Astrophysics Data System (ADS)

Amirnasr, Elham

It is widely recognized that nonwoven basis weight non-uniformity affects various properties of nonwovens. However, few studies can be found in this topic. The development of uniformity definition and measurement methods and the study of their impact on various web properties such as filtration properties and air permeability would be beneficial both in industrial applications and in academia. They can be utilized as a quality control tool and would provide insights about nonwoven behaviors that cannot be solely explained by average values. Therefore, for quantifying nonwoven web basis weight uniformity we purse to develop an optical analytical tool. The quadrant method and clustering analysis was utilized in an image analysis scheme to help define "uniformity" and its spatial variation. Implementing the quadrant method in an image analysis system allows the establishment of a uniformity index that can be used to quantify the degree of uniformity. Clustering analysis has also been modified and verified using uniform and random simulated images with known parameters. Number of clusters and cluster properties such as cluster size, member and density was determined. We also utilized this new measurement method to evaluate uniformity of nonwovens produced with different processes and investigated impacts of uniformity on filtration and permeability. The results of quadrant method shows that uniformity index computed from quadrant method demonstrate a good range for non-uniformity of nonwoven webs. Clustering analysis is also been applied on reference nonwoven with known visual uniformity. From clustering analysis results, cluster size is promising to be used as uniformity parameter. It is been shown that non-uniform nonwovens has provide lager cluster size than uniform nonwovens. It was been tried to find a relationship between web properties and uniformity index (as a web characteristic). To achieve this, filtration properties, air permeability, solidity and uniformity index of meltblown and spunbond samples was measured. Results for filtration test show some deviation between theoretical and experimental filtration efficiency by considering different types of fiber diameter. This deviation can occur due to variation in basis weight non-uniformity. So an appropriate theory is required to predict the variation of filtration efficiency with respect to non-uniformity of nonwoven filter media. And the results for air permeability test showed that uniformity index determined by quadrant method and measured properties have some relationship. In the other word, air permeability decreases as uniformity index on nonwoven web increase.

Groundwater Quality: Analysis of Its Temporal and Spatial Variability in a Karst Aquifer.

PubMed

Pacheco Castro, Roger; Pacheco Ávila, Julia; Ye, Ming; Cabrera Sansores, Armando

2018-01-01

This study develops an approach based on hierarchical cluster analysis for investigating the spatial and temporal variation of water quality governing processes. The water quality data used in this study were collected in the karst aquifer of Yucatan, Mexico, the only source of drinking water for a population of nearly two million people. Hierarchical cluster analysis was applied to the quality data of all the sampling periods lumped together. This was motivated by the observation that, if water quality does not vary significantly in time, two samples from the same sampling site will belong to the same cluster. The resulting distribution maps of clusters and box-plots of the major chemical components reveal the spatial and temporal variability of groundwater quality. Principal component analysis was used to verify the results of cluster analysis and to derive the variables that explained most of the variation of the groundwater quality data. Results of this work increase the knowledge about how precipitation and human contamination impact groundwater quality in Yucatan. Spatial variability of groundwater quality in the study area is caused by: a) seawater intrusion and groundwater rich in sulfates at the west and in the coast, b) water rock interactions and the average annual precipitation at the middle and east zones respectively, and c) human contamination present in two localized zones. Changes in the amount and distribution of precipitation cause temporal variation by diluting groundwater in the aquifer. This approach allows to analyze the variation of groundwater quality controlling processes efficiently and simultaneously. © 2017, National Ground Water Association.

Cluster analysis of medical service resources at district hospitals in Taiwan, 2007-2011.

PubMed

Tseng, Shu-Fang; Lee, Tian-Shyug; Deng, Chung-Yeh

2015-12-01

A vast amount of the annual/national budget has been spent on the National Health Insurance program in Taiwan. However, the market for district hospitals has become increasingly competitive, and district hospitals are under pressure to optimize the use of health service resources. Therefore, we employed a clustering method to explore variations in input and output service volumes, and investigate resource allocation and health care service efficiency in district hospitals. Descriptive and cluster analyses were conducted to examine the district hospitals included in the Ministry of Health and Welfare database during 2007-2011. The results, according to the types of hospital ownership, suggested that the number of public hospitals has decreased and that of private hospitals increased; the largest increase in the number of district hospitals occurred when Taichung City was merged into Taichung County. The descriptive statistics from 2007 to 2011 indicated that 43% and 36.4% of the hospitals had 501-800 occupied beds and 101-200 physicians, respectively, and > 401 medical staff members. However, the number of outpatients and discharged patients exceeded 6001 and 90,001, respectively. In addition, the highest percentage of hospitals (43.9%) had 30,001-60,000 emergency department patients. In 2010, the number of patients varied widely, and the analysis of variance cluster results were nonsignificant (p > 0.05). District hospitals belonging to low-throughput and low-performance groups were encouraged to improve resource utilization for enhancing health care service efficiency. Copyright © 2015. Published by Elsevier Taiwan.

Induced liquid-crystalline ordering in solutions of stiff and flexible amphiphilic macromolecules: Effect of mixture composition

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

Glagolev, Mikhail K.; Vasilevskaya, Valentina V., E-mail: vvvas@polly.phys.msu.ru; Khokhlov, Alexei R.

Impact of mixture composition on self-organization in concentrated solutions of stiff helical and flexible macromolecules was studied by means of molecular dynamics simulation. The macromolecules were composed of identical amphiphilic monomer units but a fraction f of macromolecules had stiff helical backbones and the remaining chains were flexible. In poor solvents the compacted flexible macromolecules coexist with bundles or filament clusters from few intertwined stiff helical macromolecules. The increase of relative content f of helical macromolecules leads to increase of the length of helical clusters, to alignment of clusters with each other, and then to liquid-crystalline-like ordering along a singlemore » direction. The formation of filament clusters causes segregation of helical and flexible macromolecules and the alignment of the filaments induces effective liquid-like ordering of flexible macromolecules. A visual analysis and calculation of order parameter relaying the anisotropy of diffraction allow concluding that transition from disordered to liquid-crystalline state proceeds sharply at relatively low content of stiff components.« less

Faster sequence homology searches by clustering subsequences.

PubMed

Suzuki, Shuji; Kakuta, Masanori; Ishida, Takashi; Akiyama, Yutaka

2015-04-15

Sequence homology searches are used in various fields. New sequencing technologies produce huge amounts of sequence data, which continuously increase the size of sequence databases. As a result, homology searches require large amounts of computational time, especially for metagenomic analysis. We developed a fast homology search method based on database subsequence clustering, and implemented it as GHOSTZ. This method clusters similar subsequences from a database to perform an efficient seed search and ungapped extension by reducing alignment candidates based on triangle inequality. The database subsequence clustering technique achieved an ∼2-fold increase in speed without a large decrease in search sensitivity. When we measured with metagenomic data, GHOSTZ is ∼2.2-2.8 times faster than RAPSearch and is ∼185-261 times faster than BLASTX. The source code is freely available for download at http://www.bi.cs.titech.ac.jp/ghostz/ akiyama@cs.titech.ac.jp Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

ClusterViz: A Cytoscape APP for Cluster Analysis of Biological Network.

PubMed

Wang, Jianxin; Zhong, Jiancheng; Chen, Gang; Li, Min; Wu, Fang-xiang; Pan, Yi

2015-01-01

Cluster analysis of biological networks is one of the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization of clustering results is crucial to uncover the structure of biological networks. In this paper, ClusterViz, an APP of Cytoscape 3 for cluster analysis and visualization, has been developed. In order to reduce complexity and enable extendibility for ClusterViz, we designed the architecture of ClusterViz based on the framework of Open Services Gateway Initiative. According to the architecture, the implementation of ClusterViz is partitioned into three modules including interface of ClusterViz, clustering algorithms and visualization and export. ClusterViz fascinates the comparison of the results of different algorithms to do further related analysis. Three commonly used clustering algorithms, FAG-EC, EAGLE and MCODE, are included in the current version. Due to adopting the abstract interface of algorithms in module of the clustering algorithms, more clustering algorithms can be included for the future use. To illustrate usability of ClusterViz, we provided three examples with detailed steps from the important scientific articles, which show that our tool has helped several research teams do their research work on the mechanism of the biological networks.

Applications of modern statistical methods to analysis of data in physical science

NASA Astrophysics Data System (ADS)

Wicker, James Eric

Modern methods of statistical and computational analysis offer solutions to dilemmas confronting researchers in physical science. Although the ideas behind modern statistical and computational analysis methods were originally introduced in the 1970's, most scientists still rely on methods written during the early era of computing. These researchers, who analyze increasingly voluminous and multivariate data sets, need modern analysis methods to extract the best results from their studies. The first section of this work showcases applications of modern linear regression. Since the 1960's, many researchers in spectroscopy have used classical stepwise regression techniques to derive molecular constants. However, problems with thresholds of entry and exit for model variables plagues this analysis method. Other criticisms of this kind of stepwise procedure include its inefficient searching method, the order in which variables enter or leave the model and problems with overfitting data. We implement an information scoring technique that overcomes the assumptions inherent in the stepwise regression process to calculate molecular model parameters. We believe that this kind of information based model evaluation can be applied to more general analysis situations in physical science. The second section proposes new methods of multivariate cluster analysis. The K-means algorithm and the EM algorithm, introduced in the 1960's and 1970's respectively, formed the basis of multivariate cluster analysis methodology for many years. However, several shortcomings of these methods include strong dependence on initial seed values and inaccurate results when the data seriously depart from hypersphericity. We propose new cluster analysis methods based on genetic algorithms that overcomes the strong dependence on initial seed values. In addition, we propose a generalization of the Genetic K-means algorithm which can accurately identify clusters with complex hyperellipsoidal covariance structures. We then use this new algorithm in a genetic algorithm based Expectation-Maximization process that can accurately calculate parameters describing complex clusters in a mixture model routine. Using the accuracy of this GEM algorithm, we assign information scores to cluster calculations in order to best identify the number of mixture components in a multivariate data set. We will showcase how these algorithms can be used to process multivariate data from astronomical observations.

Interactive Parallel Data Analysis within Data-Centric Cluster Facilities using the IPython Notebook

NASA Astrophysics Data System (ADS)

Pascoe, S.; Lansdowne, J.; Iwi, A.; Stephens, A.; Kershaw, P.

2012-12-01

The data deluge is making traditional analysis workflows for many researchers obsolete. Support for parallelism within popular tools such as matlab, IDL and NCO is not well developed and rarely used. However parallelism is necessary for processing modern data volumes on a timescale conducive to curiosity-driven analysis. Furthermore, for peta-scale datasets such as the CMIP5 archive, it is no longer practical to bring an entire dataset to a researcher's workstation for analysis, or even to their institutional cluster. Therefore, there is an increasing need to develop new analysis platforms which both enable processing at the point of data storage and which provides parallelism. Such an environment should, where possible, maintain the convenience and familiarity of our current analysis environments to encourage curiosity-driven research. We describe how we are combining the interactive python shell (IPython) with our JASMIN data-cluster infrastructure. IPython has been specifically designed to bridge the gap between the HPC-style parallel workflows and the opportunistic curiosity-driven analysis usually carried out using domain specific languages and scriptable tools. IPython offers a web-based interactive environment, the IPython notebook, and a cluster engine for parallelism all underpinned by the well-respected Python/Scipy scientific programming stack. JASMIN is designed to support the data analysis requirements of the UK and European climate and earth system modeling community. JASMIN, with its sister facility CEMS focusing the earth observation community, has 4.5 PB of fast parallel disk storage alongside over 370 computing cores provide local computation. Through the IPython interface to JASMIN, users can make efficient use of JASMIN's multi-core virtual machines to perform interactive analysis on all cores simultaneously or can configure IPython clusters across multiple VMs. Larger-scale clusters can be provisioned through JASMIN's batch scheduling system. Outputs can be summarised and visualised using the full power of Python's many scientific tools, including Scipy, Matplotlib, Pandas and CDAT. This rich user experience is delivered through the user's web browser; maintaining the interactive feel of a workstation-based environment with the parallel power of a remote data-centric processing facility.

Identification of Reliable Components in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS): a Data-Driven Approach across Metabolic Processes.

PubMed

Motegi, Hiromi; Tsuboi, Yuuri; Saga, Ayako; Kagami, Tomoko; Inoue, Maki; Toki, Hideaki; Minowa, Osamu; Noda, Tetsuo; Kikuchi, Jun

2015-11-04

There is an increasing need to use multivariate statistical methods for understanding biological functions, identifying the mechanisms of diseases, and exploring biomarkers. In addition to classical analyses such as hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis, various multivariate strategies, including independent component analysis, non-negative matrix factorization, and multivariate curve resolution, have recently been proposed. However, determining the number of components is problematic. Despite the proposal of several different methods, no satisfactory approach has yet been reported. To resolve this problem, we implemented a new idea: classifying a component as "reliable" or "unreliable" based on the reproducibility of its appearance, regardless of the number of components in the calculation. Using the clustering method for classification, we applied this idea to multivariate curve resolution-alternating least squares (MCR-ALS). Comparisons between conventional and modified methods applied to proton nuclear magnetic resonance ((1)H-NMR) spectral datasets derived from known standard mixtures and biological mixtures (urine and feces of mice) revealed that more plausible results are obtained by the modified method. In particular, clusters containing little information were detected with reliability. This strategy, named "cluster-aided MCR-ALS," will facilitate the attainment of more reliable results in the metabolomics datasets.

Mass Profile Decomposition of the Frontier Fields Cluster MACS J0416-2403: Insights on the Dark-matter Inner Profile

NASA Astrophysics Data System (ADS)

Annunziatella, M.; Bonamigo, M.; Grillo, C.; Mercurio, A.; Rosati, P.; Caminha, G.; Biviano, A.; Girardi, M.; Gobat, R.; Lombardi, M.; Munari, E.

2017-12-01

We present a high-resolution dissection of the two-dimensional total mass distribution in the core of the Hubble Frontier Fields galaxy cluster MACS J0416.1‑2403, at z = 0.396. We exploit HST/WFC3 near-IR (F160W) imaging, VLT/Multi Unit Spectroscopic Explorer spectroscopy, and Chandra data to separate the stellar, hot gas, and dark-matter mass components in the inner 300 kpc of the cluster. We combine the recent results of our refined strong lensing analysis, which includes the contribution of the intracluster gas, with the modeling of the surface brightness and stellar mass distributions of 193 cluster members, of which 144 are spectroscopically confirmed. We find that, moving from 10 to 300 kpc from the cluster center, the stellar to total mass fraction decreases from 12% to 1% and the hot gas to total mass fraction increases from 3% to 9%, resulting in a baryon fraction of approximatively 10% at the outermost radius. We measure that the stellar component represents ∼30%, near the cluster center, and 15%, at larger clustercentric distances, of the total mass in the cluster substructures. We subtract the baryonic mass component from the total mass distribution and conclude that within 30 kpc (∼3 times the effective radius of the brightest cluster galaxy) from the cluster center the surface mass density profile of the total mass and global (cluster plus substructures) dark-matter are steeper and that of the diffuse (cluster) dark-matter is shallower than an NFW profile. Our current analysis does not point to a significant offset between the cluster stellar and dark-matter components. This detailed and robust reconstruction of the inner dark-matter distribution in a larger sample of galaxy clusters will set a new benchmark for different structure formation scenarios.

Improving clustering with metabolic pathway data.

PubMed

Milone, Diego H; Stegmayer, Georgina; López, Mariana; Kamenetzky, Laura; Carrari, Fernando

2014-04-10

It is a common practice in bioinformatics to validate each group returned by a clustering algorithm through manual analysis, according to a-priori biological knowledge. This procedure helps finding functionally related patterns to propose hypotheses for their behavior and the biological processes involved. Therefore, this knowledge is used only as a second step, after data are just clustered according to their expression patterns. Thus, it could be very useful to be able to improve the clustering of biological data by incorporating prior knowledge into the cluster formation itself, in order to enhance the biological value of the clusters. A novel training algorithm for clustering is presented, which evaluates the biological internal connections of the data points while the clusters are being formed. Within this training algorithm, the calculation of distances among data points and neurons centroids includes a new term based on information from well-known metabolic pathways. The standard self-organizing map (SOM) training versus the biologically-inspired SOM (bSOM) training were tested with two real data sets of transcripts and metabolites from Solanum lycopersicum and Arabidopsis thaliana species. Classical data mining validation measures were used to evaluate the clustering solutions obtained by both algorithms. Moreover, a new measure that takes into account the biological connectivity of the clusters was applied. The results of bSOM show important improvements in the convergence and performance for the proposed clustering method in comparison to standard SOM training, in particular, from the application point of view. Analyses of the clusters obtained with bSOM indicate that including biological information during training can certainly increase the biological value of the clusters found with the proposed method. It is worth to highlight that this fact has effectively improved the results, which can simplify their further analysis.The algorithm is available as a web-demo at http://fich.unl.edu.ar/sinc/web-demo/bsom-lite/. The source code and the data sets supporting the results of this article are available at http://sourceforge.net/projects/sourcesinc/files/bsom.

[Bibliometrics and visualization analysis of land use regression models in ambient air pollution research].

PubMed

Zhang, Y J; Zhou, D H; Bai, Z P; Xue, F X

2018-02-10

Objective: To quantitatively analyze the current status and development trends regarding the land use regression (LUR) models on ambient air pollution studies. Methods: Relevant literature from the PubMed database before June 30, 2017 was analyzed, using the Bibliographic Items Co-occurrence Matrix Builder (BICOMB 2.0). Keywords co-occurrence networks, cluster mapping and timeline mapping were generated, using the CiteSpace 5.1.R5 software. Relevant literature identified in three Chinese databases was also reviewed. Results: Four hundred sixty four relevant papers were retrieved from the PubMed database. The number of papers published showed an annual increase, in line with the growing trend of the index. Most papers were published in the journal of Environmental Health Perspectives . Results from the Co-word cluster analysis identified five clusters: cluster#0 consisted of birth cohort studies related to the health effects of prenatal exposure to air pollution; cluster#1 referred to land use regression modeling and exposure assessment; cluster#2 was related to the epidemiology on traffic exposure; cluster#3 dealt with the exposure to ultrafine particles and related health effects; cluster#4 described the exposure to black carbon and related health effects. Data from Timeline mapping indicated that cluster#0 and#1 were the main research areas while cluster#3 and#4 were the up-coming hot areas of research. Ninety four relevant papers were retrieved from the Chinese databases with most of them related to studies on modeling. Conclusion: In order to better assess the health-related risks of ambient air pollution, and to best inform preventative public health intervention policies, application of LUR models to environmental epidemiology studies in China should be encouraged.

Gene expression profiles of breast biopsies from healthy women identify a group with claudin-low features.

PubMed

Haakensen, Vilde D; Lingjaerde, Ole Christian; Lüders, Torben; Riis, Margit; Prat, Aleix; Troester, Melissa A; Holmen, Marit M; Frantzen, Jan Ole; Romundstad, Linda; Navjord, Dina; Bukholm, Ida K; Johannesen, Tom B; Perou, Charles M; Ursin, Giske; Kristensen, Vessela N; Børresen-Dale, Anne-Lise; Helland, Aslaug

2011-11-01

Increased understanding of the variability in normal breast biology will enable us to identify mechanisms of breast cancer initiation and the origin of different subtypes, and to better predict breast cancer risk. Gene expression patterns in breast biopsies from 79 healthy women referred to breast diagnostic centers in Norway were explored by unsupervised hierarchical clustering and supervised analyses, such as gene set enrichment analysis and gene ontology analysis and comparison with previously published genelists and independent datasets. Unsupervised hierarchical clustering identified two separate clusters of normal breast tissue based on gene-expression profiling, regardless of clustering algorithm and gene filtering used. Comparison of the expression profile of the two clusters with several published gene lists describing breast cells revealed that the samples in cluster 1 share characteristics with stromal cells and stem cells, and to a certain degree with mesenchymal cells and myoepithelial cells. The samples in cluster 1 also share many features with the newly identified claudin-low breast cancer intrinsic subtype, which also shows characteristics of stromal and stem cells. More women belonging to cluster 1 have a family history of breast cancer and there is a slight overrepresentation of nulliparous women in cluster 1. Similar findings were seen in a separate dataset consisting of histologically normal tissue from both breasts harboring breast cancer and from mammoplasty reductions. This is the first study to explore the variability of gene expression patterns in whole biopsies from normal breasts and identified distinct subtypes of normal breast tissue. Further studies are needed to determine the specific cell contribution to the variation in the biology of normal breasts, how the clusters identified relate to breast cancer risk and their possible link to the origin of the different molecular subtypes of breast cancer.

antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences.

PubMed

Medema, Marnix H; Blin, Kai; Cimermancic, Peter; de Jager, Victor; Zakrzewski, Piotr; Fischbach, Michael A; Weber, Tilmann; Takano, Eriko; Breitling, Rainer

2011-07-01

Bacterial and fungal secondary metabolism is a rich source of novel bioactive compounds with potential pharmaceutical applications as antibiotics, anti-tumor drugs or cholesterol-lowering drugs. To find new drug candidates, microbiologists are increasingly relying on sequencing genomes of a wide variety of microbes. However, rapidly and reliably pinpointing all the potential gene clusters for secondary metabolites in dozens of newly sequenced genomes has been extremely challenging, due to their biochemical heterogeneity, the presence of unknown enzymes and the dispersed nature of the necessary specialized bioinformatics tools and resources. Here, we present antiSMASH (antibiotics & Secondary Metabolite Analysis Shell), the first comprehensive pipeline capable of identifying biosynthetic loci covering the whole range of known secondary metabolite compound classes (polyketides, non-ribosomal peptides, terpenes, aminoglycosides, aminocoumarins, indolocarbazoles, lantibiotics, bacteriocins, nucleosides, beta-lactams, butyrolactones, siderophores, melanins and others). It aligns the identified regions at the gene cluster level to their nearest relatives from a database containing all other known gene clusters, and integrates or cross-links all previously available secondary-metabolite specific gene analysis methods in one interactive view. antiSMASH is available at http://antismash.secondarymetabolites.org.

Delineation of Stenotrophomonas maltophilia isolates from cystic fibrosis patients by fatty acid methyl ester profiles and matrix-assisted laser desorption/ionization time-of-flight mass spectra using hierarchical cluster analysis and principal component analysis.

PubMed

Vidigal, Pedrina Gonçalves; Mosel, Frank; Koehling, Hedda Luise; Mueller, Karl Dieter; Buer, Jan; Rath, Peter Michael; Steinmann, Joerg

2014-12-01

Stenotrophomonas maltophilia is an opportunist multidrug-resistant pathogen that causes a wide range of nosocomial infections. Various cystic fibrosis (CF) centres have reported an increasing prevalence of S. maltophilia colonization/infection among patients with this disease. The purpose of this study was to assess specific fingerprints of S. maltophilia isolates from CF patients (n = 71) by investigating fatty acid methyl esters (FAMEs) through gas chromatography (GC) and highly abundant proteins by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), and to compare them with isolates obtained from intensive care unit (ICU) patients (n = 20) and the environment (n = 11). Principal component analysis (PCA) of GC-FAME patterns did not reveal a clustering corresponding to distinct CF, ICU or environmental types. Based on the peak area index, it was observed that S. maltophilia isolates from CF patients produced significantly higher amounts of fatty acids in comparison with ICU patients and the environmental isolates. Hierarchical cluster analysis (HCA) based on the MALDI-TOF MS peak profiles of S. maltophilia revealed the presence of five large clusters, suggesting a high phenotypic diversity. Although HCA of MALDI-TOF mass spectra did not result in distinct clusters predominantly composed of CF isolates, PCA revealed the presence of a distinct cluster composed of S. maltophilia isolates from CF patients. Our data suggest that S. maltophilia colonizing CF patients tend to modify not only their fatty acid patterns but also their protein patterns as a response to adaptation in the unfavourable environment of the CF lung. © 2014 The Authors.

The Quantitative Analysis of Chennai Automotive Industry Cluster

NASA Astrophysics Data System (ADS)

Bhaskaran, Ethirajan

2016-07-01

Chennai, also called as Detroit of India due to presence of Automotive Industry producing over 40 % of the India's vehicle and components. During 2001-2002, the Automotive Component Industries (ACI) in Ambattur, Thirumalizai and Thirumudivakkam Industrial Estate, Chennai has faced problems on infrastructure, technology, procurement, production and marketing. The objective is to study the Quantitative Performance of Chennai Automotive Industry Cluster before (2001-2002) and after the CDA (2008-2009). The methodology adopted is collection of primary data from 100 ACI using quantitative questionnaire and analyzing using Correlation Analysis (CA), Regression Analysis (RA), Friedman Test (FMT), and Kruskall Wallis Test (KWT).The CA computed for the different set of variables reveals that there is high degree of relationship between the variables studied. The RA models constructed establish the strong relationship between the dependent variable and a host of independent variables. The models proposed here reveal the approximate relationship in a closer form. KWT proves, there is no significant difference between three locations clusters with respect to: Net Profit, Production Cost, Marketing Costs, Procurement Costs and Gross Output. This supports that each location has contributed for development of automobile component cluster uniformly. The FMT proves, there is no significant difference between industrial units in respect of cost like Production, Infrastructure, Technology, Marketing and Net Profit. To conclude, the Automotive Industries have fully utilized the Physical Infrastructure and Centralised Facilities by adopting CDA and now exporting their products to North America, South America, Europe, Australia, Africa and Asia. The value chain analysis models have been implemented in all the cluster units. This Cluster Development Approach (CDA) model can be implemented in industries of under developed and developing countries for cost reduction and productivity increase.

Insights into magmatic processes and hydrothermal alteration of in situ superfast spreading ocean crust at ODP/IODP site 1256 from a cluster analysis of rock magnetic properties

NASA Astrophysics Data System (ADS)

Dekkers, Mark J.; Heslop, David; Herrero-Bervera, Emilio; Acton, Gary; Krasa, David

2014-08-01

We analyze magnetic properties from Ocean Drilling Program (ODP)/Integrated ODP (IODP) Hole 1256D (6°44.1' N, 91°56.1' W) on the Cocos Plate in ˜15.2 Ma oceanic crust generated by superfast seafloor spreading, the only drill hole that has sampled all three oceanic crust layers in a tectonically undisturbed setting. Fuzzy c-means cluster analysis and nonlinear mapping are utilized to study down-hole trends in the ratio of the saturation remanent magnetization and the saturation magnetization, the coercive force, the ratio of the remanent coercive force and coercive force, the low-field magnetic susceptibility, and the Curie temperature, to evaluate the effects of magmatic and hydrothermal processes on magnetic properties. A statistically robust five cluster solution separates the data predominantly into three clusters that express increasing hydrothermal alteration of the lavas, which differ from two distinct clusters mainly representing the dikes and gabbros. Extensive alteration can obliterate magnetic property differences between lavas, dikes, and gabbros. The imprint of thermochemical alteration on the iron-titanium oxides is only partially related to the porosity of the rocks. Thus, the analysis complements interpretation based on electrofacies analysis. All clusters display rock magnetic characteristics compatible with an ability to retain a stable natural remanent magnetization suggesting that the entire sampled sequence of ocean crust can contribute to marine magnetic anomalies. Paleointensity determination is difficult because of the propensity of oxyexsolution during laboratory heating and/or the presence of intergrowths. The upper part of the extrusive sequence, the granoblastic dikes, and moderately altered gabbros may contain a comparatively uncontaminated thermoremanent magnetization.

Carbon Fibers Conductivity Studies

NASA Technical Reports Server (NTRS)

Yang, C. Y.; Butkus, A. M.

1980-01-01

In an attempt to understand the process of electrical conduction in polyacrylonitrile (PAN)-based carbon fibers, calculations were carried out on cluster models of the fiber consisting of carbon, nitrogen, and hydrogen atoms using the modified intermediate neglect of differential overlap (MINDO) molecular orbital (MO) method. The models were developed based on the assumption that PAN carbon fibers obtained with heat treatment temperatures (HTT) below 1000 C retain nitrogen in a graphite-like lattice. For clusters modeling an edge nitrogen site, analysis of the occupied MO's indicated an electron distribution similar to that of graphite. A similar analysis for the somewhat less stable interior nitrogen site revealed a partially localized II electron distribution around the nitrogen atom. The differences in bonding trends and structural stability between edge and interior nitrogen clusters led to a two-step process proposed for nitrogen evolution with increasing HTT.

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.

Clustering Analysis of Antibiograms and Antibiogram Types of Streptococcus agalactiae Strains from Tilapia in China.

PubMed

Liu, Chan; Feng, Juan; Zhang, Defeng; Xie, Yundan; Li, Anxing; Wang, Jiangyong; Su, Youlu

2018-05-11

In view of the changing antibiotic-resistance profiles of Streptococcus agalactiae from tilapia in China, antimicrobial susceptibilities of 75 S. agalactiae strains were determined by the disc diffusion method, and cluster analyses of the antibiograms and antibiogram types were performed. All strains displayed multidrug resistance (MDR). The antimicrobial-resistance rates were highest (>90%) to aminoglycosides, sulfonamides, pipemidic acid, and norfloxacin, followed by penicillin, ampicillin, and ciprofloxacin (26.7-38.7%); those to furadantin, lincomycin, erythromycin, ofloxacin, tetracycline, and florfenicol were low (<10%), and no resistance to vancomycin, cefalexin, cefoxitin, amoxicillin, medemycin, doxitard, oxytetracycline, rifampin, chloramphenicol, or thiamphenicol was detected. Statistical analysis showed that the resistance rate to ciprofloxacin increased significantly in 2016 (p = 0.009), whereas that to trimethoprim/sulfamethoxazole decreased (p = 0.017). Cluster analyses identified that the strains had 23 antibiogram types (A-W) and clustered in five groups (Groups I-V). The strains with higher antimicrobial resistance mainly clustered in Groups I and II. Our results show that the antibiograms varied with time and by location and that antibiogram types are constantly updating and expanding. Effective measures must be taken to reduce the antimicrobial resistance and spread of MDR strains.

Collaborative filtering recommendation model based on fuzzy clustering algorithm

NASA Astrophysics Data System (ADS)

Yang, Ye; Zhang, Yunhua

2018-05-01

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

Evaluation of data quality, timeliness and acceptability of the tuberculosis surveillance system in Brazil's micro-regions.

PubMed

Silva, Gabriela Drummond Marques da; Bartholomay, Patrícia; Cruz, Oswaldo Gonçalves; Garcia, Leila Posenato

2017-10-01

This study aimed to evaluate quality, acceptability and timeliness of the data in the tuberculosis surveillance system in Brazilian micro-regions. An ecological cross-sectional study was carried out, after a qualitative stage for selecting indicators. All 558 Brazilian micro-regions were used as units of analysis. Data available in the National Notifiable Diseases Information System (SINAN), from 2012 to 2014, were used to calculate 14 indicators relating to four attributes: completeness, consistency, timeliness and acceptability. The study made use of cluster analysis to group micro-regions according to acceptability and timeliness. Three clusters were identified among the 473 micro-regions with optimal or regular completeness (70% to 100%) and with over five notifications. Cluster 1 (n = 109) presented mean timeliness of notification and treatment equal to 62.8% and 24.9%, respectively. Cluster 2 (n = 143) had a mean percentage of cases tested for HIV equal to 55.9%. Cluster 3 (n = 221) had the best performing tuberculosis indicators. Results suggest priority areas for improving surveillance of tuberculosis, predominantly in the central-north part of the country. They also point to the need to increase the timeliness of treatment and the percentage of cases tested for HIV.

Intercenter Differences in Bronchopulmonary Dysplasia or Death Among Very Low Birth Weight Infants

PubMed Central

Walsh, Michele; Bobashev, Georgiy; Das, Abhik; Levine, Burton; Carlo, Waldemar A.; Higgins, Rosemary D.

2011-01-01

OBJECTIVES: To determine (1) the magnitude of clustering of bronchopulmonary dysplasia (36 weeks) or death (the outcome) across centers of the Eunice Kennedy Shriver National Institute of Child and Human Development National Research Network, (2) the infant-level variables associated with the outcome and estimate their clustering, and (3) the center-specific practices associated with the differences and build predictive models. METHODS: Data on neonates with a birth weight of <1250 g from the cluster-randomized benchmarking trial were used to determine the magnitude of clustering of the outcome according to alternating logistic regression by using pairwise odds ratio and predictive modeling. Clinical variables associated with the outcome were identified by using multivariate analysis. The magnitude of clustering was then evaluated after correction for infant-level variables. Predictive models were developed by using center-specific and infant-level variables for data from 2001 2004 and projected to 2006. RESULTS: In 2001–2004, clustering of bronchopulmonary dysplasia/death was significant (pairwise odds ratio: 1.3; P < .001) and increased in 2006 (pairwise odds ratio: 1.6; overall incidence: 52%; range across centers: 32%–74%); center rates were relatively stable over time. Variables that varied according to center and were associated with increased risk of outcome included lower body temperature at NICU admission, use of prophylactic indomethacin, specific drug therapy on day 1, and lack of endotracheal intubation. Center differences remained significant even after correction for clustered variables. CONCLUSION: Bronchopulmonary dysplasia/death rates demonstrated moderate clustering according to center. Clinical variables associated with the outcome were also clustered. Center differences after correction of clustered variables indicate presence of as-yet unmeasured center variables. PMID:21149431

Whole Genome Sequence and Phylogenetic Analysis Show Helicobacter pylori Strains from Latin America Have Followed a Unique Evolution Pathway

PubMed Central

Muñoz-Ramírez, Zilia Y.; Mendez-Tenorio, Alfonso; Kato, Ikuko; Bravo, Maria M.; Rizzato, Cosmeri; Thorell, Kaisa; Torres, Roberto; Aviles-Jimenez, Francisco; Camorlinga, Margarita; Canzian, Federico; Torres, Javier

2017-01-01

Helicobacter pylori (HP) genetics may determine its clinical outcomes. Despite high prevalence of HP infection in Latin America (LA), there have been no phylogenetic studies in the region. We aimed to understand the structure of HP populations in LA mestizo individuals, where gastric cancer incidence remains high. The genome of 107 HP strains from Mexico, Nicaragua and Colombia were analyzed with 59 publicly available worldwide genomes. To study bacterial relationship on whole genome level we propose a virtual hybridization technique using thousands of high-entropy 13 bp DNA probes to generate fingerprints. Phylogenetic virtual genome fingerprint (VGF) was compared with Multi Locus Sequence Analysis (MLST) and with phylogenetic analyses of cagPAI virulence island sequences. With MLST some Nicaraguan and Mexican strains clustered close to Africa isolates, whereas European isolates were spread without clustering and intermingled with LA isolates. VGF analysis resulted in increased resolution of populations, separating European from LA strains. Furthermore, clusters with exclusively Colombian, Mexican, or Nicaraguan strains were observed, where the Colombian cluster separated from Europe, Asia, and Africa, while Nicaraguan and Mexican clades grouped close to Africa. In addition, a mixed large LA cluster including Mexican, Colombian, Nicaraguan, Peruvian, and Salvadorian strains was observed; all LA clusters separated from the Amerind clade. With cagPAI sequence analyses LA clades clearly separated from Europe, Asia and Amerind, and Colombian strains formed a single cluster. A NeighborNet analyses suggested frequent and recent recombination events particularly among LA strains. Results suggests that in the new world, H. pylori has evolved to fit mestizo LA populations, already 500 years after the Spanish colonization. This co-adaption may account for regional variability in gastric cancer risk. PMID:28293542

Social Media Use and Depression and Anxiety Symptoms: A Cluster Analysis.

PubMed

Shensa, Ariel; Sidani, Jaime E; Dew, Mary Amanda; Escobar-Viera, César G; Primack, Brian A

2018-03-01

Individuals use social media with varying quantity, emotional, and behavioral at- tachment that may have differential associations with mental health outcomes. In this study, we sought to identify distinct patterns of social media use (SMU) and to assess associations between those patterns and depression and anxiety symptoms. In October 2014, a nationally-representative sample of 1730 US adults ages 19 to 32 completed an online survey. Cluster analysis was used to identify patterns of SMU. Depression and anxiety were measured using respective 4-item Patient-Reported Outcome Measurement Information System (PROMIS) scales. Multivariable logistic regression models were used to assess associations between clus- ter membership and depression and anxiety. Cluster analysis yielded a 5-cluster solu- tion. Participants were characterized as "Wired," "Connected," "Diffuse Dabblers," "Concentrated Dabblers," and "Unplugged." Membership in 2 clusters - "Wired" and "Connected" - increased the odds of elevated depression and anxiety symptoms (AOR = 2.7, 95% CI = 1.5-4.7; AOR = 3.7, 95% CI = 2.1-6.5, respectively, and AOR = 2.0, 95% CI = 1.3-3.2; AOR = 2.0, 95% CI = 1.3-3.1, respectively). SMU pattern characterization of a large population suggests 2 pat- terns are associated with risk for depression and anxiety. Developing educational interventions that address use patterns rather than single aspects of SMU (eg, quantity) would likely be useful.

ICAP - An Interactive Cluster Analysis Procedure for analyzing remotely sensed data

NASA Technical Reports Server (NTRS)

Wharton, S. W.; Turner, B. J.

1981-01-01

An Interactive Cluster Analysis Procedure (ICAP) was developed to derive classifier training statistics from remotely sensed data. ICAP differs from conventional clustering algorithms by allowing the analyst to optimize the cluster configuration by inspection, rather than by manipulating process parameters. Control of the clustering process alternates between the algorithm, which creates new centroids and forms clusters, and the analyst, who can evaluate and elect to modify the cluster structure. Clusters can be deleted, or lumped together pairwise, or new centroids can be added. A summary of the cluster statistics can be requested to facilitate cluster manipulation. The principal advantage of this approach is that it allows prior information (when available) to be used directly in the analysis, since the analyst interacts with ICAP in a straightforward manner, using basic terms with which he is more likely to be familiar. Results from testing ICAP showed that an informed use of ICAP can improve classification, as compared to an existing cluster analysis procedure.

Missing continuous outcomes under covariate dependent missingness in cluster randomised trials

PubMed Central

Diaz-Ordaz, Karla; Bartlett, Jonathan W

2016-01-01

Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline covariate effect on outcome either the same or different between intervention groups. We show that both unadjusted cluster-level analysis and baseline covariate adjusted cluster-level analysis give unbiased estimates of the intervention effect only if both intervention groups have the same missingness mechanisms and there is no interaction between baseline covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group. PMID:27177885

Missing continuous outcomes under covariate dependent missingness in cluster randomised trials.

PubMed

Hossain, Anower; Diaz-Ordaz, Karla; Bartlett, Jonathan W

2017-06-01

Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline covariate effect on outcome either the same or different between intervention groups. We show that both unadjusted cluster-level analysis and baseline covariate adjusted cluster-level analysis give unbiased estimates of the intervention effect only if both intervention groups have the same missingness mechanisms and there is no interaction between baseline covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group.

SCUD: fast structure clustering of decoys using reference state to remove overall rotation.

PubMed

Li, Hongzhi; Zhou, Yaoqi

2005-08-01

We developed a method for fast decoy clustering by using reference root-mean-squared distance (rRMSD) rather than commonly used pairwise RMSD (pRMSD) values. For 41 proteins with 2000 decoys each, the computing efficiency increases nine times without a significant change in the accuracy of near-native selections. Tests on additional protein decoys based on different reference conformations confirmed this result. Further analysis indicates that the pRMSD and rRMSD values are highly correlated (with an average correlation coefficient of 0.82) and the clusters obtained from pRMSD and rRMSD values are highly similar (the representative structures of the top five largest clusters from the two methods are 74% identical). SCUD (Structure ClUstering of Decoys) with an automatic cutoff value is available at http://theory.med.buffalo.edu. (c) 2005 Wiley Periodicals, Inc.

High-Performance Data Analysis Tools for Sun-Earth Connection Missions

NASA Technical Reports Server (NTRS)

Messmer, Peter

2011-01-01

The data analysis tool of choice for many Sun-Earth Connection missions is the Interactive Data Language (IDL) by ITT VIS. The increasing amount of data produced by these missions and the increasing complexity of image processing algorithms requires access to higher computing power. Parallel computing is a cost-effective way to increase the speed of computation, but algorithms oftentimes have to be modified to take advantage of parallel systems. Enhancing IDL to work on clusters gives scientists access to increased performance in a familiar programming environment. The goal of this project was to enable IDL applications to benefit from both computing clusters as well as graphics processing units (GPUs) for accelerating data analysis tasks. The tool suite developed in this project enables scientists now to solve demanding data analysis problems in IDL that previously required specialized software, and it allows them to be solved orders of magnitude faster than on conventional PCs. The tool suite consists of three components: (1) TaskDL, a software tool that simplifies the creation and management of task farms, collections of tasks that can be processed independently and require only small amounts of data communication; (2) mpiDL, a tool that allows IDL developers to use the Message Passing Interface (MPI) inside IDL for problems that require large amounts of data to be exchanged among multiple processors; and (3) GPULib, a tool that simplifies the use of GPUs as mathematical coprocessors from within IDL. mpiDL is unique in its support for the full MPI standard and its support of a broad range of MPI implementations. GPULib is unique in enabling users to take advantage of an inexpensive piece of hardware, possibly already installed in their computer, and achieve orders of magnitude faster execution time for numerically complex algorithms. TaskDL enables the simple setup and management of task farms on compute clusters. The products developed in this project have the potential to interact, so one can build a cluster of PCs, each equipped with a GPU, and use mpiDL to communicate between the nodes and GPULib to accelerate the computations on each node.

Effects of selected socio-demographic characteristics on nutrition knowledge and eating behavior of elementary students in two provinces in China.

PubMed

Qian, Ling; Zhang, Fan; Newman, Ian M; Shell, Duane F; Du, Weijing

2017-07-14

National and international child health surveys have indicated an increase in childhood obesity in China. The increase has been attributed to a rising standard of living, increasing availability of unhealthy foods, and a lack of knowledge about healthy diet. The objective of this study was to assess the effect of selected socio-demographic characteristics on the BMI, nutrition knowledge, and eating behavior of elementary school children. Multistage stratified cluster sampling was used. Information on demographics, nutrition knowledge, and eating behavior was gathered by means of questionnaires. The schools' doctors provided the height and weight data. The study was set in one economically advantaged and one economically disadvantaged province in China. The participants were Grade 3 students, ages 8-10 years (N = 3922). A cluster analysis identified four socio-demographic variables distinguished by parental education and family living arrangement. A one-way ANOVA compared differences among the clusters in BMI, child nutrition knowledge, and child eating behavior. Students in the cluster with lowest parent education level had the lowest nutrition knowledge scores and eating behavior scores. There was no significant benefit from college education versus high school education of parents in the other three clusters. BMI was not affected by parent education level. The nutrition status of elementary school age children will benefit most by increasing the general level of education for those adults who are presently least educated.

Understanding clusters of risk factors across different environmental and social contexts for the prediction of injuries among Canadian youth.

PubMed

Russell, K; Davison, C; King, N; Pike, I; Pickett, W

2016-05-01

Among Canadian youth, injury is the most common reason for presentation to the emergency department. Youth who commonly engage in multiple risk-taking behaviours are at greater risk for injury, but is it unknown if this phenomenon is more pronounced in different contexts. We aimed to study relationships between risk-taking behaviours and injury, and variations in such relationships between different environmental and social contexts, among youth in Canada. Risk-taking behaviour and injury outcome data were collected from grade 9 to 10 students using the 2009-2010 (Cycle 6) of the Health Behaviour in School-Aged Children Survey (n=10,429). Principal components analysis was used to identify clusters of risk-taking behaviours. Within each identified cluster, the degree of risk-taking was categorized into quartiles from lowest to highest engagement in the behaviours. Risk ratios with 95% confidence intervals were calculated to determine the association between the risk of any injury and the degree of risk-taking behaviour specific to the cluster. Clusters were then examined across home, school, neighbourhood and sport contexts. Four clusters of risk-taking behaviour were identified which were labelled as "gateway substance use", "hard drugs and weapons", "overt risk-taking", and "physical activity". Each cluster was related to injury occurrence in a graded fashion. Clusters of risk behaviour were most strongly associated with injuries sustained in neighbourhood settings, and expectedly, increasing physical activity behaviours were associated with increased risk of sport injuries and injuries occurring at school. This study furthers understanding of clustered risk-taking phenomena that put youth at increasing levels of injury risk. Higher risks for injury and associated gradients were observed in less structured contexts such as neighbourhoods. In contrast, clustered physical activity behaviours were most related to school injury or sport injury and were more likely to be sustained in a supervised context. Understanding the clustered and cumulative nature of risk-behaviours, and how these vary by environmental and social context, helps to explain potential mechanisms of injury as well as modifiable factors that may be important avenues for intervention. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cluster and principal component analysis based on SSR markers of Amomum tsao-ko in Jinping County of Yunnan Province

NASA Astrophysics Data System (ADS)

Ma, Mengli; Lei, En; Meng, Hengling; Wang, Tiantao; Xie, Linyan; Shen, Dong; Xianwang, Zhou; Lu, Bingyue

2017-08-01

Amomum tsao-ko is a commercial plant that used for various purposes in medicinal and food industries. For the present investigation, 44 germplasm samples were collected from Jinping County of Yunnan Province. Clusters analysis and 2-dimensional principal component analysis (PCA) was used to represent the genetic relations among Amomum tsao-ko by using simple sequence repeat (SSR) markers. Clustering analysis clearly distinguished the samples groups. Two major clusters were formed; first (Cluster I) consisted of 34 individuals, the second (Cluster II) consisted of 10 individuals, Cluster I as the main group contained multiple sub-clusters. PCA also showed 2 groups: PCA Group 1 included 29 individuals, PCA Group 2 included 12 individuals, consistent with the results of cluster analysis. The purpose of the present investigation was to provide information on genetic relationship of Amomum tsao-ko germplasm resources in main producing areas, also provide a theoretical basis for the protection and utilization of Amomum tsao-ko resources.

Galaxy clusters in the SDSS Stripe 82 based on photometric redshifts

DOE PAGES

Durret, F.; Adami, C.; Bertin, E.; ...

2015-06-10

Based on a recent photometric redshift galaxy catalogue, we have searched for galaxy clusters in the Stripe ~82 region of the Sloan Digital Sky Survey by applying the Adami & MAzure Cluster FInder (AMACFI). Extensive tests were made to fine-tune the AMACFI parameters and make the cluster detection as reliable as possible. The same method was applied to the Millennium simulation to estimate our detection efficiency and the approximate masses of the detected clusters. Considering all the cluster galaxies (i.e. within a 1 Mpc radius of the cluster to which they belong and with a photoz differing by less thanmore » 0.05 from that of the cluster), we stacked clusters in various redshift bins to derive colour-magnitude diagrams and galaxy luminosity functions (GLFs). For each galaxy with absolute magnitude brighter than -19.0 in the r band, we computed the disk and spheroid components by applying SExtractor, and by stacking clusters we determined how the disk-to-spheroid flux ratio varies with cluster redshift and mass. We also detected 3663 clusters in the redshift range 0.1513 and a few 10 14 solar masses. Furthermore, by stacking the cluster galaxies in various redshift bins, we find a clear red sequence in the (g'-r') versus r' colour-magnitude diagrams, and the GLFs are typical of clusters, though with a possible contamination from field galaxies. The morphological analysis of the cluster galaxies shows that the fraction of late-type to early-type galaxies shows an increase with redshift (particularly in high mass clusters) and a decrease with detection level, i.e. cluster mass. From the properties of the cluster galaxies, the majority of the candidate clusters detected here seem to be real clusters with typical cluster properties.« less

Galaxy clusters in the SDSS Stripe 82 based on photometric redshifts

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

Durret, F.; Adami, C.; Bertin, E.

Based on a recent photometric redshift galaxy catalogue, we have searched for galaxy clusters in the Stripe ~82 region of the Sloan Digital Sky Survey by applying the Adami & MAzure Cluster FInder (AMACFI). Extensive tests were made to fine-tune the AMACFI parameters and make the cluster detection as reliable as possible. The same method was applied to the Millennium simulation to estimate our detection efficiency and the approximate masses of the detected clusters. Considering all the cluster galaxies (i.e. within a 1 Mpc radius of the cluster to which they belong and with a photoz differing by less thanmore » 0.05 from that of the cluster), we stacked clusters in various redshift bins to derive colour-magnitude diagrams and galaxy luminosity functions (GLFs). For each galaxy with absolute magnitude brighter than -19.0 in the r band, we computed the disk and spheroid components by applying SExtractor, and by stacking clusters we determined how the disk-to-spheroid flux ratio varies with cluster redshift and mass. We also detected 3663 clusters in the redshift range 0.1513 and a few 10 14 solar masses. Furthermore, by stacking the cluster galaxies in various redshift bins, we find a clear red sequence in the (g'-r') versus r' colour-magnitude diagrams, and the GLFs are typical of clusters, though with a possible contamination from field galaxies. The morphological analysis of the cluster galaxies shows that the fraction of late-type to early-type galaxies shows an increase with redshift (particularly in high mass clusters) and a decrease with detection level, i.e. cluster mass. From the properties of the cluster galaxies, the majority of the candidate clusters detected here seem to be real clusters with typical cluster properties.« less

Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome.

PubMed

Lalonde, Michel; Wells, R Glenn; Birnie, David; Ruddy, Terrence D; Wassenaar, Richard

2014-07-01

Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster analysis results were similar to SPECT RNA phase analysis (ROC AUC = 0.78, p = 0.73 vs cluster AUC; sensitivity/specificity = 59%/89%) and PET scar size analysis (ROC AUC = 0.73, p = 1.0 vs cluster AUC; sensitivity/specificity = 76%/67%). A SPECT RNA cluster analysis algorithm was developed for the prediction of CRT outcome. Cluster analysis results produced results equivalent to those obtained from Fourier and scar analysis.

Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

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

Lalonde, Michel, E-mail: mlalonde15@rogers.com; Wassenaar, Richard; Wells, R. Glenn

2014-07-15

Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: Aboutmore » 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster analysis results were similar to SPECT RNA phase analysis (ROC AUC = 0.78, p = 0.73 vs cluster AUC; sensitivity/specificity = 59%/89%) and PET scar size analysis (ROC AUC = 0.73, p = 1.0 vs cluster AUC; sensitivity/specificity = 76%/67%). Conclusions: A SPECT RNA cluster analysis algorithm was developed for the prediction of CRT outcome. Cluster analysis results produced results equivalent to those obtained from Fourier and scar analysis.« less

Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps

PubMed Central

Godoy, Eduardo J.; Lozano, Miguel; Martínez-Mateu, Laura; Atienza, Felipe; Saiz, Javier; Sebastian, Rafael

2017-01-01

Non-invasive localization of continuous atrial ectopic beats remains a cornerstone for the treatment of atrial arrhythmias. The lack of accurate tools to guide electrophysiologists leads to an increase in the recurrence rate of ablation procedures. Existing approaches are based on the analysis of the P-waves main characteristics and the forward body surface potential maps (BSPMs) or on the inverse estimation of the electric activity of the heart from those BSPMs. These methods have not provided an efficient and systematic tool to localize ectopic triggers. In this work, we propose the use of machine learning techniques to spatially cluster and classify ectopic atrial foci into clearly differentiated atrial regions by using the body surface P-wave integral map (BSPiM) as a biomarker. Our simulated results show that ectopic foci with similar BSPiM naturally cluster into differentiated non-intersected atrial regions and that new patterns could be correctly classified with an accuracy of 97% when considering 2 clusters and 96% for 4 clusters. Our results also suggest that an increase in the number of clusters is feasible at the cost of decreasing accuracy. PMID:28704537

Microforms in gravel bed rivers: Formation, disintegration, and effects on bedload transport

USGS Publications Warehouse

Strom, K.; Papanicolaou, A.N.; Evangelopoulos, N.; Odeh, M.

2004-01-01

This research aims to advance current knowledge on cluster formation and evolution by tackling some of the aspects associated with cluster microtopography and the effects of clusters on bedload transport. The specific objectives of the study are (1) to identify the bed shear stress range in which clusters form and disintegrate, (2) to quantitatively describe the spacing characteristics and orientation of clusters with respect to flow characteristics, (3) to quantify the effects clusters have on the mean bedload rate, and (4) to assess the effects of clusters on the pulsating nature of bedload. In order to meet the objectives of this study, two main experimental scenarios, namely, Test Series A and B (20 experiments overall) are considered in a laboratory flume under well-controlled conditions. Series A tests are performed to address objectives (1) and (2) while Series B is designed to meet objectives (3) and (4). Results show that cluster microforms develop in uniform sediment at 1.25 to 2 times the Shields parameter of an individual particle and start disintegrating at about 2.25 times the Shields parameter. It is found that during an unsteady flow event, effects of clusters on bedload transport rate can be classified in three different phases: a sink phase where clusters absorb incoming sediment, a neutral phase where clusters do not affect bedload, and a source phase where clusters release particles. Clusters also increase the magnitude of the fluctuations in bedload transport rate, showing that clusters amplify the unsteady nature of bedload transport. A fourth-order autoregressive, autoregressive integrated moving average model is employed to describe the time series of bedload and provide a predictive formula for predicting bedload at different periods. Finally, a change-point analysis enhanced with a binary segmentation procedure is performed to identify the abrupt changes in the bedload statistic characteristics due to the effects of clusters and detect the different phases in bedload time series using probability theory. The analysis verifies the experimental findings that three phases are detected in the bedload rate time series structure, namely, sink, neutral, and source. ?? ASCE / JUNE 2004.

The X-CLASS-redMaPPer galaxy cluster comparison. I. Identification procedures

NASA Astrophysics Data System (ADS)

Sadibekova, T.; Pierre, M.; Clerc, N.; Faccioli, L.; Gastaud, R.; Le Fevre, J.-P.; Rozo, E.; Rykoff, E.

2014-11-01

Context. This paper is the first in a series undertaking a comprehensive correlation analysis between optically selected and X-ray-selected cluster catalogues. The rationale of the project is to develop a holistic picture of galaxy clusters utilising optical and X-ray-cluster-selected catalogues with well-understood selection functions. Aims: Unlike most of the X-ray/optical cluster correlations to date, the present paper focuses on the non-matching objects in either waveband. We investigate how the differences observed between the optical and X-ray catalogues may stem from (1) a shortcoming of the detection algorithms; (2) dispersion in the X-ray/optical scaling relations; or (3) substantial intrinsic differences between the cluster populations probed in the X-ray and optical bands. The aim is to inventory and elucidate these effects in order to account for selection biases in the further determination of X-ray/optical cluster scaling relations. Methods: We correlated the X-CLASS serendipitous cluster catalogue extracted from the XMM archive with the redMaPPer optical cluster catalogue derived from the Sloan Digital Sky Survey (DR8). We performed a detailed and, in large part, interactive analysis of the matching output from the correlation. The overlap between the two catalogues has been accurately determined and possible cluster positional errors were manually recovered. The final samples comprise 270 and 355 redMaPPer and X-CLASS clusters, respectively. X-ray cluster matching rates were analysed as a function of optical richness. In the second step, the redMaPPer clusters were correlated with the entire X-ray catalogue, containing point and uncharacterised sources (down to a few 10-15 erg s-1 cm-2 in the [0.5-2] keV band). A stacking analysis was performed for the remaining undetected optical clusters. Results: We find that all rich (λ ≥ 80) clusters are detected in X-rays out to z = 0.6. Below this redshift, the richness threshold for X-ray detection steadily decreases with redshift. Likewise, all X-ray bright clusters are detected by redMaPPer. After correcting for obvious pipeline shortcomings (about 10% of the cases both in optical and X-ray), ~50% of the redMaPPer (down to a richness of 20) are found to coincide with an X-CLASS cluster; when considering X-ray sources of any type, this fraction increases to ~80%; for the remaining objects, the stacking analysis finds a weak signal within 0.5 Mpc around the cluster optical centres. The fraction of clusters totally dominated by AGN-type emission appears to be a few percent. Conversely, ~40% of the X-CLASS clusters are identified with a redMaPPer (down to a richness of 20) - part of the non-matches being due to the X-CLASS sample extending further out than redMaPPer (z< 1.5 vs. z< 0.6), but extending the correlation down to a richness of 5 raises the matching rate to ~65%. Conclusions: This state-of-the-art study involving two well-validated cluster catalogues has shown itself to be complex, and it points to a number of issues inherent to blind cross-matching, owing both to pipeline shortcomings and cluster peculiar properties. These can only been accounted for after a manual check. The combined X-ray and optical scaling relations will be presented in a subsequent article.

Near real-time space-time cluster analysis for detection of enteric disease outbreaks in a community setting.

PubMed

Glatman-Freedman, Aharona; Kaufman, Zalman; Kopel, Eran; Bassal, Ravit; Taran, Diana; Valinsky, Lea; Agmon, Vered; Shpriz, Manor; Cohen, Daniel; Anis, Emilia; Shohat, Tamy

2016-08-01

To enhance timely surveillance of bacterial enteric pathogens, space-time cluster analysis was introduced in Israel in May 2013. Stool isolation data of Salmonella, Shigella, and Campylobacter from patients of a large Health Maintenance Organization were analyzed weekly by ArcGIS and SaTScan, and cluster results were sent promptly to local departments of health (LDOHs). During eighteen months, we identified 52 Shigella sonnei clusters, two Salmonella clusters, and no Campylobacter clusters. S. sonnei clusters lasted from one to 33 days and included three to 30 individuals. Thirty-one (60%) of the S. sonnei clusters were known to LDOHs prior to cluster analysis. Clusters not previously known by the LDOHs prompted epidemiologic investigations. In 31 of the 37 (84%) confirmed clusters, educational institutes (nursery schools, kindergartens, and a primary school) were involved. Cluster analysis demonstrated capability to complement enteric disease surveillance. Scaling up the system can further enhance timely detection and control of outbreaks. Copyright © 2016 The British Infection Association. Published by Elsevier Ltd. All rights reserved.

An effective fuzzy kernel clustering analysis approach for gene expression data.

PubMed

Sun, Lin; Xu, Jiucheng; Yin, Jiaojiao

2015-01-01

Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approach to fuzzy kernel clustering analysis (FKCA) that identifies desired cluster number and obtains more steady results for gene expression data. First of all, to optimize characteristic differences and estimate optimal cluster number, Gaussian kernel function is introduced to improve spectrum analysis method (SAM). By combining subtractive clustering with max-min distance mean, maximum distance method (MDM) is proposed to determine cluster centers. Then, the corresponding steps of improved SAM (ISAM) and MDM are given respectively, whose superiority and stability are illustrated through performing experimental comparisons on gene expression data. Finally, by introducing ISAM and MDM into FKCA, an effective improved FKCA algorithm is proposed. Experimental results from public gene expression data and UCI database show that the proposed algorithms are feasible for cluster analysis, and the clustering accuracy is higher than the other related clustering algorithms.

Importance of Viral Sequence Length and Number of Variable and Informative Sites in Analysis of HIV Clustering.

PubMed

Novitsky, Vlad; Moyo, Sikhulile; Lei, Quanhong; DeGruttola, Victor; Essex, M

2015-05-01

To improve the methodology of HIV cluster analysis, we addressed how analysis of HIV clustering is associated with parameters that can affect the outcome of viral clustering. The extent of HIV clustering and tree certainty was compared between 401 HIV-1C near full-length genome sequences and subgenomic regions retrieved from the LANL HIV Database. Sliding window analysis was based on 99 windows of 1,000 bp and 45 windows of 2,000 bp. Potential associations between the extent of HIV clustering and sequence length and the number of variable and informative sites were evaluated. The near full-length genome HIV sequences showed the highest extent of HIV clustering and the highest tree certainty. At the bootstrap threshold of 0.80 in maximum likelihood (ML) analysis, 58.9% of near full-length HIV-1C sequences but only 15.5% of partial pol sequences (ViroSeq) were found in clusters. Among HIV-1 structural genes, pol showed the highest extent of clustering (38.9% at a bootstrap threshold of 0.80), although it was significantly lower than in the near full-length genome sequences. The extent of HIV clustering was significantly higher for sliding windows of 2,000 bp than 1,000 bp. We found a strong association between the sequence length and proportion of HIV sequences in clusters, and a moderate association between the number of variable and informative sites and the proportion of HIV sequences in clusters. In HIV cluster analysis, the extent of detectable HIV clustering is directly associated with the length of viral sequences used, as well as the number of variable and informative sites. Near full-length genome sequences could provide the most informative HIV cluster analysis. Selected subgenomic regions with a high extent of HIV clustering and high tree certainty could also be considered as a second choice.

Importance of Viral Sequence Length and Number of Variable and Informative Sites in Analysis of HIV Clustering

PubMed Central

Novitsky, Vlad; Moyo, Sikhulile; Lei, Quanhong; DeGruttola, Victor

2015-01-01

Abstract To improve the methodology of HIV cluster analysis, we addressed how analysis of HIV clustering is associated with parameters that can affect the outcome of viral clustering. The extent of HIV clustering and tree certainty was compared between 401 HIV-1C near full-length genome sequences and subgenomic regions retrieved from the LANL HIV Database. Sliding window analysis was based on 99 windows of 1,000 bp and 45 windows of 2,000 bp. Potential associations between the extent of HIV clustering and sequence length and the number of variable and informative sites were evaluated. The near full-length genome HIV sequences showed the highest extent of HIV clustering and the highest tree certainty. At the bootstrap threshold of 0.80 in maximum likelihood (ML) analysis, 58.9% of near full-length HIV-1C sequences but only 15.5% of partial pol sequences (ViroSeq) were found in clusters. Among HIV-1 structural genes, pol showed the highest extent of clustering (38.9% at a bootstrap threshold of 0.80), although it was significantly lower than in the near full-length genome sequences. The extent of HIV clustering was significantly higher for sliding windows of 2,000 bp than 1,000 bp. We found a strong association between the sequence length and proportion of HIV sequences in clusters, and a moderate association between the number of variable and informative sites and the proportion of HIV sequences in clusters. In HIV cluster analysis, the extent of detectable HIV clustering is directly associated with the length of viral sequences used, as well as the number of variable and informative sites. Near full-length genome sequences could provide the most informative HIV cluster analysis. Selected subgenomic regions with a high extent of HIV clustering and high tree certainty could also be considered as a second choice. PMID:25560745

Geographical Analysis of the Distribution and Spread of Human Rabies in China from 2005 to 2011

PubMed Central

Yin, Wenwu; Yu, Hongjie; Si, Yali; Li, Jianhui; Zhou, Yuanchun; Zhou, Xiaoyan; Magalhães, Ricardo J. Soares.

2013-01-01

Background Rabies is a significant public health problem in China in that it records the second highest case incidence globally. Surveillance data on canine rabies in China is lacking and human rabies notifications can be a useful indicator of areas where animal and human rabies control could be integrated. Previous spatial epidemiological studies lacked adequate spatial resolution to inform targeted rabies control decisions. We aimed to describe the spatiotemporal distribution of human rabies and model its geographical spread to provide an evidence base to inform future integrated rabies control strategies in China. Methods We geo-referenced a total of 17,760 human rabies cases of China from 2005 to 2011. In our spatial analyses we used Gaussian kernel density analysis, average nearest neighbor distance, Spatial Temporal Density-Based Spatial Clustering of Applications with Noise and developed a model of rabies spatiotemporal spread. Findings Human rabies cases increased from 2005 to 2007 and decreased during 2008 to 2011 companying change of the spatial distribution. The ANN distance among human rabies cases increased between 2005 and 2011, and the degree of clustering of human rabies cases decreased during that period. A total 480 clusters were detected by ST-DBSCAN, 89.4% clusters initiated before 2007. Most of clusters were mainly found in South of China. The number and duration of cluster decreased significantly after 2008. Areas with the highest density of human rabies cases varied spatially each year and in some areas remained with high outbreak density for several years. Though few places have recovered from human rabies, most of affected places are still suffering from the disease. Conclusion Human rabies in mainland China is geographically clustered and its spatial extent changed during 2005 to 2011. The results provide a scientific basis for public health authorities in China to improve human rabies control and prevention program. PMID:23991098

Reducing Earth Topography Resolution for SMAP Mission Ground Tracks Using K-Means Clustering

NASA Technical Reports Server (NTRS)

Rizvi, Farheen

2013-01-01

The K-means clustering algorithm is used to reduce Earth topography resolution for the SMAP mission ground tracks. As SMAP propagates in orbit, knowledge of the radar antenna footprints on Earth is required for the antenna misalignment calibration. Each antenna footprint contains a latitude and longitude location pair on the Earth surface. There are 400 pairs in one data set for the calibration model. It is computationally expensive to calculate corresponding Earth elevation for these data pairs. Thus, the antenna footprint resolution is reduced. Similar topographical data pairs are grouped together with the K-means clustering algorithm. The resolution is reduced to the mean of each topographical cluster called the cluster centroid. The corresponding Earth elevation for each cluster centroid is assigned to the entire group. Results show that 400 data points are reduced to 60 while still maintaining algorithm performance and computational efficiency. In this work, sensitivity analysis is also performed to show a trade-off between algorithm performance versus computational efficiency as the number of cluster centroids and algorithm iterations are increased.

General-circulation-model simulations of future snowpack in the western United States

USGS Publications Warehouse

McCabe, G.J.; Wolock, D.M.

1999-01-01

April 1 snowpack accumulations measured at 311 snow courses in the western United States (U.S.) are grouped using a correlation-based cluster analysis. A conceptual snow accumulation and melt model and monthly temperature and precipitation for each cluster are used to estimate cluster-average April 1 snowpack. The conceptual snow model is subsequently used to estimate future snowpack by using changes in monthly temperature and precipitation simulated by the Canadian Centre for Climate Modeling and Analysis (CCC) and the Hadley Centre for Climate Prediction and Research (HADLEY) general circulation models (GCMs). Results for the CCC model indicate that although winter precipitation is estimated to increase in the future, increases in temperatures will result in large decreases in April 1 snowpack for the entire western US. Results for the HADLEY model also indicate large decreases in April 1 snowpack for most of the western US, but the decreases are not as severe as those estimated using the CCC simulations. Although snowpack conditions are estimated to decrease for most areas of the western US, both GCMs estimate a general increase in winter precipitation toward the latter half of the next century. Thus, water quantity may be increased in the western US; however, the timing of runoff will be altered because precipitation will more frequently occur as rain rather than as snow.

Caloric beverage drinking patterns are differentially associated with diet quality and adiposity among Spanish girls and boys.

PubMed

Schröder, Helmut; Mendez, Michelle A; Ribas, Lourdes; Funtikova, Anna N; Gomez, Santiago F; Fíto, Montserrat; Aranceta, Javier; Serra-Majem, Lluis

2014-09-01

The present study assesses the impact of beverage consumption pattern on diet quality and anthropometric proxy measures for abdominal adiposity in Spanish adolescents. Data were obtained from a representative national sample of 1,149 Spanish adolescents aged 10-18 years. Height, weight, and waist circumferences were measured. Dietary assessment was performed with a 24-h recall. Beverage patterns were identified by cluster analysis. Adherence to the Mediterranean diet was measured by the KIDMED index. Three beverage clusters were identified for boys--"whole milk" (62.5 %), "low-fat milk" (17.5 %) and "soft drinks" (20.1 %)-and for girls--"whole milk" (57.8 %), "low-fat milk" (20.8 %) and juice (21.4 %), accounting for 8.3, 9.6, 13.9, 8.6, 11.5 and 12.9 % of total energy intake, respectively. Each unit of increase in the KIDMED index was associated with a 14.0 % higher (p = 0.004) and 11.0 % lower (p = 0.048) probability of membership in the "low-fat milk" and "soft drinks" cluster in girls and boys, respectively, compared with the "whole milk" cluster. Boys in the "soft drinks" cluster had a higher risk of 1-unit increase in BMI z score (29.0 %, p = 0.040), 1-cm increase in waist circumference regressed on height and age (3.0 %, p = 0.027) and 0.1-unit increase in waist/height ratio (21.4 %, p = 0.031) compared with the "whole milk" cluster. A caloric beverage pattern dominated by intake of "soft drinks" is related to general and abdominal adiposity and diet quality in Spanish male adolescents.

Performance comparison analysis library communication cluster system using merge sort

NASA Astrophysics Data System (ADS)

Wulandari, D. A. R.; Ramadhan, M. E.

2018-04-01

Begins by using a single processor, to increase the speed of computing time, the use of multi-processor was introduced. The second paradigm is known as parallel computing, example cluster. The cluster must have the communication potocol for processing, one of it is message passing Interface (MPI). MPI have many library, both of them OPENMPI and MPICH2. Performance of the cluster machine depend on suitable between performance characters of library communication and characters of the problem so this study aims to analyze the comparative performances libraries in handling parallel computing process. The case study in this research are MPICH2 and OpenMPI. This case research execute sorting’s problem to know the performance of cluster system. The sorting problem use mergesort method. The research method is by implementing OpenMPI and MPICH2 on a Linux-based cluster by using five computer virtual then analyze the performance of the system by different scenario tests and three parameters for to know the performance of MPICH2 and OpenMPI. These performances are execution time, speedup and efficiency. The results of this study showed that the addition of each data size makes OpenMPI and MPICH2 have an average speed-up and efficiency tend to increase but at a large data size decreases. increased data size doesn’t necessarily increased speed up and efficiency but only execution time example in 100000 data size. OpenMPI has a execution time greater than MPICH2 example in 1000 data size average execution time with MPICH2 is 0,009721 and OpenMPI is 0,003895 OpenMPI can customize communication needs.

Effects of Group Size and Lack of Sphericity on the Recovery of Clusters in K-Means Cluster Analysis

ERIC Educational Resources Information Center

de Craen, Saskia; Commandeur, Jacques J. F.; Frank, Laurence E.; Heiser, Willem J.

2006-01-01

K-means cluster analysis is known for its tendency to produce spherical and equally sized clusters. To assess the magnitude of these effects, a simulation study was conducted, in which populations were created with varying departures from sphericity and group sizes. An analysis of the recovery of clusters in the samples taken from these…

Changing cluster composition in cluster randomised controlled trials: design and analysis considerations

PubMed Central

2014-01-01

Background There are many methodological challenges in the conduct and analysis of cluster randomised controlled trials, but one that has received little attention is that of post-randomisation changes to cluster composition. To illustrate this, we focus on the issue of cluster merging, considering the impact on the design, analysis and interpretation of trial outcomes. Methods We explored the effects of merging clusters on study power using standard methods of power calculation. We assessed the potential impacts on study findings of both homogeneous cluster merges (involving clusters randomised to the same arm of a trial) and heterogeneous merges (involving clusters randomised to different arms of a trial) by simulation. To determine the impact on bias and precision of treatment effect estimates, we applied standard methods of analysis to different populations under analysis. Results Cluster merging produced a systematic reduction in study power. This effect depended on the number of merges and was most pronounced when variability in cluster size was at its greatest. Simulations demonstrate that the impact on analysis was minimal when cluster merges were homogeneous, with impact on study power being balanced by a change in observed intracluster correlation coefficient (ICC). We found a decrease in study power when cluster merges were heterogeneous, and the estimate of treatment effect was attenuated. Conclusions Examples of cluster merges found in previously published reports of cluster randomised trials were typically homogeneous rather than heterogeneous. Simulations demonstrated that trial findings in such cases would be unbiased. However, simulations also showed that any heterogeneous cluster merges would introduce bias that would be hard to quantify, as well as having negative impacts on the precision of estimates obtained. Further methodological development is warranted to better determine how to analyse such trials appropriately. Interim recommendations include avoidance of cluster merges where possible, discontinuation of clusters following heterogeneous merges, allowance for potential loss of clusters and additional variability in cluster size in the original sample size calculation, and use of appropriate ICC estimates that reflect cluster size. PMID:24884591

New Scenario of Dynamical Heterogeneity in Supercooled Liquid and Glassy States of 2D Monatomic System.

PubMed

Van Hoang, Vo; Teboul, Victor; Odagaki, Takashi

2015-12-24

Via analysis of spatiotemporal arrangements of atoms based on their dynamics in supercooled liquid and glassy states of a 2D monatomic system with a double-well Lennard-Jones-Gauss (LJG) interaction potential, we find a new scenario of dynamical heterogeneity. Atoms with the same or very close mobility have a tendency to aggregate into clusters. The number of atoms with high mobility (and size of their clusters) increases with decreasing temperature passing over a maximum before decreasing down to zero. Position of the peak moves toward a lower temperature if mobility of atoms in clusters is lower together with an enhancement of height of the peak. In contrast, the number of atoms with very low mobility or solidlike atoms (and size of their clusters) has a tendency to increase with decreasing temperature and then it suddenly increases in the vicinity of the glass transition temperature leading to the formation of a glassy state. A sudden increase in the number of strongly correlated solidlike atoms in the vicinity of a glass transition temperature (Tg) may be an origin of a drastical increase in viscosity of the glass-forming systems approaching the glass transition. In fact, we find that the diffusion coefficient decays exponentially with a fraction of solidlike atoms exhibiting a sudden decrease in the vicinity of the glass transition region.

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

Jacobson, Heather R.; Pilachowski, Catherine A.; Friel, Eileen D., E-mail: jacob189@msu.edu, E-mail: catyp@astro.indiana.edu, E-mail: edfriel@mac.com

We present a detailed chemical abundance study of evolved stars in 10 open clusters based on Hydra multi-object echelle spectra obtained with the WIYN 3.5 m telescope. From an analysis of both equivalent widths and spectrum synthesis, abundances have been determined for the elements Fe, Na, O, Mg, Si, Ca, Ti, Ni, Zr, and for two of the 10 clusters, Al and Cr. To our knowledge, this is the first detailed abundance analysis for clusters NGC 1245, NGC 2194, NGC 2355, and NGC 2425. These 10 clusters were selected for analysis because they span a Galactocentric distance range R{sub gc}more » {approx} 9-13 kpc, the approximate location of the transition between the inner and outer disks. Combined with cluster samples from our previous work and those of other studies in the literature, we explore abundance trends as a function of cluster R{sub gc}, age, and [Fe/H]. As found previously by us and other studies, the [Fe/H] distribution appears to decrease with increasing R{sub gc} to a distance of {approx}12 kpc and then flattens to a roughly constant value in the outer disk. Cluster average element [X/Fe] ratios appear to be independent of R{sub gc}, although the picture for [O/Fe] is more complicated with a clear trend of [O/Fe] with [Fe/H] and sample incompleteness. Other than oxygen, no other element [X/Fe] exhibits a clear trend with [Fe/H]; likewise, there does not appear to be any strong correlation between abundance and cluster age. We divided clusters into different age bins to explore temporal variations in the radial element distributions. The radial metallicity gradient appears to have flattened slightly as a function of time, as found by other studies. There is also some indication that the transition from the inner disk metallicity gradient to the {approx}constant [Fe/H] distribution of the outer disk occurs at different Galactocentric radii for different age bins. However, interpretation of the time evolution of radial abundance distributions is complicated by the unequal R{sub gc} and [Fe/H] ranges spanned by clusters in different age bins.« less

Classification of patients based on their evaluation of hospital outcomes: cluster analysis following a national survey in Norway

PubMed Central

2013-01-01

Background A general trend towards positive patient-reported evaluations of hospitals could be taken as a sign that most patients form a homogeneous, reasonably pleased group, and consequently that there is little need for quality improvement. The objective of this study was to explore this assumption by identifying and statistically validating clusters of patients based on their evaluation of outcomes related to overall satisfaction, malpractice and benefit of treatment. Methods Data were collected using a national patient-experience survey of 61 hospitals in the 4 health regions in Norway during spring 2011. Postal questionnaires were mailed to 23,420 patients after their discharge from hospital. Cluster analysis was performed to identify response clusters of patients, based on their responses to single items about overall patient satisfaction, benefit of treatment and perception of malpractice. Results Cluster analysis identified six response groups, including one cluster with systematically poorer evaluation across outcomes (18.5% of patients) and one small outlier group (5.3%) with very poor scores across all outcomes. One-Way ANOVA with post-hoc tests showed that most differences between the six response groups on the three outcome items were significant. The response groups were significantly associated with nine patient-experience indicators (p < 0.001), and all groups were significantly different from each of the other groups on a majority of the patient-experience indicators. Clusters were significantly associated with age, education, self-perceived health, gender, and the degree to write open comments in the questionnaire. Conclusions The study identified five response clusters with distinct patient-reported outcome scores, in addition to a heterogeneous outlier group with very poor scores across all outcomes. The outlier group and the cluster with systematically poorer evaluation across outcomes comprised almost one-quarter of all patients, clearly demonstrating the need to tailor quality initiatives and improve patient-perceived quality in hospitals. More research on patient clustering in patient evaluation is needed, as well as standardization of methodology to increase comparability across studies. PMID:23433450

A generalized analysis of hydrophobic and loop clusters within globular protein sequences

PubMed Central

Eudes, Richard; Le Tuan, Khanh; Delettré, Jean; Mornon, Jean-Paul; Callebaut, Isabelle

2007-01-01

Background Hydrophobic Cluster Analysis (HCA) is an efficient way to compare highly divergent sequences through the implicit secondary structure information directly derived from hydrophobic clusters. However, its efficiency and application are currently limited by the need of user expertise. In order to help the analysis of HCA plots, we report here the structural preferences of hydrophobic cluster species, which are frequently encountered in globular domains of proteins. These species are characterized only by their hydrophobic/non-hydrophobic dichotomy. This analysis has been extended to loop-forming clusters, using an appropriate loop alphabet. Results The structural behavior of hydrophobic cluster species, which are typical of protein globular domains, was investigated within banks of experimental structures, considered at different levels of sequence redundancy. The 294 more frequent hydrophobic cluster species were analyzed with regard to their association with the different secondary structures (frequencies of association with secondary structures and secondary structure propensities). Hydrophobic cluster species are predominantly associated with regular secondary structures, and a large part (60 %) reveals preferences for α-helices or β-strands. Moreover, the analysis of the hydrophobic cluster amino acid composition generally allows for finer prediction of the regular secondary structure associated with the considered cluster within a cluster species. We also investigated the behavior of loop forming clusters, using a "PGDNS" alphabet. These loop clusters do not overlap with hydrophobic clusters and are highly associated with coils. Finally, the structural information contained in the hydrophobic structural words, as deduced from experimental structures, was compared to the PSI-PRED predictions, revealing that β-strands and especially α-helices are generally over-predicted within the limits of typical β and α hydrophobic clusters. Conclusion The dictionary of hydrophobic clusters described here can help the HCA user to interpret and compare the HCA plots of globular protein sequences, as well as provides an original fundamental insight into the structural bricks of protein folds. Moreover, the novel loop cluster analysis brings additional information for secondary structure prediction on the whole sequence through a generalized cluster analysis (GCA), and not only on regular secondary structures. Such information lays the foundations for developing a new and original tool for secondary structure prediction. PMID:17210072

Spatial distribution and cluster analysis of risky sexual behaviours and STDs reported by Chinese adults in Guangzhou, China: a representative population-based study

PubMed Central

Chen, Wen; Zhou, Fangjing; Hall, Brian J; Wang, Yu; Latkin, Carl; Ling, Li; Tucker, Joseph D

2016-01-01

Objectives To assess associations between residences location, risky sexual behaviours and sexually transmitted diseases (STDs) among adults living in Guangzhou, China. Methods Data were obtained from 751 Chinese adults aged 18–59 years in Guangzhou, China, using stratified random sampling by using spatial epidemiological methods. Face-to-face household interviews were conducted to collect self-report data on risky sexual behaviours and diagnosed STDs. Kulldorff’s spatial scan statistic was implemented to identify and detect spatial distribution and clusters of risky sexual behaviours and STDs. The presence and location of statistically significant clusters were mapped in the study areas using ArcGIS software. Results The prevalence of self-reported risky sexual behaviours was between 5.1% and 50.0%. The self-reported lifetime prevalence of diagnosed STDs was 7.06%. Anal intercourse clustered in an area located along the border within the rural–urban continuum (p=0.001). High rate clusters for alcohol or other drugs using before sex (p=0.008) and migrants who lived in Guangzhou <1 year (p=0.007) overlapped this cluster. Excess cases for unprotected sex (p=0.031) overlapped the cluster for college students (p<0.001). Five of nine (55.6%) students who had sexual experience during the last 12 months located in the cluster of unprotected sex. Conclusions Short-term migrants and college students reported greater risky sexual behaviours. Programmes to increase safer sex within these communities to reduce the risk of STDs are warranted in Guangzhou. Spatial analysis identified geographical clusters of risky sexual behaviours, which is critical for optimising surveillance and targeting control measures for these locations in the future. PMID:26843400

Latino/a depression and smoking: an analysis through the lenses of culture, gender, and ethnicity.

PubMed

Lorenzo-Blanco, Elma I; Cortina, Lilia M

2013-06-01

Rates of major depressive disorder (MDD) and cigarette smoking increase with Latino/a acculturation, but this varies by gender and ethnic subgroup. We investigated how lived experiences (i.e., discrimination, family conflict, family cohesion, familismo) clustered together in the everyday lives of Latina/os. We further examined associations of cluster profile and Latino/a subgroup with MDD and smoking, and tested whether gender moderated these associations. Data came from the National Latino Asian American Study, which included 2,554 Latino/as (48 % female; mean age = 38.02 years). K-means cluster analysis revealed six profiles of experience, which varied by gender and socio-cultural characteristics. Proportionately more women than men were in groups with problematic family lives. Acculturated Latino/as were disproportionately represented in profiles reporting frequent discrimination, family conflict, and a lack of shared family values and cohesion. Profiles characterized by high discrimination and family problems also predicted elevated risk for MDD and smoking. Findings suggest that Latino/a acculturation comes jointly with increased discrimination, increased family conflict, and reduced family cohesion and shared family values, exacerbating risk for MDD and smoking. This research on pathways to depression and smoking can inform the development of targeted assessment, prevention, and intervention strategies, tailored to the needs of Latino/as.

HF in clusters of molecular hydrogen. I. Size evolution of quantum solvation by parahydrogen molecules.

PubMed

Jiang, Hao; Bacić, Zlatko

2005-06-22

We present a theoretical study of the quantum solvation of the HF molecule by a small number of parahydrogen molecules, having n = 1-13 solvent particles. The minimum-energy cluster structures determined for n = 1-12 have all of the H(2) molecules in the first solvent shell. The first solvent shell closes at n = 12 and its geometry is icosahedral, with the HF molecule at the center. The quantum-mechanical ground-state properties of the clusters are calculated exactly using the diffusion Monte Carlo method. The zero-point energy of (p-H(2))(n)HF clusters is unusually large, amounting to 86% of the potential well depth for n > 7. The radial probability distribution functions (PDFs) confirm that the first solvent shell is complete for n = 12, and that the 13th p-H(2) molecule begins to fill the second solvent shell. The p-H(2) molecules execute large-amplitude motions and are highly mobile, making the solvent cage exceptionally fluxional. The anisotropy of the solvent, very pronounced for small clusters, decreases rapidly with increasing n, so that for n approximately 8-9 the solvent environment is practically isotropic. The analysis of the pair angular PDF reveals that for a given n, the parahydrogen solvent density around the HF is modulated in a pattern which clearly reflects the lowest-energy cluster configuration. The rigidity of the solvent clusters displays an interesting size dependence, increasing from n = 6 to 9, becoming floppier for n = 10, and increasing again up to n = 12, as the solvent shell is filled. The rigidity of the solvent cage appears to reach its maximum for n = 12, the point at which the first solvent shell is closed.

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

PubMed

Borri, Marco; Schmidt, Maria A; Powell, Ceri; Koh, Dow-Mu; Riddell, Angela M; Partridge, Mike; Bhide, Shreerang A; Nutting, Christopher M; Harrington, Kevin J; Newbold, Katie L; Leach, Martin O

2015-01-01

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

Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2011-01-01

This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison,…

Investigating Subtypes of Child Development: A Comparison of Cluster Analysis and Latent Class Cluster Analysis in Typology Creation

ERIC Educational Resources Information Center

DiStefano, Christine; Kamphaus, R. W.

2006-01-01

Two classification methods, latent class cluster analysis and cluster analysis, are used to identify groups of child behavioral adjustment underlying a sample of elementary school children aged 6 to 11 years. Behavioral rating information across 14 subscales was obtained from classroom teachers and used as input for analyses. Both the procedures…

Hemodynamic Response to Interictal Epileptiform Discharges Addressed by Personalized EEG-fNIRS Recordings

PubMed Central

Pellegrino, Giovanni; Machado, Alexis; von Ellenrieder, Nicolas; Watanabe, Satsuki; Hall, Jeffery A.; Lina, Jean-Marc; Kobayashi, Eliane; Grova, Christophe

2016-01-01

Objective: We aimed at studying the hemodynamic response (HR) to Interictal Epileptic Discharges (IEDs) using patient-specific and prolonged simultaneous ElectroEncephaloGraphy (EEG) and functional Near InfraRed Spectroscopy (fNIRS) recordings. Methods: The epileptic generator was localized using Magnetoencephalography source imaging. fNIRS montage was tailored for each patient, using an algorithm to optimize the sensitivity to the epileptic generator. Optodes were glued using collodion to achieve prolonged acquisition with high quality signal. fNIRS data analysis was handled with no a priori constraint on HR time course, averaging fNIRS signals to similar IEDs. Cluster-permutation analysis was performed on 3D reconstructed fNIRS data to identify significant spatio-temporal HR clusters. Standard (GLM with fixed HRF) and cluster-permutation EEG-fMRI analyses were performed for comparison purposes. Results: fNIRS detected HR to IEDs for 8/9 patients. It mainly consisted oxy-hemoglobin increases (seven patients), followed by oxy-hemoglobin decreases (six patients). HR was lateralized in six patients and lasted from 8.5 to 30 s. Standard EEG-fMRI analysis detected an HR in 4/9 patients (4/9 without enough IEDs, 1/9 unreliable result). The cluster-permutation EEG-fMRI analysis restricted to the region investigated by fNIRS showed additional strong and non-canonical BOLD responses starting earlier than the IEDs and lasting up to 30 s. Conclusions: (i) EEG-fNIRS is suitable to detect the HR to IEDs and can outperform EEG-fMRI because of prolonged recordings and greater chance to detect IEDs; (ii) cluster-permutation analysis unveils additional HR features underestimated when imposing a canonical HR function (iii) the HR is often bilateral and lasts up to 30 s. PMID:27047325

Spatial and temporal changes in household structure locations using high-resolution satellite imagery for population assessment: an analysis in southern Zambia, 2006-2011.

PubMed

Shields, Timothy; Pinchoff, Jessie; Lubinda, Jailos; Hamapumbu, Harry; Searle, Kelly; Kobayashi, Tamaki; Thuma, Philip E; Moss, William J; Curriero, Frank C

2016-05-31

Satellite imagery is increasingly available at high spatial resolution and can be used for various purposes in public health research and programme implementation. Comparing a census generated from two satellite images of the same region in rural southern Zambia obtained four and a half years apart identified patterns of household locations and change over time. The length of time that a satellite image-based census is accurate determines its utility. Households were enumerated manually from satellite images obtained in 2006 and 2011 of the same area. Spatial statistics were used to describe clustering, cluster detection, and spatial variation in the location of households. A total of 3821 household locations were enumerated in 2006 and 4256 in 2011, a net change of 435 houses (11.4% increase). Comparison of the images indicated that 971 (25.4%) structures were added and 536 (14.0%) removed. Further analysis suggested similar household clustering in the two images and no substantial difference in concentration of households across the study area. Cluster detection analysis identified a small area where significantly more household structures were removed than expected; however, the amount of change was of limited practical significance. These findings suggest that random sampling of households for study participation would not induce geographic bias if based on a 4.5-year-old image in this region. Application of spatial statistical methods provides insights into the population distribution changes between two time periods and can be helpful in assessing the accuracy of satellite imagery.

20 Years Spatial-Temporal Analysis of Dengue Fever and Hemorrhagic Fever in Mexico.

PubMed

Hernández-Gaytán, Sendy Isarel; Díaz-Vásquez, Francisco Javier; Duran-Arenas, Luis Gerardo; López Cervantes, Malaquías; Rothenberg, Stephen J

2017-10-01

Dengue Fever (DF) is a human vector-borne disease and a major public health problem worldwide. In Mexico, DF and Dengue Hemorrhagic Fever (DHF) cases have increased in recent years. The aim of this study was to identify variations in the spatial distribution of DF and DHF cases over time using space-time statistical analysis and geographic information systems. Official data of DF and DHF cases were obtained in 32 states from 1995-2015. Space-time scan statistics were used to determine the space-time clusters of DF and DHF cases nationwide, and a geographic information system was used to display the location of clusters. A total of 885,748 DF cases was registered of which 13.4% (n = 119,174) correspond to DHF in the 32 states from 1995-2015. The most likely cluster of DF (relative risk = 25.5) contained the states of Jalisco, Colima, and Nayarit, on the Pacific coast in 2009, and the most likely cluster of DHF (relative risk = 8.5) was in the states of Chiapas, Tabasco, Campeche, Oaxaca, Veracruz, Quintana Roo, Yucatán, Puebla, Morelos, and Guerrero principally on the Gulf coast over 2006-2015. The geographic distribution of DF and DHF cases has increased in recent years and cases are significantly clustered in two coastal areas (Pacific and Gulf of Mexico). This provides the basis for further investigation of risk factors as well as interventions in specific areas. Copyright © 2018 IMSS. Published by Elsevier Inc. All rights reserved.

Cluster analysis in phenotyping a Portuguese population.

PubMed

Loureiro, C C; Sa-Couto, P; Todo-Bom, A; Bousquet, J

2015-09-03

Unbiased cluster analysis using clinical parameters has identified asthma phenotypes. Adding inflammatory biomarkers to this analysis provided a better insight into the disease mechanisms. This approach has not yet been applied to asthmatic Portuguese patients. To identify phenotypes of asthma using cluster analysis in a Portuguese asthmatic population treated in secondary medical care. Consecutive patients with asthma were recruited from the outpatient clinic. Patients were optimally treated according to GINA guidelines and enrolled in the study. Procedures were performed according to a standard evaluation of asthma. Phenotypes were identified by cluster analysis using Ward's clustering method. Of the 72 patients enrolled, 57 had full data and were included for cluster analysis. Distribution was set in 5 clusters described as follows: cluster (C) 1, early onset mild allergic asthma; C2, moderate allergic asthma, with long evolution, female prevalence and mixed inflammation; C3, allergic brittle asthma in young females with early disease onset and no evidence of inflammation; C4, severe asthma in obese females with late disease onset, highly symptomatic despite low Th2 inflammation; C5, severe asthma with chronic airflow obstruction, late disease onset and eosinophilic inflammation. In our study population, the identified clusters were mainly coincident with other larger-scale cluster analysis. Variables such as age at disease onset, obesity, lung function, FeNO (Th2 biomarker) and disease severity were important for cluster distinction. Copyright © 2015. Published by Elsevier España, S.L.U.

Modem Signature Analysis.

DTIC Science & Technology

1982-10-01

AD-A127 993 MODEM SIGNATURE ANALISIS (U) PAR TECHNOLOGY CORP NEW / HARTFORD NY V EDWARDS ET AL. OCT 82 RADC-TR-82-269 F30602-80-C-0264 NCLASSIFIED F/G...as an indication of the class clustering and separation between different classes in the modem data base. It is apparent from the projection that the...that as the clusters disperse, the likelihood of a sample crossing the boundary into an adjacent region and causing a symbol decision error increases. As

Phenotypes Determined by Cluster Analysis in Moderate to Severe Bronchial Asthma.

PubMed

Youroukova, Vania M; Dimitrova, Denitsa G; Valerieva, Anna D; Lesichkova, Spaska S; Velikova, Tsvetelina V; Ivanova-Todorova, Ekaterina I; Tumangelova-Yuzeir, Kalina D

2017-06-01

Bronchial asthma is a heterogeneous disease that includes various subtypes. They may share similar clinical characteristics, but probably have different pathological mechanisms. To identify phenotypes using cluster analysis in moderate to severe bronchial asthma and to compare differences in clinical, physiological, immunological and inflammatory data between the clusters. Forty adult patients with moderate to severe bronchial asthma out of exacerbation were included. All underwent clinical assessment, anthropometric measurements, skin prick testing, standard spirometry and measurement fraction of exhaled nitric oxide. Blood eosinophilic count, serum total IgE and periostin levels were determined. Two-step cluster approach, hierarchical clustering method and k-mean analysis were used for identification of the clusters. We have identified four clusters. Cluster 1 (n=14) - late-onset, non-atopic asthma with impaired lung function, Cluster 2 (n=13) - late-onset, atopic asthma, Cluster 3 (n=6) - late-onset, aspirin sensitivity, eosinophilic asthma, and Cluster 4 (n=7) - early-onset, atopic asthma. Our study is the first in Bulgaria in which cluster analysis is applied to asthmatic patients. We identified four clusters. The variables with greatest force for differentiation in our study were: age of asthma onset, duration of diseases, atopy, smoking, blood eosinophils, nonsteroidal anti-inflammatory drugs hypersensitivity, baseline FEV1/FVC and symptoms severity. Our results support the concept of heterogeneity of bronchial asthma and demonstrate that cluster analysis can be an useful tool for phenotyping of disease and personalized approach to the treatment of patients.

Detecting dominant motion patterns in crowds of pedestrians

NASA Astrophysics Data System (ADS)

Saqib, Muhammad; Khan, Sultan Daud; Blumenstein, Michael

2017-02-01

As the population of the world increases, urbanization generates crowding situations which poses challenges to public safety and security. Manual analysis of crowded situations is a tedious job and usually prone to errors. In this paper, we propose a novel technique of crowd analysis, the aim of which is to detect different dominant motion patterns in real-time videos. A motion field is generated by computing the dense optical flow. The motion field is then divided into blocks. For each block, we adopt an Intra-clustering algorithm for detecting different flows within the block. Later on, we employ Inter-clustering for clustering the flow vectors among different blocks. We evaluate the performance of our approach on different real-time videos. The experimental results show that our proposed method is capable of detecting distinct motion patterns in crowded videos. Moreover, our algorithm outperforms state-of-the-art methods.

Soft-landing ion mobility of silver clusters for small-molecule matrix-assisted laser desorption ionization mass spectrometry and imaging of latent fingerprints.

PubMed

Walton, Barbara L; Verbeck, Guido F

2014-08-19

Matrix-assisted laser desorption ionization (MALDI) imaging is gaining popularity, but matrix effects such as mass spectral interference and damage to the sample limit its applications. Replacing traditional matrices with silver particles capable of equivalent or increased photon energy absorption from the incoming laser has proven to be beneficial for low mass analysis. Not only can silver clusters be advantageous for low mass compound detection, but they can be used for imaging as well. Conventional matrix application methods can obstruct samples, such as fingerprints, rendering them useless after mass analysis. The ability to image latent fingerprints without causing damage to the ridge pattern is important as it allows for further characterization of the print. The application of silver clusters by soft-landing ion mobility allows for enhanced MALDI and preservation of fingerprint integrity.

Cross-scale analysis of cluster correspondence using different operational neighborhoods

NASA Astrophysics Data System (ADS)

Lu, Yongmei; Thill, Jean-Claude

2008-09-01

Cluster correspondence analysis examines the spatial autocorrelation of multi-location events at the local scale. This paper argues that patterns of cluster correspondence are highly sensitive to the definition of operational neighborhoods that form the spatial units of analysis. A subset of multi-location events is examined for cluster correspondence if they are associated with the same operational neighborhood. This paper discusses the construction of operational neighborhoods for cluster correspondence analysis based on the spatial properties of the underlying zoning system and the scales at which the zones are aggregated into neighborhoods. Impacts of this construction on the degree of cluster correspondence are also analyzed. Empirical analyses of cluster correspondence between paired vehicle theft and recovery locations are conducted on different zoning methods and across a series of geographic scales and the dynamics of cluster correspondence patterns are discussed.

Resemblance profiles as clustering decision criteria: Estimating statistical power, error, and correspondence for a hypothesis test for multivariate structure.

PubMed

Kilborn, Joshua P; Jones, David L; Peebles, Ernst B; Naar, David F

2017-04-01

Clustering data continues to be a highly active area of data analysis, and resemblance profiles are being incorporated into ecological methodologies as a hypothesis testing-based approach to clustering multivariate data. However, these new clustering techniques have not been rigorously tested to determine the performance variability based on the algorithm's assumptions or any underlying data structures. Here, we use simulation studies to estimate the statistical error rates for the hypothesis test for multivariate structure based on dissimilarity profiles (DISPROF). We concurrently tested a widely used algorithm that employs the unweighted pair group method with arithmetic mean (UPGMA) to estimate the proficiency of clustering with DISPROF as a decision criterion. We simulated unstructured multivariate data from different probability distributions with increasing numbers of objects and descriptors, and grouped data with increasing overlap, overdispersion for ecological data, and correlation among descriptors within groups. Using simulated data, we measured the resolution and correspondence of clustering solutions achieved by DISPROF with UPGMA against the reference grouping partitions used to simulate the structured test datasets. Our results highlight the dynamic interactions between dataset dimensionality, group overlap, and the properties of the descriptors within a group (i.e., overdispersion or correlation structure) that are relevant to resemblance profiles as a clustering criterion for multivariate data. These methods are particularly useful for multivariate ecological datasets that benefit from distance-based statistical analyses. We propose guidelines for using DISPROF as a clustering decision tool that will help future users avoid potential pitfalls during the application of methods and the interpretation of results.

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

NASA Astrophysics Data System (ADS)

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

2014-04-01

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

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

The Impact of Clinical, Demographic and Risk Factors on Rates of HIV Transmission: A Population-based Phylogenetic Analysis in British Columbia, Canada

PubMed Central

Poon, Art F. Y.; Joy, Jeffrey B.; Woods, Conan K.; Shurgold, Susan; Colley, Guillaume; Brumme, Chanson J.; Hogg, Robert S.; Montaner, Julio S. G.; Harrigan, P. Richard

2015-01-01

Background. The diversification of human immunodeficiency virus (HIV) is shaped by its transmission history. We therefore used a population based province wide HIV drug resistance database in British Columbia (BC), Canada, to evaluate the impact of clinical, demographic, and behavioral factors on rates of HIV transmission. Methods. We reconstructed molecular phylogenies from 27 296 anonymized bulk HIV pol sequences representing 7747 individuals in BC—about half the estimated HIV prevalence in BC. Infections were grouped into clusters based on phylogenetic distances, as a proxy for variation in transmission rates. Rates of cluster expansion were reconstructed from estimated dates of HIV seroconversion. Results. Our criteria grouped 4431 individuals into 744 clusters largely separated with respect to risk factors, including large established clusters predominated by injection drug users and more-recently emerging clusters comprising men who have sex with men. The mean log10 viral load of an individual's phylogenetic neighborhood (composed of 5 other individuals with shortest phylogenetic distances) increased their odds of appearing in a cluster by >2-fold per log10 viruses per milliliter. Conclusions. Hotspots of ongoing HIV transmission can be characterized in near real time by the secondary analysis of HIV resistance genotypes, providing an important potential resource for targeting public health initiatives for HIV prevention. PMID:25312037

The link between parental allergy and offspring allergic and nonallergic rhinitis.

PubMed

Westman, M; Kull, I; Lind, T; Melén, E; Stjärne, P; Toskala, E; Wickman, M; Bergström, A

2013-12-01

Parental allergy-related disease increases the risk for rhinitis, but it remains unknown how different phenotypes of parental allergy affect this risk. The aim of this study was to investigate how parental hay fever, asthma, and eczema affect the risk of allergic rhinitis (AR) and nonallergic rhinitis (NAR) at 8 years of age. Information on 2413 children from a population-based birth cohort was used combining questionnaire data and IgE to inhalant allergens. Logistic regression was used to estimate the association between parental allergy-related disease and AR and NAR. In addition, cluster analysis was used to search for latent phenotypes of heredity likely to be associated with AR and NAR. At age 8 years, 13.8% of the children had AR, while 6.4% had NAR. Parental isolated hay fever increased the odds of AR (OR 2.2, 95% CI 1.6-3.2), whereas isolated asthma or eczema did not. The odds of NAR increased when one parent had two or more allergy-related diseases. In the cluster analysis, the highest proportion of AR, 37.5%, was seen in a cluster where both parents had hay fever and pollen allergy and that of NAR, 11.0%, in a cluster where one parent had hay fever, pollen allergy, and eczema. Parental allergy-related disease may be an important risk factor for NAR as well as AR, and the risk is comparable for maternal and paternal allergy. Parental hay fever seems to be the dominating hereditary risk factor for AR. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Spatiotemporal Analysis of the Malaria Epidemic in Mainland China, 2004-2014.

PubMed

Huang, Qiang; Hu, Lin; Liao, Qi-Bin; Xia, Jing; Wang, Qian-Ru; Peng, Hong-Juan

2017-08-01

The purpose of this study is to characterize spatiotemporal heterogeneities in malaria distribution at a provincial level and investigate the association between malaria incidence and climate factors from 2004 to 2014 in China to inform current malaria control efforts. National malaria incidence peaked (4.6/100,000) in 2006 and decreased to a very low level (0.21/100,000) in 2014, and the proportion of imported cases increased from 16.2% in 2004 to 98.2% in 2014. Statistical analyses of global and local spatial autocorrelations and purely spatial scan statistics revealed that malaria was localized in Hainan, Anhui, and Yunnan during 2004-2009 and then gradually shifted and clustered in Yunnan after 2010. Purely temporal clusters shortened to less than 5 months during 2012-2014. The two most likely clusters detected using spatiotemporal analysis occurred in Anhui between July 2005 and November 2007 and Yunnan between January 2010 and June 2012. Correlation coefficients for the association between malaria incidence and climate factors sharply decreased after 2010, and there were zero-month lag effects for climate factors during 2010-2014. Overall, the spatiotemporal distribution of malaria in China changed from relatively scattered (2004-2009) to relatively clustered (2010-2014). As the proportion of imported cases increased, the effect of climate factors on malaria incidence has gradually become weaker since 2011. Therefore, new warning systems should be applied to monitor resurgence and outbreaks of malaria in mainland China, and quarantine at borders should be reinforced to control the increasingly trend of imported malaria cases.

Antioxidant properties of different edible mushroom species and increased bioconversion efficiency of Pleurotus eryngii using locally available casing materials.

PubMed

Mishra, K K; Pal, R S; Arunkumar, R; Chandrashekara, C; Jain, S K; Bhatt, J C

2013-06-01

Total phenolics, radical scavenging activity (RSA) on DPPH, ascorbic acid content and chelating activity on Fe(2+) of Pleurotus citrinopileatus, Pleurotus djamor, Pleurotus eryngii, Pleurotus flabellatus, Pleurotus florida, Pleurotus ostreatus, Pleurotus sajor-caju and Hypsizygus ulmarius have been evaluated. The assayed mushrooms contained 3.94-21.67 mg TAE of phenolics, 13.63-69.67% DPPH scavenging activity, 3.76-6.76 mg ascorbic acid and 60.25-82.7% chelating activity. Principal Component Analysis (PCA) revealed that significantly higher total phenolics, RSA on DPPH and growth/day was present in P. eryngii whereas P. citrinopileatus showed higher ascorbic acid and chelating activity. Agglomerative hierarchical clustering analysis revealed that studied mushroom species fall into two clusters; Cluster I included P. djamor, P. eryngii and P. flabellatus, while Cluster II included H. ulmarius, P. sajor-caju, P. citrinopileatus, P. ostreatus and P. florida. Enhanced yield of P. eryngii was achieved on spent compost casing material. Use of casing materials enhanced yield by 21-107% over non-cased substrate. Copyright © 2012 Elsevier Ltd. All rights reserved.

Spatial analysis for the epidemiological study of cardiovascular diseases: A systematic literature search.

PubMed

Mena, Carlos; Sepúlveda, Cesar; Fuentes, Eduardo; Ormazábal, Yony; Palomo, Iván

2018-05-07

Cardiovascular diseases (CVDs) are the primary cause of death and disability in de world, and the detection of populations at risk as well as localization of vulnerable areas is essential for adequate epidemiological management. Techniques developed for spatial analysis, among them geographical information systems and spatial statistics, such as cluster detection and spatial correlation, are useful for the study of the distribution of the CVDs. These techniques, enabling recognition of events at different geographical levels of study (e.g., rural, deprived neighbourhoods, etc.), make it possible to relate CVDs to factors present in the immediate environment. The systemic literature presented here shows that this group of diseases is clustered with regard to incidence, mortality and hospitalization as well as obesity, smoking, increased glycated haemoglobin levels, hypertension physical activity and age. In addition, acquired variables such as income, residency (rural or urban) and education, contribute to CVD clustering. Both local cluster detection and spatial regression techniques give statistical weight to the findings providing valuable information that can influence response mechanisms in the health services by indicating locations in need of intervention and assignment of available resources.

Aggregation Number in Water/n-Hexanol Molecular Clusters Formed in Cyclohexane at Different Water/n-Hexanol/Cyclohexane Compositions Calculated by Titration 1H NMR.

PubMed

Flores, Mario E; Shibue, Toshimichi; Sugimura, Natsuhiko; Nishide, Hiroyuki; Moreno-Villoslada, Ignacio

2017-11-09

Upon titration of n-hexanol/cyclohexane mixtures of different molar compositions with water, water/n-hexanol clusters are formed in cyclohexane. Here, we develop a new method to estimate the water and n-hexanol aggregation numbers in the clusters that combines integration analysis in one-dimensional 1 H NMR spectra, diffusion coefficients calculated by diffusion-ordered NMR spectroscopy, and further application of the Stokes-Einstein equation to calculate the hydrodynamic volume of the clusters. Aggregation numbers of 5-15 molecules of n-hexanol per cluster in the absence of water were observed in the whole range of n-hexanol/cyclohexane molar fractions studied. After saturation with water, aggregation numbers of 6-13 n-hexanol and 0.5-5 water molecules per cluster were found. O-H and O-O atom distances related to hydrogen bonds between donor/acceptor molecules were theoretically calculated using density functional theory. The results show that at low n-hexanol molar fractions, where a robust hydrogen-bond network is held between n-hexanol molecules, addition of water makes the intermolecular O-O atom distance shorter, reinforcing molecular association in the clusters, whereas at high n-hexanol molar fractions, where dipole-dipole interactions dominate, addition of water makes the intermolecular O-O atom distance longer, weakening the cluster structure. This correlates with experimental NMR results, which show an increase in the size and aggregation number in the clusters upon addition of water at low n-hexanol molar fractions, and a decrease of these magnitudes at high n-hexanol molar fractions. In addition, water produces an increase in the proton exchange rate between donor/acceptor molecules at all n-hexanol molar fractions.

Evaluation of Solute Clusters Associated with Bake-Hardening Response in Isothermal Aged Al-Mg-Si Alloys Using a Three-Dimensional Atom Probe

NASA Astrophysics Data System (ADS)

Aruga, Yasuhiro; Kozuka, Masaya; Takaki, Yasuo; Sato, Tatsuo

2014-12-01

Temporal changes in the number density, size distribution, and chemical composition of clusters formed during natural aging at room temperature and pre-aging at 363 K (90 °C) in an Al-0.62Mg-0.93Si (mass pct) alloy were evaluated using atom probe tomography. More than 10 million atoms were examined in the cluster analysis, in which about 1000 clusters were obtained for each material after various aging treatments. The statistically proven records show that both number density and the average radius of clusters in pre-aged materials are larger than in naturally aged materials. It was revealed that the fraction of clusters with a low Mg/Si ratio after natural aging for a short time is higher than with other aging treatments, regardless of cluster size. This indicates that Si-rich clusters form more easily after short-period natural aging, and that Mg atoms can diffuse into the clusters or possibly form another type of Mg-Si cluster after prolonged natural aging. The formation of large clusters with a uniform Mg/Si ratio is encouraged by pre-aging. It can be concluded that an increase of small clusters with various Mg/Si ratios does not promote the bake-hardening (BH) response, whereas large clusters with a uniform Mg/Si ratio play an important role in hardening during the BH treatment at 443 K (170 °C).

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

Modest validity and fair reproducibility of dietary patterns derived by cluster analysis.

PubMed

Funtikova, Anna N; Benítez-Arciniega, Alejandra A; Fitó, Montserrat; Schröder, Helmut

2015-03-01

Cluster analysis is widely used to analyze dietary patterns. We aimed to analyze the validity and reproducibility of the dietary patterns defined by cluster analysis derived from a food frequency questionnaire (FFQ). We hypothesized that the dietary patterns derived by cluster analysis have fair to modest reproducibility and validity. Dietary data were collected from 107 individuals from population-based survey, by an FFQ at baseline (FFQ1) and after 1 year (FFQ2), and by twelve 24-hour dietary recalls (24-HDR). Repeatability and validity were measured by comparing clusters obtained by the FFQ1 and FFQ2 and by the FFQ2 and 24-HDR (reference method), respectively. Cluster analysis identified a "fruits & vegetables" and a "meat" pattern in each dietary data source. Cluster membership was concordant for 66.7% of participants in FFQ1 and FFQ2 (reproducibility), and for 67.0% in FFQ2 and 24-HDR (validity). Spearman correlation analysis showed reasonable reproducibility, especially in the "fruits & vegetables" pattern, and lower validity also especially in the "fruits & vegetables" pattern. κ statistic revealed a fair validity and reproducibility of clusters. Our findings indicate a reasonable reproducibility and fair to modest validity of dietary patterns derived by cluster analysis. Copyright © 2015 Elsevier Inc. All rights reserved.

Cluster Analysis to Identify Possible Subgroups in Tinnitus Patients.

PubMed

van den Berge, Minke J C; Free, Rolien H; Arnold, Rosemarie; de Kleine, Emile; Hofman, Rutger; van Dijk, J Marc C; van Dijk, Pim

2017-01-01

In tinnitus treatment, there is a tendency to shift from a "one size fits all" to a more individual, patient-tailored approach. Insight in the heterogeneity of the tinnitus spectrum might improve the management of tinnitus patients in terms of choice of treatment and identification of patients with severe mental distress. The goal of this study was to identify subgroups in a large group of tinnitus patients. Data were collected from patients with severe tinnitus complaints visiting our tertiary referral tinnitus care group at the University Medical Center Groningen. Patient-reported and physician-reported variables were collected during their visit to our clinic. Cluster analyses were used to characterize subgroups. For the selection of the right variables to enter in the cluster analysis, two approaches were used: (1) variable reduction with principle component analysis and (2) variable selection based on expert opinion. Various variables of 1,783 tinnitus patients were included in the analyses. Cluster analysis (1) included 976 patients and resulted in a four-cluster solution. The effect of external influences was the most discriminative between the groups, or clusters, of patients. The "silhouette measure" of the cluster outcome was low (0.2), indicating a "no substantial" cluster structure. Cluster analysis (2) included 761 patients and resulted in a three-cluster solution, comparable to the first analysis. Again, a "no substantial" cluster structure was found (0.2). Two cluster analyses on a large database of tinnitus patients revealed that clusters of patients are mostly formed by a different response of external influences on their disease. However, both cluster outcomes based on this dataset showed a poor stability, suggesting that our tinnitus population comprises a continuum rather than a number of clearly defined subgroups.

Ecological tolerances of Miocene larger benthic foraminifera from Indonesia

NASA Astrophysics Data System (ADS)

Novak, Vibor; Renema, Willem

2018-01-01

To provide a comprehensive palaeoenvironmental reconstruction based on larger benthic foraminifera (LBF), a quantitative analysis of their assemblage composition is needed. Besides microfacies analysis which includes environmental preferences of foraminiferal taxa, statistical analyses should also be employed. Therefore, detrended correspondence analysis and cluster analysis were performed on relative abundance data of identified LBF assemblages deposited in mixed carbonate-siliciclastic (MCS) systems and blue-water (BW) settings. Studied MCS system localities include ten sections from the central part of the Kutai Basin in East Kalimantan, ranging from late Burdigalian to Serravallian age. The BW samples were collected from eleven sections of the Bulu Formation on Central Java, dated as Serravallian. Results from detrended correspondence analysis reveal significant differences between these two environmental settings. Cluster analysis produced five clusters of samples; clusters 1 and 2 comprise dominantly MCS samples, clusters 3 and 4 with dominance of BW samples, and cluster 5 showing a mixed composition with both MCS and BW samples. The results of cluster analysis were afterwards subjected to indicator species analysis resulting in the interpretation that generated three groups among LBF taxa: typical assemblage indicators, regularly occurring taxa and rare taxa. By interpreting the results of detrended correspondence analysis, cluster analysis and indicator species analysis, along with environmental preferences of identified LBF taxa, a palaeoenvironmental model is proposed for the distribution of LBF in Miocene MCS systems and adjacent BW settings of Indonesia.

Clustering, randomness, and regularity in cloud fields: 2. Cumulus cloud fields

NASA Astrophysics Data System (ADS)

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

1992-12-01

During the last decade a major controversy has been brewing concerning the proper characterization of cumulus convection. The prevailing view has been that cumulus clouds form in clusters, in which cloud spacing is closer than that found for the overall cloud field and which maintains its identity over many cloud lifetimes. This "mutual protection hypothesis" of Randall and Huffman (1980) has been challenged by the "inhibition hypothesis" of Ramirez et al. (1990) which strongly suggests that the spatial distribution of cumuli must tend toward a regular distribution. A dilemma has resulted because observations have been reported to support both hypotheses. The present work reports a detailed analysis of cumulus cloud field spatial distributions based upon Landsat, Advanced Very High Resolution Radiometer, and Skylab data. Both nearest-neighbor and point-to-cloud cumulative distribution function statistics are investigated. The results show unequivocally that when both large and small clouds are included in the cloud field distribution, the cloud field always has a strong clustering signal. The strength of clustering is largest at cloud diameters of about 200-300 m, diminishing with increasing cloud diameter. In many cases, clusters of small clouds are found which are not closely associated with large clouds. As the small clouds are eliminated from consideration, the cloud field typically tends towards regularity. Thus it would appear that the "inhibition hypothesis" of Ramirez and Bras (1990) has been verified for the large clouds. However, these results are based upon the analysis of point processes. A more exact analysis also is made which takes into account the cloud size distributions. Since distinct clouds are by definition nonoverlapping, cloud size effects place a restriction upon the possible locations of clouds in the cloud field. The net effect of this analysis is that the large clouds appear to be randomly distributed, with only weak tendencies towards regularity. For clouds less than 1 km in diameter, the average nearest-neighbor distance is equal to 3-7 cloud diameters. For larger clouds, the ratio of cloud nearest-neighbor distance to cloud diameter increases sharply with increasing cloud diameter. This demonstrates that large clouds inhibit the growth of other large clouds in their vicinity. Nevertheless, this leads to random distributions of large clouds, not regularity.

PANCHROMATIC HUBBLE ANDROMEDA TREASURY. XVI. STAR CLUSTER FORMATION EFFICIENCY AND THE CLUSTERED FRACTION OF YOUNG STARS

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

Johnson, L. Clifton; Sandstrom, Karin; Seth, Anil C.

We use the Panchromatic Hubble Andromeda Treasury survey data set to perform spatially resolved measurements of star cluster formation efficiency (Γ), the fraction of stellar mass formed in long-lived star clusters. We use robust star formation history and cluster parameter constraints, obtained through color–magnitude diagram analysis of resolved stellar populations, to study Andromeda’s cluster and field populations over the last ∼300 Myr. We measure Γ of 4%–8% for young, 10–100 Myr-old populations in M31. We find that cluster formation efficiency varies systematically across the M31 disk, consistent with variations in mid-plane pressure. These Γ measurements expand the range of well-studiedmore » galactic environments, providing precise constraints in an H i-dominated, low-intensity star formation environment. Spatially resolved results from M31 are broadly consistent with previous trends observed on galaxy-integrated scales, where Γ increases with increasing star formation rate surface density (Σ{sub SFR}). However, we can explain observed scatter in the relation and attain better agreement between observations and theoretical models if we account for environmental variations in gas depletion time ( τ {sub dep}) when modeling Γ, accounting for the qualitative shift in star formation behavior when transitioning from a H{sub 2}-dominated to a H i-dominated interstellar medium. We also demonstrate that Γ measurements in high Σ{sub SFR} starburst systems are well-explained by τ {sub dep}-dependent fiducial Γ models.« less

Identification of five chronic obstructive pulmonary disease subgroups with different prognoses in the ECLIPSE cohort using cluster analysis.

PubMed

Rennard, Stephen I; Locantore, Nicholas; Delafont, Bruno; Tal-Singer, Ruth; Silverman, Edwin K; Vestbo, Jørgen; Miller, Bruce E; Bakke, Per; Celli, Bartolomé; Calverley, Peter M A; Coxson, Harvey; Crim, Courtney; Edwards, Lisa D; Lomas, David A; MacNee, William; Wouters, Emiel F M; Yates, Julie C; Coca, Ignacio; Agustí, Alvar

2015-03-01

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease that likely includes clinically relevant subgroups. To identify subgroups of COPD in ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) subjects using cluster analysis and to assess clinically meaningful outcomes of the clusters during 3 years of longitudinal follow-up. Factor analysis was used to reduce 41 variables determined at recruitment in 2,164 patients with COPD to 13 main factors, and the variables with the highest loading were used for cluster analysis. Clusters were evaluated for their relationship with clinically meaningful outcomes during 3 years of follow-up. The relationships among clinical parameters were evaluated within clusters. Five subgroups were distinguished using cross-sectional clinical features. These groups differed regarding outcomes. Cluster A included patients with milder disease and had fewer deaths and hospitalizations. Cluster B had less systemic inflammation at baseline but had notable changes in health status and emphysema extent. Cluster C had many comorbidities, evidence of systemic inflammation, and the highest mortality. Cluster D had low FEV1, severe emphysema, and the highest exacerbation and COPD hospitalization rate. Cluster E was intermediate for most variables and may represent a mixed group that includes further clusters. The relationships among clinical variables within clusters differed from that in the entire COPD population. Cluster analysis using baseline data in ECLIPSE identified five COPD subgroups that differ in outcomes and inflammatory biomarkers and show different relationships between clinical parameters, suggesting the clusters represent clinically and biologically different subtypes of COPD.

The structure of geopolymers - Theoretical studies

NASA Astrophysics Data System (ADS)

Koleżyński, Andrzej; Król, Magdalena; Żychowicz, Mikołaj

2018-07-01

This work presents the results of DFT and classical mechanics' calculations and theoretical analysis of geopolymer structure. The calculations were carried out using a bottom-up approach (from small oligomers to clusters with increasing size) for various Si:Al ratio. For all model structures after geometry optimization, respective IR spectra were simulated and compared with the experimental ones. The obtained results show that the concordance of simulated spectra with the experiment, for a given Si:Al ratio, increases with the size of the cluster and increasing local order. Moreover, the increase of the level of local disorder (structure "openness") results in significant band splitting, not observable in real geopolymers. This suggest that, in the case of real geopolymeric structures one can expect the presence of reasonably big, ordered structural fragments, analogous to zeolites.

An image processing pipeline to detect and segment nuclei in muscle fiber microscopic images.

PubMed

Guo, Yanen; Xu, Xiaoyin; Wang, Yuanyuan; Wang, Yaming; Xia, Shunren; Yang, Zhong

2014-08-01

Muscle fiber images play an important role in the medical diagnosis and treatment of many muscular diseases. The number of nuclei in skeletal muscle fiber images is a key bio-marker of the diagnosis of muscular dystrophy. In nuclei segmentation one primary challenge is to correctly separate the clustered nuclei. In this article, we developed an image processing pipeline to automatically detect, segment, and analyze nuclei in microscopic image of muscle fibers. The pipeline consists of image pre-processing, identification of isolated nuclei, identification and segmentation of clustered nuclei, and quantitative analysis. Nuclei are initially extracted from background by using local Otsu's threshold. Based on analysis of morphological features of the isolated nuclei, including their areas, compactness, and major axis lengths, a Bayesian network is trained and applied to identify isolated nuclei from clustered nuclei and artifacts in all the images. Then a two-step refined watershed algorithm is applied to segment clustered nuclei. After segmentation, the nuclei can be quantified for statistical analysis. Comparing the segmented results with those of manual analysis and an existing technique, we find that our proposed image processing pipeline achieves good performance with high accuracy and precision. The presented image processing pipeline can therefore help biologists increase their throughput and objectivity in analyzing large numbers of nuclei in muscle fiber images. © 2014 Wiley Periodicals, Inc.

An orbital and electron density analysis of weak interactions in ethanol-water, methanol-water, ethanol and methanol small clusters.

PubMed

Mejía, Sol M; Flórez, Elizabeth; Mondragón, Fanor

2012-04-14

A computational study of (ethanol)(n)-water, n = 1 to 5 heteroclusters was carried out employing the B3LYP∕6-31+G(d) approach. The molecular (MO) and atomic (AO) orbital analysis and the topological study of the electron density provided results that were successfully correlated. Results were compared with those obtained for (ethanol)(n), (methanol)(n), n = 1 to 6 clusters and (methanol)(n)-water, n = 1 to 5 heteroclusters. These systems showed the same trends observed in the (ethanol)(n)-water, n = 1 to 5 heteroclusters such as an O---O distance of 5 Å to which the O-H---O hydrogen bonds (HBs) can have significant influence on the constituent monomers. The HOMO of the hetero(clusters) is less stable than the HOMO of the isolated alcohol monomer as the hetero(cluster) size increases, that destabilization is higher for linear geometries than for cyclic geometries. Changes of the occupancy and energy of the AO are correlated with the strength of O-H---O and C-H---O HBs as well as with the proton donor and/or acceptor character of the involved molecules. In summary, the current MO and AO analysis provides alternative ways to characterize HBs. However, this analysis cannot be applied to the study of H---H interactions observed in the molecular graphs.

An investigation on thermal patterns in Iran based on spatial autocorrelation

NASA Astrophysics Data System (ADS)

Fallah Ghalhari, Gholamabbas; Dadashi Roudbari, Abbasali

2018-02-01

The present study aimed at investigating temporal-spatial patterns and monthly patterns of temperature in Iran using new spatial statistical methods such as cluster and outlier analysis, and hotspot analysis. To do so, climatic parameters, monthly average temperature of 122 synoptic stations, were assessed. Statistical analysis showed that January with 120.75% had the most fluctuation among the studied months. Global Moran's Index revealed that yearly changes of temperature in Iran followed a strong spatially clustered pattern. Findings showed that the biggest thermal cluster pattern in Iran, 0.975388, occurred in May. Cluster and outlier analyses showed that thermal homogeneity in Iran decreases in cold months, while it increases in warm months. This is due to the radiation angle and synoptic systems which strongly influence thermal order in Iran. The elevations, however, have the most notable part proved by Geographically weighted regression model. Iran's thermal analysis through hotspot showed that hot thermal patterns (very hot, hot, and semi-hot) were dominant in the South, covering an area of 33.5% (about 552,145.3 km2). Regions such as mountain foot and low lands lack any significant spatial autocorrelation, 25.2% covering about 415,345.1 km2. The last is the cold thermal area (very cold, cold, and semi-cold) with about 25.2% covering about 552,145.3 km2 of the whole area of Iran.

Somatotyping using 3D anthropometry: a cluster analysis.

PubMed

Olds, Tim; Daniell, Nathan; Petkov, John; David Stewart, Arthur

2013-01-01

Somatotyping is the quantification of human body shape, independent of body size. Hitherto, somatotyping (including the most popular method, the Heath-Carter system) has been based on subjective visual ratings, sometimes supported by surface anthropometry. This study used data derived from three-dimensional (3D) whole-body scans as inputs for cluster analysis to objectively derive clusters of similar body shapes. Twenty-nine dimensions normalised for body size were measured on a purposive sample of 301 adults aged 17-56 years who had been scanned using a Vitus Smart laser scanner. K-means Cluster Analysis with v-fold cross-validation was used to determine shape clusters. Three male and three female clusters emerged, and were visualised using those scans closest to the cluster centroid and a caricature defined by doubling the difference between the average scan and the cluster centroid. The male clusters were decidedly endomorphic (high fatness), ectomorphic (high linearity), and endo-mesomorphic (a mixture of fatness and muscularity). The female clusters were clearly endomorphic, ectomorphic, and the ecto-mesomorphic (a mixture of linearity and muscularity). An objective shape quantification procedure combining 3D scanning and cluster analysis yielded shape clusters strikingly similar to traditional somatotyping.

Clusters of Occupations Based on Systematically Derived Work Dimensions: An Exploratory Study.

ERIC Educational Resources Information Center

Cunningham, J. W.; And Others

The study explored the feasibility of deriving an educationally relevant occupational cluster structure based on Occupational Analysis Inventory (OAI) work dimensions. A hierarchical cluster analysis was applied to the factor score profiles of 814 occupations on 22 higher-order OAI work dimensions. From that analysis, 73 occupational clusters were…

Using cluster analysis to identify phenotypes and validation of mortality in men with COPD.

PubMed

Chen, Chiung-Zuei; Wang, Liang-Yi; Ou, Chih-Ying; Lee, Cheng-Hung; Lin, Chien-Chung; Hsiue, Tzuen-Ren

2014-12-01

Cluster analysis has been proposed to examine phenotypic heterogeneity in chronic obstructive pulmonary disease (COPD). The aim of this study was to use cluster analysis to define COPD phenotypes and validate them by assessing their relationship with mortality. Male subjects with COPD were recruited to identify and validate COPD phenotypes. Seven variables were assessed for their relevance to COPD, age, FEV(1) % predicted, BMI, history of severe exacerbations, mMRC, SpO(2), and Charlson index. COPD groups were identified by cluster analysis and validated prospectively against mortality during a 4-year follow-up. Analysis of 332 COPD subjects identified five clusters from cluster A to cluster E. Assessment of the predictive validity of these clusters of COPD showed that cluster E patients had higher all cause mortality (HR 18.3, p < 0.0001), and respiratory cause mortality (HR 21.5, p < 0.0001) than those in the other four groups. Cluster E patients also had higher all cause mortality (HR 14.3, p = 0.0002) and respiratory cause mortality (HR 10.1, p = 0.0013) than patients in cluster D alone. COPD patient with severe airflow limitation, many symptoms, and a history of frequent severe exacerbations was a novel and distinct clinical phenotype predicting mortality in men with COPD.

Students' Changing Attitudes and Aspirations Towards Physics During Secondary School

NASA Astrophysics Data System (ADS)

Sheldrake, Richard; Mujtaba, Tamjid; Reiss, Michael J.

2017-11-01

Many countries desire more students to study science subjects, although relatively few students decide to study non-compulsory physics at upper-secondary school and at university. To gain insight into students' intentions to study non-compulsory physics, a longitudinal sample (covering 2258 students across 88 secondary schools in England) was surveyed in year 8 (age 12/13) and again in year 10 (age 14/15). Predictive modelling highlighted that perceived advice, perceived utility of physics, interest in physics, self-concept beliefs (students' subjective beliefs of their current abilities and performance) and home support specifically orientated to physics were key predictors of students' intentions. Latent-transition analysis via Markov models revealed clusters of students, given these factors at years 8 and 10. Students' intentions varied across the clusters, and at year 10 even varied when accounting for the students' underlying attitudes and beliefs, highlighting that considering clusters offered additional explanatory power and insight. Regardless of whether three-cluster, four-cluster, or five-cluster models were considered, the majority of students remained in the same cluster over time; for those who transitioned clusters, more students changed clusters reflecting an increase in attitudes than changed clusters reflecting a decrease. Students in the cluster with the most positive attitudes were most likely to remain within that cluster, while students in clusters with less positive attitudes were more likely to change clusters. Overall, the cluster profiles highlighted that students' attitudes and beliefs may be more closely related than previously assumed, but that changes in their attitudes and beliefs were indeed possible.

Sustainable Development in Indian Automotive Component Clusters

NASA Astrophysics Data System (ADS)

Bhaskaran, E.

2013-01-01

India is the world's second fastest growing auto market and boasts of the sixth largest automobile industry after China, the US, Germany, Japan and Brazil. The Indian auto component industry recorded its highest year-on-year growth of 34.2 % in 2010-2011, raking in revenue of US 39.9 billion; major contribution coming from exports at US five billion and fresh investment from the US at around US two billion. For inclusive growth and sustainable development most of the auto components manufacturers has adopted the cluster development approach. The objective is to study the technical efficiency (θ), peer weights (λ i ), input slacks (S-) and output slacks (S+) of four Auto Component Clusters (ACC) in India. The methodology adopted is using Data Envelopment Analysis of Input Oriented Banker Charnes Cooper Model by taking number of units and number of employments as inputs and sales and exports in crores as an outputs. The non-zero λ i 's represents the weights for efficient clusters. The S > 0 obtained for one ACC reveals the excess no. of units (S-) and employment (S-) and shortage in sales (S+) and exports (S+). However the variable returns to scale are increasing for three clusters, constant for one more cluster and with nil decrease. To conclude, for inclusive growth and sustainable development, the inefficient ACC should increase their turnover and exports, as decrease in no. of enterprises and employment is practically not possible. Moreover for sustainable development, the ACC should strengthen infrastructure interrelationships, technology interrelationships, procurement interrelationships, production interrelationships and marketing interrelationships to increase productivity and efficiency to compete in the world market.

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

PubMed Central

Borri, Marco; Schmidt, Maria A.; Powell, Ceri; Koh, Dow-Mu; Riddell, Angela M.; Partridge, Mike; Bhide, Shreerang A.; Nutting, Christopher M.; Harrington, Kevin J.; Newbold, Katie L.; Leach, Martin O.

2015-01-01

Purpose To describe a methodology, based on cluster analysis, to partition multi-parametric functional imaging data into groups (or clusters) of similar functional characteristics, with the aim of characterizing functional heterogeneity within head and neck tumour volumes. To evaluate the performance of the proposed approach on a set of longitudinal MRI data, analysing the evolution of the obtained sub-sets with treatment. Material and Methods The cluster analysis workflow was applied to a combination of dynamic contrast-enhanced and diffusion-weighted imaging MRI data from a cohort of squamous cell carcinoma of the head and neck patients. Cumulative distributions of voxels, containing pre and post-treatment data and including both primary tumours and lymph nodes, were partitioned into k clusters (k = 2, 3 or 4). Principal component analysis and cluster validation were employed to investigate data composition and to independently determine the optimal number of clusters. The evolution of the resulting sub-regions with induction chemotherapy treatment was assessed relative to the number of clusters. Results The clustering algorithm was able to separate clusters which significantly reduced in voxel number following induction chemotherapy from clusters with a non-significant reduction. Partitioning with the optimal number of clusters (k = 4), determined with cluster validation, produced the best separation between reducing and non-reducing clusters. Conclusion The proposed methodology was able to identify tumour sub-regions with distinct functional properties, independently separating clusters which were affected differently by treatment. This work demonstrates that unsupervised cluster analysis, with no prior knowledge of the data, can be employed to provide a multi-parametric characterization of functional heterogeneity within tumour volumes. PMID:26398888

Structure and substructure analysis of DAFT/FADA galaxy clusters in the [0.4-0.9] redshift range

NASA Astrophysics Data System (ADS)

Guennou, L.; Adami, C.; Durret, F.; Lima Neto, G. B.; Ulmer, M. P.; Clowe, D.; LeBrun, V.; Martinet, N.; Allam, S.; Annis, J.; Basa, S.; Benoist, C.; Biviano, A.; Cappi, A.; Cypriano, E. S.; Gavazzi, R.; Halliday, C.; Ilbert, O.; Jullo, E.; Just, D.; Limousin, M.; Márquez, I.; Mazure, A.; Murphy, K. J.; Plana, H.; Rostagni, F.; Russeil, D.; Schirmer, M.; Slezak, E.; Tucker, D.; Zaritsky, D.; Ziegler, B.

2014-01-01

Context. The DAFT/FADA survey is based on the study of ~90 rich (masses found in the literature >2 × 1014 M⊙) and moderately distant clusters (redshifts 0.4 < z < 0.9), all with HST imaging data available. This survey has two main objectives: to constrain dark energy (DE) using weak lensing tomography on galaxy clusters and to build a database (deep multi-band imaging allowing photometric redshift estimates, spectroscopic data, X-ray data) of rich distant clusters to study their properties. Aims: We analyse the structures of all the clusters in the DAFT/FADA survey for which XMM-Newton and/or a sufficient number of galaxy redshifts in the cluster range are available, with the aim of detecting substructures and evidence for merging events. These properties are discussed in the framework of standard cold dark matter (ΛCDM) cosmology. Methods: In X-rays, we analysed the XMM-Newton data available, fit a β-model, and subtracted it to identify residuals. We used Chandra data, when available, to identify point sources. In the optical, we applied a Serna & Gerbal (SG) analysis to clusters with at least 15 spectroscopic galaxy redshifts available in the cluster range. We discuss the substructure detection efficiencies of both methods. Results: XMM-Newton data were available for 32 clusters, for which we derive the X-ray luminosity and a global X-ray temperature for 25 of them. For 23 clusters we were able to fit the X-ray emissivity with a β-model and subtract it to detect substructures in the X-ray gas. A dynamical analysis based on the SG method was applied to the clusters having at least 15 spectroscopic galaxy redshifts in the cluster range: 18 X-ray clusters and 11 clusters with no X-ray data. The choice of a minimum number of 15 redshifts implies that only major substructures will be detected. Ten substructures were detected both in X-rays and by the SG method. Most of the substructures detected both in X-rays and with the SG method are probably at their first cluster pericentre approach and are relatively recent infalls. We also find hints of a decreasing X-ray gas density profile core radius with redshift. Conclusions: The percentage of mass included in substructures was found to be roughly constant with redshift values of 5-15%, in agreement both with the general CDM framework and with the results of numerical simulations. Galaxies in substructures show the same general behaviour as regular cluster galaxies; however, in substructures, there is a deficiency of both late type and old stellar population galaxies. Late type galaxies with recent bursts of star formation seem to be missing in the substructures close to the bottom of the host cluster potential well. However, our sample would need to be increased to allow a more robust analysis. Tables 1, 2, 4 and Appendices A-C are available in electronic form at http://www.aanda.org

Which nets are being used: factors associated with mosquito net use in Amhara, Oromia and Southern Nations, Nationalities and Peoples' Regions of Ethiopia.

PubMed

Ngondi, Jeremiah M; Graves, Patricia M; Gebre, Teshome; Mosher, Aryc W; Shargie, Estifanos B; Emerson, Paul M; Richards, Frank O

2011-04-17

There has been recent large scale-up of malaria control interventions in Ethiopia where transmission is unstable. While household ownership of long-lasting insecticidal nets (LLIN) has increased greatly, there are concerns about inadequate net use. This study aimed to investigate factors associated with net use at two time points, before and after mass distribution of nets. Two cross sectional surveys were carried out in 2006 and 2007 in Amhara, Oromia and SNNP regions. The latter was a sub-sample of the national Malaria Indicator Survey (MIS 3R). Each survey wave used multi-stage cluster random sampling with 25 households per cluster (224 clusters with 5,730 households in Baseline 2006 and 245 clusters with 5,910 households in MIS 3R 2007). Net ownership was assessed by visual inspection while net utilization was reported as use of the net the previous night. This net level analysis was restricted to households owning at least one net of any type. Logistic regression models of association between net use and explanatory variables including net type, age, condition, cost and other household characteristics were undertaken using generalized linear latent and mixed models (GLLAMM). A total of 3,784 nets in 2,430 households were included in the baseline 2006 analysis while the MIS 3R 2007 analysis comprised 5,413 nets in 3,328 households. The proportion of nets used the previous night decreased from 85.1% to 56.0% between baseline 2006 and MIS 3R 2007, respectively. Factors independently associated with increased proportion of nets used were: LLIN net type (at baseline 2006); indoor residual spraying (at MIS 3R 2007); and increasing wealth index at both surveys. At both baseline 2006 and MIS 3R 2007, reduced proportion of nets used was independently associated with increasing net age, increasing damage of nets, increasing household net density, and increasing altitude (>2,000 m). This study identified modifiable factors affecting use of nets that were consistent across both surveys. While net replacement remains important, the findings suggest that: more education about use and care of nets; making nets more resistant to damage; and encouraging net mending are likely to maximize the huge investment in scale up of net ownership by ensuring they are used. Without this step, the widespread benefits of LLIN cannot be realized.

The Swift AGN and Cluster Survey

NASA Astrophysics Data System (ADS)

Dai, Xinyu

A key question in astrophysics is to constrain the evolution of the largest gravitationally bound structures in the universe. The serendipitous observations of Swift-XRT form an excellent medium-deep and wide soft X-ray survey, with a sky area of 160 square degrees at the flux limit of 5e-15 erg/s/cm^2. This survey is about an order of magnitude deeper than previous surveys of similar areas, and an order of magnitude wider than previous surveys of similar depth. It is comparable to the planned eROSITA deep survey, but already with the data several years ahead. The unique combination of the survey area and depth enables it to fill in the gap between the deep, pencil beam surveys (such as the Chandra Deep Fields) and the shallow, wide area surveys measured with ROSAT. With it, we will place independent and complementary measurements on the number counts and luminosity functions of X-ray sources. It has been proved that this survey is excellent for X-ray selected galaxy cluster surveys, based on our initial analysis of 1/4 of the fields and other independent studies. The highest priority goal is to produce the largest, uniformly selected catalog of X-ray selected clusters and increase the sample of intermediate to high redshift clusters (z > 0.5) by an order of magnitude. From this catalog, we will study the evolution of cluster number counts, luminosity function, scaling relations, and eventually the mass function. For example, various smaller scale surveys concluded divergently on the evolution of a key scaling relation, between temperature and luminosity of clusters. With the statistical power from this large sample, we will resolve the debate whether clusters evolve self-similarly. This is a crucial step in mapping cluster evolution and constraining cosmological models. First, we propose to extract the complete serendipitous extended source list for all Swift-XRT data to 2015. Second, we will use optical/IR observations to further identify galaxy clusters. These optical/IR observations include data from the SDSS, WISE, and deep optical follow-up observations from the APO, MDM, Magellan, and NOAO telescopes. WISE will confirm all z0.5 clusters. We will use ground-based observations to measure redshifts for z>0.5 clusters, with a focus of measuring 1/10 of the spectroscopic redshifts of z>0.5 clusters within the budget period. Third, we will analyze our deep Suzaku Xray follow-up observations of a sample of medium redshift clusters, and the 1/10 bright Swift clusters suitable for spectral analysis. We will also perform stacking analysis using the Swift data for clusters in different redshift bins to constrain the evolution of cluster properties.

Cysteine desulfurase Nfs1 and Pim1 protease control levels of Isu, the Fe-S cluster biogenesis scaffold.

PubMed

Song, Ji-Yoon; Marszalek, Jaroslaw; Craig, Elizabeth Anne

2012-06-26

Fe-S clusters are critical prosthetic groups for proteins involved in various critical biological processes. Before being transferred to recipient apo-proteins, Fe-S clusters are assembled on the highly conserved scaffold protein Isu, the abundance of which is regulated posttranslationally on disruption of the cluster biogenesis system. Here we report that Isu is degraded by the Lon-type AAA+ ATPase protease of the mitochondrial matrix, Pim1. Nfs1, the cysteine desulfurase responsible for providing sulfur for cluster formation, is required for the increased Isu stability occurring after disruption of cluster formation on or transfer from Isu. Physical interaction between the Isu and Nfs1 proteins, not the enzymatic activity of Nfs1, is the important factor in increased stability. Analysis of several conditions revealed that high Isu levels can be advantageous or disadvantageous, depending on the physiological condition. During the stationary phase, elevated Isu levels were advantageous, resulting in prolonged chronological lifespan. On the other hand, under iron-limiting conditions, high Isu levels were deleterious. Compared with cells expressing normal levels of Isu, such cells grew poorly and exhibited reduced activity of the heme-containing enzyme ferric reductase. Our results suggest that modulation of the degradation of Isu by the Pim1 protease is a regulatory mechanism serving to rapidly help balance the cell's need for critical iron-requiring processes under changing environmental conditions.

Novel Signal Noise Reduction Method through Cluster Analysis, Applied to Photoplethysmography.

PubMed

Waugh, William; Allen, John; Wightman, James; Sims, Andrew J; Beale, Thomas A W

2018-01-01

Physiological signals can often become contaminated by noise from a variety of origins. In this paper, an algorithm is described for the reduction of sporadic noise from a continuous periodic signal. The design can be used where a sample of a periodic signal is required, for example, when an average pulse is needed for pulse wave analysis and characterization. The algorithm is based on cluster analysis for selecting similar repetitions or pulses from a periodic single. This method selects individual pulses without noise, returns a clean pulse signal, and terminates when a sufficiently clean and representative signal is received. The algorithm is designed to be sufficiently compact to be implemented on a microcontroller embedded within a medical device. It has been validated through the removal of noise from an exemplar photoplethysmography (PPG) signal, showing increasing benefit as the noise contamination of the signal increases. The algorithm design is generalised to be applicable for a wide range of physiological (physical) signals.

Clinical Characteristics of Exacerbation-Prone Adult Asthmatics Identified by Cluster Analysis.

PubMed

Kim, Mi Ae; Shin, Seung Woo; Park, Jong Sook; Uh, Soo Taek; Chang, Hun Soo; Bae, Da Jeong; Cho, You Sook; Park, Hae Sim; Yoon, Ho Joo; Choi, Byoung Whui; Kim, Yong Hoon; Park, Choon Sik

2017-11-01

Asthma is a heterogeneous disease characterized by various types of airway inflammation and obstruction. Therefore, it is classified into several subphenotypes, such as early-onset atopic, obese non-eosinophilic, benign, and eosinophilic asthma, using cluster analysis. A number of asthmatics frequently experience exacerbation over a long-term follow-up period, but the exacerbation-prone subphenotype has rarely been evaluated by cluster analysis. This prompted us to identify clusters reflecting asthma exacerbation. A uniform cluster analysis method was applied to 259 adult asthmatics who were regularly followed-up for over 1 year using 12 variables, selected on the basis of their contribution to asthma phenotypes. After clustering, clinical profiles and exacerbation rates during follow-up were compared among the clusters. Four subphenotypes were identified: cluster 1 was comprised of patients with early-onset atopic asthma with preserved lung function, cluster 2 late-onset non-atopic asthma with impaired lung function, cluster 3 early-onset atopic asthma with severely impaired lung function, and cluster 4 late-onset non-atopic asthma with well-preserved lung function. The patients in clusters 2 and 3 were identified as exacerbation-prone asthmatics, showing a higher risk of asthma exacerbation. Two different phenotypes of exacerbation-prone asthma were identified among Korean asthmatics using cluster analysis; both were characterized by impaired lung function, but the age at asthma onset and atopic status were different between the two. Copyright © 2017 The Korean Academy of Asthma, Allergy and Clinical Immunology · The Korean Academy of Pediatric Allergy and Respiratory Disease

Cluster analysis of autoantibodies in 852 patients with systemic lupus erythematosus from a single center.

PubMed

Artim-Esen, Bahar; Çene, Erhan; Şahinkaya, Yasemin; Ertan, Semra; Pehlivan, Özlem; Kamali, Sevil; Gül, Ahmet; Öcal, Lale; Aral, Orhan; Inanç, Murat

2014-07-01

Associations between autoantibodies and clinical features have been described in systemic lupus erythematosus (SLE). Herein, we aimed to define autoantibody clusters and their clinical correlations in a large cohort of patients with SLE. We analyzed 852 patients with SLE who attended our clinic. Seven autoantibodies were selected for cluster analysis: anti-DNA, anti-Sm, anti-RNP, anticardiolipin (aCL) immunoglobulin (Ig)G or IgM, lupus anticoagulant (LAC), anti-Ro, and anti-La. Two-step clustering and Kaplan-Meier survival analyses were used. Five clusters were identified. A cluster consisted of patients with only anti-dsDNA antibodies, a cluster of anti-Sm and anti-RNP, a cluster of aCL IgG/M and LAC, and a cluster of anti-Ro and anti-La antibodies. Analysis revealed 1 more cluster that consisted of patients who did not belong to any of the clusters formed by antibodies chosen for cluster analysis. Sm/RNP cluster had significantly higher incidence of pulmonary hypertension and Raynaud phenomenon. DsDNA cluster had the highest incidence of renal involvement. In the aCL/LAC cluster, there were significantly more patients with neuropsychiatric involvement, antiphospholipid syndrome, autoimmune hemolytic anemia, and thrombocytopenia. According to the Systemic Lupus International Collaborating Clinics damage index, the highest frequency of damage was in the aCL/LAC cluster. Comparison of 10 and 20 years survival showed reduced survival in the aCL/LAC cluster. This study supports the existence of autoantibody clusters with distinct clinical features in SLE and shows that forming clinical subsets according to autoantibody clusters may be useful in predicting the outcome of the disease. Autoantibody clusters in SLE may exhibit differences according to the clinical setting or population.

A framework for graph-based synthesis, analysis, and visualization of HPC cluster job data.

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

Mayo, Jackson R.; Kegelmeyer, W. Philip, Jr.; Wong, Matthew H.

The monitoring and system analysis of high performance computing (HPC) clusters is of increasing importance to the HPC community. Analysis of HPC job data can be used to characterize system usage and diagnose and examine failure modes and their effects. This analysis is not straightforward, however, due to the complex relationships that exist between jobs. These relationships are based on a number of factors, including shared compute nodes between jobs, proximity of jobs in time, etc. Graph-based techniques represent an approach that is particularly well suited to this problem, and provide an effective technique for discovering important relationships in jobmore » queuing and execution data. The efficacy of these techniques is rooted in the use of a semantic graph as a knowledge representation tool. In a semantic graph job data, represented in a combination of numerical and textual forms, can be flexibly processed into edges, with corresponding weights, expressing relationships between jobs, nodes, users, and other relevant entities. This graph-based representation permits formal manipulation by a number of analysis algorithms. This report presents a methodology and software implementation that leverages semantic graph-based techniques for the system-level monitoring and analysis of HPC clusters based on job queuing and execution data. Ontology development and graph synthesis is discussed with respect to the domain of HPC job data. The framework developed automates the synthesis of graphs from a database of job information. It also provides a front end, enabling visualization of the synthesized graphs. Additionally, an analysis engine is incorporated that provides performance analysis, graph-based clustering, and failure prediction capabilities for HPC systems.« less

Partially supervised speaker clustering.

PubMed

Tang, Hao; Chu, Stephen Mingyu; Hasegawa-Johnson, Mark; Huang, Thomas S

2012-05-01

Content-based multimedia indexing, retrieval, and processing as well as multimedia databases demand the structuring of the media content (image, audio, video, text, etc.), one significant goal being to associate the identity of the content to the individual segments of the signals. In this paper, we specifically address the problem of speaker clustering, the task of assigning every speech utterance in an audio stream to its speaker. We offer a complete treatment to the idea of partially supervised speaker clustering, which refers to the use of our prior knowledge of speakers in general to assist the unsupervised speaker clustering process. By means of an independent training data set, we encode the prior knowledge at the various stages of the speaker clustering pipeline via 1) learning a speaker-discriminative acoustic feature transformation, 2) learning a universal speaker prior model, and 3) learning a discriminative speaker subspace, or equivalently, a speaker-discriminative distance metric. We study the directional scattering property of the Gaussian mixture model (GMM) mean supervector representation of utterances in the high-dimensional space, and advocate exploiting this property by using the cosine distance metric instead of the euclidean distance metric for speaker clustering in the GMM mean supervector space. We propose to perform discriminant analysis based on the cosine distance metric, which leads to a novel distance metric learning algorithm—linear spherical discriminant analysis (LSDA). We show that the proposed LSDA formulation can be systematically solved within the elegant graph embedding general dimensionality reduction framework. Our speaker clustering experiments on the GALE database clearly indicate that 1) our speaker clustering methods based on the GMM mean supervector representation and vector-based distance metrics outperform traditional speaker clustering methods based on the “bag of acoustic features” representation and statistical model-based distance metrics, 2) our advocated use of the cosine distance metric yields consistent increases in the speaker clustering performance as compared to the commonly used euclidean distance metric, 3) our partially supervised speaker clustering concept and strategies significantly improve the speaker clustering performance over the baselines, and 4) our proposed LSDA algorithm further leads to state-of-the-art speaker clustering performance.

Is It Feasible to Identify Natural Clusters of TSC-Associated Neuropsychiatric Disorders (TAND)?

PubMed

Leclezio, Loren; Gardner-Lubbe, Sugnet; de Vries, Petrus J

2018-04-01

Tuberous sclerosis complex (TSC) is a genetic disorder with multisystem involvement. The lifetime prevalence of TSC-Associated Neuropsychiatric Disorders (TAND) is in the region of 90% in an apparently unique, individual pattern. This "uniqueness" poses significant challenges for diagnosis, psycho-education, and intervention planning. To date, no studies have explored whether there may be natural clusters of TAND. The purpose of this feasibility study was (1) to investigate the practicability of identifying natural TAND clusters, and (2) to identify appropriate multivariate data analysis techniques for larger-scale studies. TAND Checklist data were collected from 56 individuals with a clinical diagnosis of TSC (n = 20 from South Africa; n = 36 from Australia). Using R, the open-source statistical platform, mean squared contingency coefficients were calculated to produce a correlation matrix, and various cluster analyses and exploratory factor analysis were examined. Ward's method rendered six TAND clusters with good face validity and significant convergence with a six-factor exploratory factor analysis solution. The "bottom-up" data-driven strategies identified a "scholastic" cluster of TAND manifestations, an "autism spectrum disorder-like" cluster, a "dysregulated behavior" cluster, a "neuropsychological" cluster, a "hyperactive/impulsive" cluster, and a "mixed/mood" cluster. These feasibility results suggest that a combination of cluster analysis and exploratory factor analysis methods may be able to identify clinically meaningful natural TAND clusters. Findings require replication and expansion in larger dataset, and could include quantification of cluster or factor scores at an individual level. Copyright © 2018 Elsevier Inc. All rights reserved.

The relationship between a low grain intake dietary pattern and impulsive behaviors in middle-aged Japanese people.

PubMed

Toyomaki, Atsuhito; Koga, Minori; Okada, Emiko; Nakai, Yukiei; Miyazaki, Akane; Tamakoshi, Akiko; Kiso, Yoshinobu; Kusumi, Ichiro

2017-01-01

Several studies indicate that dietary habits are associated with mental health. We are interested in identifying not a specific single nutrient/food group but the population preferring specific food combinations that can be related to mental health. Very few studies have examined relationships between dietary patterns and multifaceted mental states using cluster analysis. The purpose of this study was to investigate population-level dietary patterns associated with mental state using cluster analysis. We focused on depressive state, sleep quality, subjective well-being, and impulsive behaviors using rating scales. Two hundred and seventy-nine Japanese middle-aged people participated in the present study. Dietary pattern was estimated using a brief self-administered diet-history questionnaire (the BDHQ). We conducted K-means cluster analysis using thirteen BDHQ food groups: milk, meat, fish, egg, pulses, potatoes, green and yellow vegetables, other vegetables, mushrooms, seaweed, sweets, fruits, and grain. We identified three clusters characterized as "vegetable and fruit dominant," "grain dominant," and "low grain tendency" subgroups. The vegetable and fruit dominant group showed increases in several aspects of subjective well-being demonstrated by the SF-8. Differences in mean subject characteristics across clusters were tested using ANOVA. The low frequency intake of grain group showed higher impulsive behavior, demonstrated by BIS-11 deliberation and sum scores. The present study demonstrated that traditional Japanese dietary patterns, such as eating rice, can help with beneficial changes in mental health.

The relationship between a low grain intake dietary pattern and impulsive behaviors in middle-aged Japanese people

PubMed Central

Toyomaki, Atsuhito; Koga, Minori; Okada, Emiko; Nakai, Yukiei; Miyazaki, Akane; Tamakoshi, Akiko; Kiso, Yoshinobu; Kusumi, Ichiro

2017-01-01

Several studies indicate that dietary habits are associated with mental health. We are interested in identifying not a specific single nutrient/food group but the population preferring specific food combinations that can be related to mental health. Very few studies have examined relationships between dietary patterns and multifaceted mental states using cluster analysis. The purpose of this study was to investigate population-level dietary patterns associated with mental state using cluster analysis. We focused on depressive state, sleep quality, subjective well-being, and impulsive behaviors using rating scales. Two hundred and seventy-nine Japanese middle-aged people participated in the present study. Dietary pattern was estimated using a brief self-administered diet-history questionnaire (the BDHQ). We conducted K-means cluster analysis using thirteen BDHQ food groups: milk, meat, fish, egg, pulses, potatoes, green and yellow vegetables, other vegetables, mushrooms, seaweed, sweets, fruits, and grain. We identified three clusters characterized as “vegetable and fruit dominant,” “grain dominant,” and “low grain tendency” subgroups. The vegetable and fruit dominant group showed increases in several aspects of subjective well-being demonstrated by the SF-8. Differences in mean subject characteristics across clusters were tested using ANOVA. The low frequency intake of grain group showed higher impulsive behavior, demonstrated by BIS-11 deliberation and sum scores. The present study demonstrated that traditional Japanese dietary patterns, such as eating rice, can help with beneficial changes in mental health. PMID:28704469

Sirenomelia in Argentina: Prevalence, geographic clusters and temporal trends analysis.

PubMed

Groisman, Boris; Liascovich, Rosa; Gili, Juan Antonio; Barbero, Pablo; Bidondo, María Paz

2016-07-01

Sirenomelia is a severe malformation of the lower body characterized by a single medial lower limb and a variable combination of visceral abnormalities. Given that Sirenomelia is a very rare birth defect, epidemiological studies are scarce. The aim of this study is to evaluate prevalence, geographic clusters and time trends of sirenomelia in Argentina, using data from the National Network of Congenital Anomalies of Argentina (RENAC) from November 2009 until December 2014. This is a descriptive study using data from the RENAC, a hospital-based surveillance system for newborns affected with major morphological congenital anomalies. We calculated sirenomelia prevalence throughout the period, searched for geographical clusters, and evaluated time trends. The prevalence of confirmed cases of sirenomelia throughout the period was 2.35 per 100,000 births. Cluster analysis showed no statistically significant geographical aggregates. Time-trends analysis showed that the prevalence was higher in years 2009 to 2010. The observed prevalence was higher than the observed in previous epidemiological studies in other geographic regions. We observed a likely real increase in the initial period of our study. We used strict diagnostic criteria, excluding cases that only had clinical diagnosis of sirenomelia. Therefore, real prevalence could be even higher. This study did not show any geographic clusters. Because etiology of sirenomelia has not yet been established, studies of epidemiological features of this defect may contribute to define its causes. Birth Defects Research (Part A) 106:604-611, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Psychosocial Costs of Racism to Whites: Exploring Patterns through Cluster Analysis

ERIC Educational Resources Information Center

Spanierman, Lisa B.; Poteat, V. Paul; Beer, Amanda M.; Armstrong, Patrick Ian

2006-01-01

Participants (230 White college students) completed the Psychosocial Costs of Racism to Whites (PCRW) Scale. Using cluster analysis, we identified 5 distinct cluster groups on the basis of PCRW subscale scores: the unempathic and unaware cluster contained the lowest empathy scores; the insensitive and afraid cluster consisted of low empathy and…

Allergen Sensitization Pattern by Sex: A Cluster Analysis in Korea.

PubMed

Ohn, Jungyoon; Paik, Seung Hwan; Doh, Eun Jin; Park, Hyun-Sun; Yoon, Hyun-Sun; Cho, Soyun

2017-12-01

Allergens tend to sensitize simultaneously. Etiology of this phenomenon has been suggested to be allergen cross-reactivity or concurrent exposure. However, little is known about specific allergen sensitization patterns. To investigate the allergen sensitization characteristics according to gender. Multiple allergen simultaneous test (MAST) is widely used as a screening tool for detecting allergen sensitization in dermatologic clinics. We retrospectively reviewed the medical records of patients with MAST results between 2008 and 2014 in our Department of Dermatology. A cluster analysis was performed to elucidate the allergen-specific immunoglobulin (Ig)E cluster pattern. The results of MAST (39 allergen-specific IgEs) from 4,360 cases were analyzed. By cluster analysis, 39items were grouped into 8 clusters. Each cluster had characteristic features. When compared with female, the male group tended to be sensitized more frequently to all tested allergens, except for fungus allergens cluster. The cluster and comparative analysis results demonstrate that the allergen sensitization is clustered, manifesting allergen similarity or co-exposure. Only the fungus cluster allergens tend to sensitize female group more frequently than male group.

Premature Osteoblast Clustering by Enamel Matrix Proteins Induces Osteoblast Differentiation through Up-Regulation of Connexin 43 and N-Cadherin

PubMed Central

Miron, Richard J.; Hedbom, Erik; Ruggiero, Sabrina; Bosshardt, Dieter D.; Zhang, Yufeng; Mauth, Corinna; Gemperli, Anja C.; Iizuka, Tateyuki; Buser, Daniel; Sculean, Anton

2011-01-01

In recent years, enamel matrix derivative (EMD) has garnered much interest in the dental field for its apparent bioactivity that stimulates regeneration of periodontal tissues including periodontal ligament, cementum and alveolar bone. Despite its widespread use, the underlying cellular mechanisms remain unclear and an understanding of its biological interactions could identify new strategies for tissue engineering. Previous in vitro research has demonstrated that EMD promotes premature osteoblast clustering at early time points. The aim of the present study was to evaluate the influence of cell clustering on vital osteoblast cell-cell communication and adhesion molecules, connexin 43 (cx43) and N-cadherin (N-cad) as assessed by immunofluorescence imaging, real-time PCR and Western blot analysis. In addition, differentiation markers of osteoblasts were quantified using alkaline phosphatase, osteocalcin and von Kossa staining. EMD significantly increased the expression of connexin 43 and N-cadherin at early time points ranging from 2 to 5 days. Protein expression was localized to cell membranes when compared to control groups. Alkaline phosphatase activity was also significantly increased on EMD-coated samples at 3, 5 and 7 days post seeding. Interestingly, higher activity was localized to cell cluster regions. There was a 3 fold increase in osteocalcin and bone sialoprotein mRNA levels for osteoblasts cultured on EMD-coated culture dishes. Moreover, EMD significantly increased extracellular mineral deposition in cell clusters as assessed through von Kossa staining at 5, 7, 10 and 14 days post seeding. We conclude that EMD up-regulates the expression of vital osteoblast cell-cell communication and adhesion molecules, which enhances the differentiation and mineralization activity of osteoblasts. These findings provide further support for the clinical evidence that EMD increases the speed and quality of new bone formation in vivo. PMID:21858092

Spatial Analysis of the Human Immunodeficiency Virus Epidemic among Men Who Have Sex with Men in China, 2006-2015.

PubMed

Qin, Qianqian; Guo, Wei; Tang, Weiming; Mahapatra, Tanmay; Wang, Liyan; Zhang, Nanci; Ding, Zhengwei; Cai, Chang; Cui, Yan; Sun, Jiangping

2017-04-01

Studies have shown a recent upsurge in human immunodeficiency virus (HIV) burden among men who have sex with men (MSM) in China, especially in urban areas. For intervention planning and resource allocation, spatial analyses of HIV/AIDS case-clusters were required to identify epidemic foci and trends among MSM in China. Information regarding MSM recorded as HIV/AIDS cases during 2006-2015 were extracted from the National Case Reporting System. Demographic trends were determined through Cochran-Armitage trend tests. Distribution of case-clusters was examined using spatial autocorrelation. Spatial-temporal scan was used to detect disease clustering. Spatial correlations between cases and socioenvironmental factors were determined by spatial regression. Between 2006 and 2015, in China, 120 371 HIV/AIDS cases were identified among MSM. Newly identified HIV/AIDS cases among self-reported MSM increased from 487 cases in 2006 to >30 000 cases in 2015. Among those HIV/AIDS cases recorded during 2006-2015, 47.0% were 20-29 years old and 24.9% were aged 30-39 years. Based on clusters of HIV/AIDS cases identified through spatial analysis, the epidemic was concentrated among MSM in large cities. Spatial-temporal clusters contained municipalities, provincial capitals, and main cities such as Beijing, Shanghai, Chongqing, Chengdu, and Guangzhou. Spatial regression analysis showed that sociodemographic indicators such as population density, per capita gross domestic product, and number of county-level medical institutions had statistically significant positive correlations with HIV/AIDS among MSM. Assorted spatial analyses revealed an increasingly concentrated HIV epidemic among young MSM in Chinese cities, calling for targeted health education and intensive interventions at an early age. © The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.

Metallicity Gradients in the Intracluster Gas of Abell 496

NASA Astrophysics Data System (ADS)

Dupke, Renato A.; White, Raymond E., III

2000-07-01

Analysis of spatially resolved ASCA spectra of the intracluster gas in Abell 496 confirms there are mild metal abundance enhancements near the center, as previously found in a joint analysis of spectra from Ginga Large Area Counter and Einstein solid state spectrometer. Simultaneous analysis of spectra from all ASCA instruments (SIS+GIS) shows that the iron abundance is 0.36+/-0.03 solar 3'-12' from the center of the cluster and rises ~50% to 0.53+/-0.04 solar within the central 2'. The F-test shows that this abundance gradient is significant at the more than 99.99% level. Nickel and sulfur abundances are also centrally enhanced. We use a variety of elemental abundance ratios to assess the relative contribution of Type Ia supernovae (SNe Ia) and Type II supernovae (SNe II) to the metal enrichment of the intracluster gas. We find spatial gradients in several abundance ratios, indicating that the fraction of iron from SNe Ia increases toward the cluster center, with SNe Ia accounting for ~50% of the iron mass 3'-12' from the center and ~70% within 2'. The increased proportion of SN Ia ejecta at the center is such that the central iron abundance enhancement can be attributed wholly to SNe Ia; we find no significant gradient in SN II ejecta. These spatial gradients in the proportion of SN Ia/II ejecta imply that the dominant metal enrichment mechanism near the center is different than in the outer parts of the cluster. We show that the central abundance enhancement is unlikely to be due to ram pressure stripping of gas from cluster galaxies or to secularly accumulated stellar mass loss within the central cD. We suggest that the additional SN Ia ejecta near the center is the vestige of a secondary SN Ia-driven wind from the cD (following a more energetic protogalactic SN II-driven wind phase), which was partially smothered in the cD due to its location at the cluster center.

Clusters of community exposure to coastal flooding hazards based on storm and sea level rise scenarios—implications for adaptation networks in the San Francisco Bay region

USGS Publications Warehouse

Hummel, Michelle; Wood, Nathan J.; Schweikert, Amy; Stacey, Mark T.; Jones, Jeanne; Barnard, Patrick L.; Erikson, Li H.

2018-01-01

Sea level is projected to rise over the coming decades, further increasing the extent of flooding hazards in coastal communities. Efforts to address potential impacts from climate-driven coastal hazards have called for collaboration among communities to strengthen the application of best practices. However, communities currently lack practical tools for identifying potential partner communities based on similar hazard exposure characteristics. This study uses statistical cluster analysis to identify similarities in community exposure to flooding hazards for a suite of sea level rise and storm scenarios. We demonstrate this approach using 63 jurisdictions in the San Francisco Bay region of California (USA) and compare 21 distinct exposure variables related to residents, employees, and structures for six hazard scenario combinations of sea level rise and storms. Results indicate that cluster analysis can provide an effective mechanism for identifying community groupings. Cluster compositions changed based on the selected societal variables and sea level rise scenarios, suggesting that a community could participate in multiple networks to target specific issues or policy interventions. The proposed clustering approach can serve as a data-driven foundation to help communities identify other communities with similar adaptation challenges and to enhance regional efforts that aim to facilitate adaptation planning and investment prioritization.

Experimental verification of the cluster model of CH3F-(ortho-H2)n in solid para-H2 by using mid-infrared pump-probe laser spectroscopy

NASA Astrophysics Data System (ADS)

Miyamoto, Yuki; Mizoguchi, Asao; Kanamori, Hideto

2017-03-01

The bleaching process in the C-F stretching mode (ν3 band) of CH3F-(ortho-H2)n [n = 0 and 1] clusters in solid para-H2 was monitored using pump and probe laser spectroscopy on the C-H stretching mode (ν1 and 2ν5 bands). From an analysis of the depleted spectral profiles, the transition frequency and linewidth of each cluster were directly determined. The results agree with the values previously derived from a deconvolution analysis of the broadened ν1/2ν5 spectrum observed by FTIR spectroscopy. The complementary increase and decrease between the n = 0 and 1 components were also verified through monitoring the ν1 and 2ν5 bands, which suggests a closed system among the CH3F-(ortho-H2)n clusters. These observations provide experimental verification of the CH3F-(ortho-H2)n cluster model. On the other hand, a trial to observe the bleaching process by pumping the C-H stretching mode was not successful. This result may be important for understanding the dynamics of vibrational relaxation processes in CH3F-(ortho-H2)n in solid para-H2.

Experimental verification of the cluster model of CH3F-(ortho-H2)n in solid para-H2 by using mid-infrared pump-probe laser spectroscopy.

PubMed

Miyamoto, Yuki; Mizoguchi, Asao; Kanamori, Hideto

2017-03-21

The bleaching process in the C-F stretching mode (ν 3 band) of CH 3 F-(ortho-H 2 ) n [n = 0 and 1] clusters in solid para-H 2 was monitored using pump and probe laser spectroscopy on the C-H stretching mode (ν 1 and 2ν 5 bands). From an analysis of the depleted spectral profiles, the transition frequency and linewidth of each cluster were directly determined. The results agree with the values previously derived from a deconvolution analysis of the broadened ν 1 /2ν 5 spectrum observed by FTIR spectroscopy. The complementary increase and decrease between the n = 0 and 1 components were also verified through monitoring the ν 1 and 2ν 5 bands, which suggests a closed system among the CH 3 F-(ortho-H 2 ) n clusters. These observations provide experimental verification of the CH 3 F-(ortho-H 2 ) n cluster model. On the other hand, a trial to observe the bleaching process by pumping the C-H stretching mode was not successful. This result may be important for understanding the dynamics of vibrational relaxation processes in CH 3 F-(ortho-H 2 ) n in solid para-H 2 .

Orbit Clustering Based on Transfer Cost

NASA Technical Reports Server (NTRS)

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

2013-01-01

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

Identification of the Main Regulator Responsible for Synthesis of the Typical Yellow Pigment Produced by Trichoderma reesei

PubMed Central

Derntl, Christian; Rassinger, Alice; Srebotnik, Ewald; Mach, Robert L.

2016-01-01

ABSTRACT The industrially used ascomycete Trichoderma reesei secretes a typical yellow pigment during cultivation, while other Trichoderma species do not. A comparative genomic analysis suggested that a putative secondary metabolism cluster, containing two polyketide-synthase encoding genes, is responsible for the yellow pigment synthesis. This cluster is conserved in a set of rather distantly related fungi, including Acremonium chrysogenum and Penicillium chrysogenum. In an attempt to silence the cluster in T. reesei, two genes of the cluster encoding transcription factors were individually deleted. For a complete genetic proof-of-function, the genes were reinserted into the genomes of the respective deletion strains. The deletion of the first transcription factor (termed yellow pigment regulator 1 [Ypr1]) resulted in the full abolishment of the yellow pigment formation and the expression of most genes of this cluster. A comparative high-pressure liquid chromatography (HPLC) analysis of supernatants of the ypr1 deletion and its parent strain suggested the presence of several yellow compounds in T. reesei that are all derived from the same cluster. A subsequent gas chromatography/mass spectrometry analysis strongly indicated the presence of sorbicillin in the major HPLC peak. The presence of the second transcription factor, termed yellow pigment regulator 2 (Ypr2), reduces the yellow pigment formation and the expression of most cluster genes, including the gene encoding the activator Ypr1. IMPORTANCE Trichoderma reesei is used for industry-scale production of carbohydrate-active enzymes. During growth, it secretes a typical yellow pigment. This is not favorable for industrial enzyme production because it makes the downstream process more complicated and thus increases operating costs. In this study, we demonstrate which regulators influence the synthesis of the yellow pigment. Based on these data, we also provide indication as to which genes are under the control of these regulators and are finally responsible for the biosynthesis of the yellow pigment. These genes are organized in a cluster that is also found in other industrially relevant fungi, such as the two antibiotic producers Penicillium chrysogenum and Acremonium chrysogenum. The targeted manipulation of a secondary metabolism cluster is an important option for any biotechnologically applied microorganism. PMID:27520818

Interactive K-Means Clustering Method Based on User Behavior for Different Analysis Target in Medicine.

PubMed

Lei, Yang; Yu, Dai; Bin, Zhang; Yang, Yang

2017-01-01

Clustering algorithm as a basis of data analysis is widely used in analysis systems. However, as for the high dimensions of the data, the clustering algorithm may overlook the business relation between these dimensions especially in the medical fields. As a result, usually the clustering result may not meet the business goals of the users. Then, in the clustering process, if it can combine the knowledge of the users, that is, the doctor's knowledge or the analysis intent, the clustering result can be more satisfied. In this paper, we propose an interactive K -means clustering method to improve the user's satisfactions towards the result. The core of this method is to get the user's feedback of the clustering result, to optimize the clustering result. Then, a particle swarm optimization algorithm is used in the method to optimize the parameters, especially the weight settings in the clustering algorithm to make it reflect the user's business preference as possible. After that, based on the parameter optimization and adjustment, the clustering result can be closer to the user's requirement. Finally, we take an example in the breast cancer, to testify our method. The experiments show the better performance of our algorithm.

Planck Cosmology, Planck Clusters, and What is to Come

NASA Astrophysics Data System (ADS)

Rozo, Eduardo

2015-08-01

Planck's view of the Cosmic Microwave Background (CMB) has ushered in a new era of precision cosmology. In the process, hints of tension with local universe cosmological probes have appeared, including not only tension between the CMB and local Hubble constant measurements, but between the CMB and Planck's own analysis of the SZ galaxy clusters discovered by Planck. We will discuss the state of cluster cosmology in light of these results, and comment on what is to come. Should these tensions continue to exist with ever future measurements of ever increasing precision, the current Planck results will stand as some of the first lines of evidence towards finally breaking the standard LCDM cosmological model!

Graduation of fertility schedules: an analysis of fertility patterns in London in the 1980s and an application to fertility forecasts.

PubMed

Congdon, P

1990-08-01

London's average total fertility rate (TFR) stood at 1.75. Using a cluster analysis to compare the 1985-1987 fertility patterns of different boroughs of London, demographers learned that 5 natural groupings occurred. 4 boroughs in a central London cluster have the distinction of having a low TFR (1.38) and late fertility (average age of 29.58 years). The researchers attributed these occurrences to the high levels of employment and career attachment and low rates of marriage among women in this cluster. 2 inner city boroughs constituted the smallest cluster and had the largest TFR (2.37), mainly due to high numbers of births to the ethnic minorities. The largest cluster consisted of 12 boroughs located mainly along the periphery with 2 centrally located boroughs (TFR, 1.79). Some of the upper class outer boroughs characterized another cluster with a TFR of 1.61. Another cluster made up of inner and outer boroughs in east and southeast London had a ample proportion of manual worker (TFR, 2.04). Social class most likely accounted for the contrast in TFRs between the 2 aformentioned clusters. Demographers observed that cyclical fluctuation of fertility occurred as opposed to secular trends. Due to these fluctuations, demographers used autoregressive moving average forecast models to time series of the fertility variables in London since 1952. They also applied structural time series models which included regression variables and the influence of cyclical and/or trend behavior. The results showed that large cohorts and the increase in female economic activity caused a delay in the modal age of births and a reduction in the number of births.

UQlust: combining profile hashing with linear-time ranking for efficient clustering and analysis of big macromolecular data.

PubMed

Adamczak, Rafal; Meller, Jarek

2016-12-28

Advances in computing have enabled current protein and RNA structure prediction and molecular simulation methods to dramatically increase their sampling of conformational spaces. The quickly growing number of experimentally resolved structures, and databases such as the Protein Data Bank, also implies large scale structural similarity analyses to retrieve and classify macromolecular data. Consequently, the computational cost of structure comparison and clustering for large sets of macromolecular structures has become a bottleneck that necessitates further algorithmic improvements and development of efficient software solutions. uQlust is a versatile and easy-to-use tool for ultrafast ranking and clustering of macromolecular structures. uQlust makes use of structural profiles of proteins and nucleic acids, while combining a linear-time algorithm for implicit comparison of all pairs of models with profile hashing to enable efficient clustering of large data sets with a low memory footprint. In addition to ranking and clustering of large sets of models of the same protein or RNA molecule, uQlust can also be used in conjunction with fragment-based profiles in order to cluster structures of arbitrary length. For example, hierarchical clustering of the entire PDB using profile hashing can be performed on a typical laptop, thus opening an avenue for structural explorations previously limited to dedicated resources. The uQlust package is freely available under the GNU General Public License at https://github.com/uQlust . uQlust represents a drastic reduction in the computational complexity and memory requirements with respect to existing clustering and model quality assessment methods for macromolecular structure analysis, while yielding results on par with traditional approaches for both proteins and RNAs.

[Typologies of Madrid's citizens (Spain) at the end-of-life: cluster analysis].

PubMed

Ortiz-Gonçalves, Belén; Perea-Pérez, Bernardo; Labajo González, Elena; Albarrán Juan, Elena; Santiago-Sáez, Andrés

2018-03-06

To establish typologies within Madrid's citizens (Spain) with regard to end-of-life by cluster analysis. The SPAD 8 programme was implemented in a sample from a health care centre in the autonomous region of Madrid (Spain). A multiple correspondence analysis technique was used, followed by a cluster analysis to create a dendrogram. A cross-sectional study was made beforehand with the results of the questionnaire. Five clusters stand out. Cluster 1: a group who preferred not to answer numerous questions (5%). Cluster 2: in favour of receiving palliative care and euthanasia (40%). Cluster 3: would oppose assisted suicide and would not ask for spiritual assistance (15%). Cluster 4: would like to receive palliative care and assisted suicide (16%). Cluster 5: would oppose assisted suicide and would ask for spiritual assistance (24%). The following four clusters stood out. Clusters 2 and 4 would like to receive palliative care, euthanasia (2) and assisted suicide (4). Clusters 4 and 5 regularly practiced their faith and their family members did not receive palliative care. Clusters 3 and 5 would be opposed to euthanasia and assisted suicide in particular. Clusters 2, 4 and 5 had not completed an advance directive document (2, 4 and 5). Clusters 2 and 3 seldom practiced their faith. This study could be taken into consideration to improve the quality of end-of-life care choices. Copyright © 2017 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Geographic Variation and Factors Associated with Female Genital Mutilation among Reproductive Age Women in Ethiopia: A National Population Based Survey

PubMed Central

Setegn, Tesfaye; Lakew, Yihunie; Deribe, Kebede

2016-01-01

Background Female genital mutilation (FGM) is a common traditional practice in developing nations including Ethiopia. It poses complex and serious long-term health risks for women and girls and can lead to death. In Ethiopia, the geographic distribution and factors associated with FGM practices are poorly understood. Therefore, we assessed the spatial distribution and factors associated with FGM among reproductive age women in the country. Method We used population based national representative surveys. Data from two (2000 and 2005) Ethiopian demographic and health surveys (EDHS) were used in this analysis. Briefly, EDHS used a stratified, two-stage cluster sampling design. A total of 15,367 (from EDHS 2000) and 14,070 (from EDHS 2005) women of reproductive age (15–49 years) were included in the analysis. Three outcome variables were used (prevalence of FGM among women, prevalence of FGM among daughters and support for the continuation of FGM). The data were weighted and descriptive statistics (percentage change), bivariate and multivariable logistic regression analyses were carried out. Multicollinearity of variables was assessed using variance inflation factors (VIF) with a reference value of 10 before interpreting the final output. The geographic variation and clustering of weighted FGM prevalence were analyzed and visualized on maps using ArcGIS. Z-scores were used to assess the statistical difference of geographic clustering of FGM prevalence spots. Result The trend of FGM weighted prevalence has been decreasing. Being wealthy, Muslim and in higher age categories are associated with increased odds of FGM among women. Similarly, daughters from Muslim women have increased odds of experiencing FGM. Women in the higher age categories have increased odds of having daughters who experience FGM. The odds of FGM among daughters decrease with increased maternal education. Mass media exposure, being wealthy and higher paternal and maternal education are associated with decreased odds of women’s support of FGM continuation. FGM prevalence and geographic clustering showed variation across regions in Ethiopia. Conclusion Individual, economic, socio-demographic, religious and cultural factors played major roles in the existing practice and continuation of FGM. The significant geographic clustering of FGM was observed across regions in Ethiopia. Therefore, targeted and integrated interventions involving religious leaders in high FGM prevalence spot clusters and addressing the socio-economic and geographic inequalities are recommended to eliminate FGM. PMID:26741488

Geographic Variation and Factors Associated with Female Genital Mutilation among Reproductive Age Women in Ethiopia: A National Population Based Survey.

PubMed

Setegn, Tesfaye; Lakew, Yihunie; Deribe, Kebede

2016-01-01

Female genital mutilation (FGM) is a common traditional practice in developing nations including Ethiopia. It poses complex and serious long-term health risks for women and girls and can lead to death. In Ethiopia, the geographic distribution and factors associated with FGM practices are poorly understood. Therefore, we assessed the spatial distribution and factors associated with FGM among reproductive age women in the country. We used population based national representative surveys. Data from two (2000 and 2005) Ethiopian demographic and health surveys (EDHS) were used in this analysis. Briefly, EDHS used a stratified, two-stage cluster sampling design. A total of 15,367 (from EDHS 2000) and 14,070 (from EDHS 2005) women of reproductive age (15-49 years) were included in the analysis. Three outcome variables were used (prevalence of FGM among women, prevalence of FGM among daughters and support for the continuation of FGM). The data were weighted and descriptive statistics (percentage change), bivariate and multivariable logistic regression analyses were carried out. Multicollinearity of variables was assessed using variance inflation factors (VIF) with a reference value of 10 before interpreting the final output. The geographic variation and clustering of weighted FGM prevalence were analyzed and visualized on maps using ArcGIS. Z-scores were used to assess the statistical difference of geographic clustering of FGM prevalence spots. The trend of FGM weighted prevalence has been decreasing. Being wealthy, Muslim and in higher age categories are associated with increased odds of FGM among women. Similarly, daughters from Muslim women have increased odds of experiencing FGM. Women in the higher age categories have increased odds of having daughters who experience FGM. The odds of FGM among daughters decrease with increased maternal education. Mass media exposure, being wealthy and higher paternal and maternal education are associated with decreased odds of women's support of FGM continuation. FGM prevalence and geographic clustering showed variation across regions in Ethiopia. Individual, economic, socio-demographic, religious and cultural factors played major roles in the existing practice and continuation of FGM. The significant geographic clustering of FGM was observed across regions in Ethiopia. Therefore, targeted and integrated interventions involving religious leaders in high FGM prevalence spot clusters and addressing the socio-economic and geographic inequalities are recommended to eliminate FGM.

Gene expression profiles of breast biopsies from healthy women identify a group with claudin-low features

PubMed Central

2011-01-01

Background Increased understanding of the variability in normal breast biology will enable us to identify mechanisms of breast cancer initiation and the origin of different subtypes, and to better predict breast cancer risk. Methods Gene expression patterns in breast biopsies from 79 healthy women referred to breast diagnostic centers in Norway were explored by unsupervised hierarchical clustering and supervised analyses, such as gene set enrichment analysis and gene ontology analysis and comparison with previously published genelists and independent datasets. Results Unsupervised hierarchical clustering identified two separate clusters of normal breast tissue based on gene-expression profiling, regardless of clustering algorithm and gene filtering used. Comparison of the expression profile of the two clusters with several published gene lists describing breast cells revealed that the samples in cluster 1 share characteristics with stromal cells and stem cells, and to a certain degree with mesenchymal cells and myoepithelial cells. The samples in cluster 1 also share many features with the newly identified claudin-low breast cancer intrinsic subtype, which also shows characteristics of stromal and stem cells. More women belonging to cluster 1 have a family history of breast cancer and there is a slight overrepresentation of nulliparous women in cluster 1. Similar findings were seen in a separate dataset consisting of histologically normal tissue from both breasts harboring breast cancer and from mammoplasty reductions. Conclusion This is the first study to explore the variability of gene expression patterns in whole biopsies from normal breasts and identified distinct subtypes of normal breast tissue. Further studies are needed to determine the specific cell contribution to the variation in the biology of normal breasts, how the clusters identified relate to breast cancer risk and their possible link to the origin of the different molecular subtypes of breast cancer. PMID:22044755

Two worlds collide: Image analysis methods for quantifying structural variation in cluster molecular dynamics

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

Steenbergen, K. G., E-mail: kgsteen@gmail.com; Gaston, N.

2014-02-14

Inspired by methods of remote sensing image analysis, we analyze structural variation in cluster molecular dynamics (MD) simulations through a unique application of the principal component analysis (PCA) and Pearson Correlation Coefficient (PCC). The PCA analysis characterizes the geometric shape of the cluster structure at each time step, yielding a detailed and quantitative measure of structural stability and variation at finite temperature. Our PCC analysis captures bond structure variation in MD, which can be used to both supplement the PCA analysis as well as compare bond patterns between different cluster sizes. Relying only on atomic position data, without requirement formore » a priori structural input, PCA and PCC can be used to analyze both classical and ab initio MD simulations for any cluster composition or electronic configuration. Taken together, these statistical tools represent powerful new techniques for quantitative structural characterization and isomer identification in cluster MD.« less

Two worlds collide: image analysis methods for quantifying structural variation in cluster molecular dynamics.

PubMed

Steenbergen, K G; Gaston, N

2014-02-14

Inspired by methods of remote sensing image analysis, we analyze structural variation in cluster molecular dynamics (MD) simulations through a unique application of the principal component analysis (PCA) and Pearson Correlation Coefficient (PCC). The PCA analysis characterizes the geometric shape of the cluster structure at each time step, yielding a detailed and quantitative measure of structural stability and variation at finite temperature. Our PCC analysis captures bond structure variation in MD, which can be used to both supplement the PCA analysis as well as compare bond patterns between different cluster sizes. Relying only on atomic position data, without requirement for a priori structural input, PCA and PCC can be used to analyze both classical and ab initio MD simulations for any cluster composition or electronic configuration. Taken together, these statistical tools represent powerful new techniques for quantitative structural characterization and isomer identification in cluster MD.

Chronic Obstructive Pulmonary Disease heterogeneity: challenges for health risk assessment, stratification and management.

PubMed

Roca, Josep; Vargas, Claudia; Cano, Isaac; Selivanov, Vitaly; Barreiro, Esther; Maier, Dieter; Falciani, Francesco; Wagner, Peter; Cascante, Marta; Garcia-Aymerich, Judith; Kalko, Susana; De Mas, Igor; Tegnér, Jesper; Escarrabill, Joan; Agustí, Alvar; Gomez-Cabrero, David

2014-11-28

Heterogeneity in clinical manifestations and disease progression in Chronic Obstructive Pulmonary Disease (COPD) lead to consequences for patient health risk assessment, stratification and management. Implicit with the classical "spill over" hypothesis is that COPD heterogeneity is driven by the pulmonary events of the disease. Alternatively, we hypothesized that COPD heterogeneities result from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering, each of them with their own dynamics. To explore the potential of a systems analysis of COPD heterogeneity focused on skeletal muscle dysfunction and on co-morbidity clustering aiming at generating predictive modeling with impact on patient management. To this end, strategies combining deterministic modeling and network medicine analyses of the Biobridge dataset were used to investigate the mechanisms of skeletal muscle dysfunction. An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was performed using a large dataset (ICD9-CM data from Medicare, 13 million people). Finally, a targeted network analysis using the outcomes of the two approaches (skeletal muscle dysfunction and co-morbidity clustering) explored shared pathways between these phenomena. (1) Evidence of abnormal regulation of skeletal muscle bioenergetics and skeletal muscle remodeling showing a significant association with nitroso-redox disequilibrium was observed in COPD; (2) COPD patients presented higher risk for co-morbidity clustering than non-COPD patients increasing with ageing; and, (3) the on-going targeted network analyses suggests shared pathways between skeletal muscle dysfunction and co-morbidity clustering. The results indicate the high potential of a systems approach to address COPD heterogeneity. Significant knowledge gaps were identified that are relevant to shape strategies aiming at fostering 4P Medicine for patients with COPD.

Hunting for origins of migraine pain: cluster analysis of spontaneous and capsaicin-induced firing in meningeal trigeminal nerve fibers

PubMed Central

Zakharov, A.; Vitale, C.; Kilinc, E.; Koroleva, K.; Fayuk, D.; Shelukhina, I.; Naumenko, N.; Skorinkin, A.; Khazipov, R.; Giniatullin, R.

2015-01-01

Trigeminal nerves in meninges are implicated in generation of nociceptive firing underlying migraine pain. However, the neurochemical mechanisms of nociceptive firing in meningeal trigeminal nerves are little understood. In this study, using suction electrode recordings from peripheral branches of the trigeminal nerve in isolated rat meninges, we analyzed spontaneous and capsaicin-induced orthodromic spiking activity. In control, biphasic single spikes with variable amplitude and shapes were observed. Application of the transient receptor potential vanilloid 1 (TRPV1) agonist capsaicin to meninges dramatically increased firing whereas the amplitudes and shapes of spikes remained essentially unchanged. This effect was antagonized by the specific TRPV1 antagonist capsazepine. Using the clustering approach, several groups of uniform spikes (clusters) were identified. The clustering approach combined with capsaicin application allowed us to detect and to distinguish “responder” (65%) from “non-responder” clusters (35%). Notably, responders fired spikes at frequencies exceeding 10 Hz, high enough to provide postsynaptic temporal summation of excitation at brainstem and spinal cord level. Almost all spikes were suppressed by tetrodotoxin (TTX) suggesting an involvement of the TTX-sensitive sodium channels in nociceptive signaling at the peripheral branches of trigeminal neurons. Our analysis also identified transient (desensitizing) and long-lasting (slowly desensitizing) responses to the continuous application of capsaicin. Thus, the persistent activation of nociceptors in capsaicin-sensitive nerve fibers shown here may be involved in trigeminal pain signaling and plasticity along with the release of migraine-related neuropeptides from TRPV1 positive neurons. Furthermore, cluster analysis could be widely used to characterize the temporal and neurochemical profiles of other pain transducers likely implicated in migraine. PMID:26283923

A hybrid monkey search algorithm for clustering analysis.

PubMed

Chen, Xin; Zhou, Yongquan; Luo, Qifang

2014-01-01

Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

RNA-seq analysis identifies an intricate regulatory network controlling cluster root development in white lupin

PubMed Central

2014-01-01

Background Highly adapted plant species are able to alter their root architecture to improve nutrient uptake and thrive in environments with limited nutrient supply. Cluster roots (CRs) are specialised structures of dense lateral roots formed by several plant species for the effective mining of nutrient rich soil patches through a combination of increased surface area and exudation of carboxylates. White lupin is becoming a model-species allowing for the discovery of gene networks involved in CR development. A greater understanding of the underlying molecular mechanisms driving these developmental processes is important for the generation of smarter plants for a world with diminishing resources to improve food security. Results RNA-seq analyses for three developmental stages of the CR formed under phosphorus-limited conditions and two of non-cluster roots have been performed for white lupin. In total 133,045,174 high-quality paired-end reads were used for a de novo assembly of the root transcriptome and merged with LAGI01 (Lupinus albus gene index) to generate an improved LAGI02 with 65,097 functionally annotated contigs. This was followed by comparative gene expression analysis. We show marked differences in the transcriptional response across the various cluster root stages to adjust to phosphate limitation by increasing uptake capacity and adjusting metabolic pathways. Several transcription factors such as PLT, SCR, PHB, PHV or AUX/IAA with a known role in the control of meristem activity and developmental processes show an increased expression in the tip of the CR. Genes involved in hormonal responses (PIN, LAX, YUC) and cell cycle control (CYCA/B, CDK) are also differentially expressed. In addition, we identify primary transcripts of miRNAs with established function in the root meristem. Conclusions Our gene expression analysis shows an intricate network of transcription factors and plant hormones controlling CR initiation and formation. In addition, functional differences between the different CR developmental stages in the acclimation to phosphorus starvation have been identified. PMID:24666749

MicroRNA-424/503 cluster members regulate bovine granulosa cell proliferation and cell cycle progression by targeting SMAD7 gene through activin signalling pathway.

PubMed

Pande, Hari Om; Tesfaye, Dawit; Hoelker, Michael; Gebremedhn, Samuel; Held, Eva; Neuhoff, Christiane; Tholen, Ernst; Schellander, Karl; Wondim, Dessie Salilew

2018-05-01

The granulosa cells are indispensable for follicular development and its function is orchestrated by several genes, which in turn posttranscriptionally regulated by microRNAs (miRNA). In our previous study, the miRRNA-424/503 cluster was found to be highly abundant in bovine granulosa cells (bGCs) of preovulatory dominant follicle compared to subordinate counterpart at day 19 of the bovine estrous cycle. Other study also indicated the involvement of miR-424/503 cluster in tumour cell resistance to apoptosis suggesting this miRNA cluster may involve in cell survival. However, the role of miR-424/503 cluster in granulosa cell function remains elusive Therefore, this study aimed to investigate the role of miRNA-424/503 cluster in bGCs function using microRNA gain- and loss-of-function approaches. The role of miR-424/503 cluster members in granulosa cell function was investigated by overexpressing or inhibiting its activity in vitro cultured granulosa cells using miR-424/503 mimic or inhibitor, respectively. Luciferase reporter assay showed that SMAD7 and ACVR2A are the direct targets of the miRNA-424/503 cluster members. In line with this, overexpression of miRNA-424/503 cluster members using its mimic and inhibition of its activity by its inhibitor reduced and increased, respectively the expression of SMAD7 and ACVR2A. Furthermore, flow cytometric analysis indicated that overexpression of miRNA-424/503 cluster members enhanced bGCs proliferation by promoting G1- to S- phase cell cycle transition. Modulation of miRNA-424/503 cluster members tended to increase phosphorylation of SMAD2/3 in the Activin signalling pathway. Moreover, sequence specific knockdown of SMAD7, the target gene of miRNA-424/503 cluster members, using small interfering RNA also revealed similar phenotypic and molecular alterations observed when miRNA-424/503 cluster members were overexpressed. Similarly, to get more insight about the role of miRNA-424/503 cluster members in activin signalling pathway, granulosa cells were treated with activin A. Activin A treatment increased cell proliferation and downregulation of both miRNA-424/503 members and its target gene, indicated the presence of negative feedback loop between activin A and the expression of miRNA-424/503. This study suggests that the miRNA-424/503 cluster members are involved in regulating bovine granulosa cell proliferation and cell cycle progression. Further, miRNA-424/503 cluster members target the SMAD7 and ACVR2A genes which are involved in the activin signalling pathway.

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.

Synthesis efficiency of heavy carbon clusters from ETFE ablated by different numbers of laser pulse in vacuum

NASA Astrophysics Data System (ADS)

Shibagaki, K.; Takada, N.; Sasaki, K.; Kadota, K.

2002-09-01

We have carried out mass spectral analysis of positive ions produced by laser ablation of a copolymer of ethylene and tetrafluoroethylene (ETFE: [CH 2CH 2CF 2CF 2] n) in vacuum using time-of-flight mass spectrometry (TOF-MS). The surfaces of the ETFE targets irradiated by different numbers of laser pulse were analyzed by X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM). Heavy carbon cluster ions C n+ with n≥30 were observed in the mass spectra. The fractional abundance of heavy clusters in the mass spectrum decreased with the number of laser pulse. On the other hand, carbon became rich in the atomic composition of the laser-irradiated surface, and the eroded area on the surface increased with the number of laser pulse. From these results, it is suggested that the carbon-rich material surface results in the less efficient production of heavy carbon clusters. In addition, it is also suggested that clustering reactions in eroded craters do not contribute to the synthesis of heavy clusters.

A History of Cluster Analysis Using the Classification Society's Bibliography Over Four Decades

NASA Astrophysics Data System (ADS)

Murtagh, Fionn; Kurtz, Michael J.

2016-04-01

The Classification Literature Automated Search Service, an annual bibliography based on citation of one or more of a set of around 80 book or journal publications, ran from 1972 to 2012. We analyze here the years 1994 to 2011. The Classification Society's Service, as it was termed, has been produced by the Classification Society. In earlier decades it was distributed as a diskette or CD with the Journal of Classification. Among our findings are the following: an enormous increase in scholarly production post approximately 2000; a very major increase in quantity, coupled with work in different disciplines, from approximately 2004; and a major shift also from cluster analysis in earlier times having mathematics and psychology as disciplines of the journals published in, and affiliations of authors, contrasted with, in more recent times, a "centre of gravity" in management and engineering.

Genetic diversity and divergence at the Arbutus unedo L. (Ericaceae) westernmost distribution limit.

PubMed

Ribeiro, Maria Margarida; Piotti, Andrea; Ricardo, Alexandra; Gaspar, Daniel; Costa, Rita; Parducci, Laura; Vendramin, Giovanni Giuseppe

2017-01-01

Mediterranean forests are fragile ecosystems vulnerable to recent global warming and reduction of precipitation, and a long-term negative effect is expected on vegetation with increasing drought and in areas burnt by fires. We investigated the spatial distribution of genetic variation of Arbutus unedo in the western Iberia Peninsula, using plastid markers with conservation and provenance regions design purposes. This species is currently undergoing an intense domestication process in the region, and, like other species, is increasingly under the threat from climate change, habitat fragmentation and wildfires. We sampled 451 trees from 15 natural populations from different ecological conditions spanning the whole species' distribution range in the region. We applied Bayesian analysis and identified four clusters (north, centre, south, and a single-population cluster). Hierarchical AMOVA showed higher differentiation among clusters than among populations within clusters. The relatively low within-clusters differentiation can be explained by a common postglacial history of nearby populations. The genetic structure found, supported by the few available palaeobotanical records, cannot exclude the hypothesis of two independent A. unedo refugia in western Iberia Peninsula during the Last Glacial Maximum. Based on the results we recommend a conservation strategy by selecting populations for conservation based on their allelic richness and diversity and careful seed transfer consistent with current species' genetic structure.

Genetic diversity and divergence at the Arbutus unedo L. (Ericaceae) westernmost distribution limit

PubMed Central

Ribeiro, Maria Margarida; Piotti, Andrea; Ricardo, Alexandra; Gaspar, Daniel; Costa, Rita; Parducci, Laura; Vendramin, Giovanni Giuseppe

2017-01-01

Mediterranean forests are fragile ecosystems vulnerable to recent global warming and reduction of precipitation, and a long-term negative effect is expected on vegetation with increasing drought and in areas burnt by fires. We investigated the spatial distribution of genetic variation of Arbutus unedo in the western Iberia Peninsula, using plastid markers with conservation and provenance regions design purposes. This species is currently undergoing an intense domestication process in the region, and, like other species, is increasingly under the threat from climate change, habitat fragmentation and wildfires. We sampled 451 trees from 15 natural populations from different ecological conditions spanning the whole species’ distribution range in the region. We applied Bayesian analysis and identified four clusters (north, centre, south, and a single-population cluster). Hierarchical AMOVA showed higher differentiation among clusters than among populations within clusters. The relatively low within-clusters differentiation can be explained by a common postglacial history of nearby populations. The genetic structure found, supported by the few available palaeobotanical records, cannot exclude the hypothesis of two independent A. unedo refugia in western Iberia Peninsula during the Last Glacial Maximum. Based on the results we recommend a conservation strategy by selecting populations for conservation based on their allelic richness and diversity and careful seed transfer consistent with current species’ genetic structure. PMID:28384294

Insight from first principles into the stability and magnetism of alkali-metal superoxide nanoclusters

NASA Astrophysics Data System (ADS)

Arcelus, Oier; Suaud, Nicolas; Katcho, Nebil A.; Carrasco, Javier

2017-05-01

Alkali-metal superoxides are gaining increasing interest as 2p magnetic materials for information and energy storage. Despite significant research efforts on bulk materials, gaps in our knowledge of the electronic and magnetic properties at the nanoscale still remain. Here, we focused on the role that structural details play in determining stability, electronic structure, and magnetic couplings of (MO2)n (M = Li, Na, and K, with n = 2-8) clusters. Using first-principles density functional theory based on the Perdew-Burke-Ernzerhof and Heyd-Scuseria-Ernzerhof functionals, we examined the effect of atomic structure on the relative stability of different polymorphs within each investigated cluster size. We found that small clusters prefer to form planar-ring structures, whereas non-planar geometries become more stable when increasing the cluster size. However, the crossover point depends on the nature of the alkali metal. Our analysis revealed that electrostatic interactions govern the highly ionic M-O2 bonding and ultimately control the relative stability between 2-D and 3-D geometries. In addition, we analyzed the weak magnetic couplings between superoxide molecules in (NaO2)4 clusters comparing model Hamiltonian methods based on Wannier function projections onto πg states with wave function-based multi-reference calculations.

Spatiotemporal analysis of dengue fever in Nepal from 2010 to 2014.

PubMed

Acharya, Bipin Kumar; Cao, ChunXiang; Lakes, Tobia; Chen, Wei; Naeem, Shahid

2016-08-22

Due to recent emergence, dengue is becoming one of the major public health problems in Nepal. The numbers of reported dengue cases in general and the area with reported dengue cases are both continuously increasing in recent years. However, spatiotemporal patterns and clusters of dengue have not been investigated yet. This study aims to fill this gap by analyzing spatiotemporal patterns based on monthly surveillance data aggregated at district. Dengue cases from 2010 to 2014 at district level were collected from the Nepal government's health and mapping agencies respectively. GeoDa software was used to map crude incidence, excess hazard and spatially smoothed incidence. Cluster analysis was performed in SaTScan software to explore spatiotemporal clusters of dengue during the above-mentioned time period. Spatiotemporal distribution of dengue fever in Nepal from 2010 to 2014 was mapped at district level in terms of crude incidence, excess risk and spatially smoothed incidence. Results show that the distribution of dengue fever was not random but clustered in space and time. Chitwan district was identified as the most likely cluster and Jhapa district was the first secondary cluster in both spatial and spatiotemporal scan. July to September of 2010 was identified as a significant temporal cluster. This study assessed and mapped for the first time the spatiotemporal pattern of dengue fever in Nepal. Two districts namely Chitwan and Jhapa were found highly affected by dengue fever. The current study also demonstrated the importance of geospatial approach in epidemiological research. The initial result on dengue patterns and risk of this study may assist institutions and policy makers to develop better preventive strategies.

Comparison of the DiversiLab Repetitive Element PCR System with spa Typing and Pulsed-Field Gel Electrophoresis for Clonal Characterization of Methicillin-Resistant Staphylococcus aureus▿

PubMed Central

Babouee, B.; Frei, R.; Schultheiss, E.; Widmer, A. F.; Goldenberger, D.

2011-01-01

The emergence of methicillin-resistant Staphylococcus aureus (MRSA) has become an increasing problem worldwide in recent decades. Molecular typing methods have been developed to identify clonality of strains and monitor spread of MRSA. We compared a new commercially available DiversiLab (DL) repetitive element PCR system with spa typing, spa clonal cluster analysis, and pulsed-field gel electrophoresis (PFGE) in terms of discriminatory power and concordance. A collection of 106 well-defined MRSA strains from our hospital was analyzed, isolated between 1994 and 2006. In addition, we analyzed 6 USA300 strains collected in our institution. DL typing separated the 106 MRSA isolates in 10 distinct clusters and 8 singleton patterns. Clustering analysis into spa clonal complexes resulted in 3 clusters: spa-CC 067/548, spa-CC 008, and spa-CC 012. The discriminatory powers (Simpson's index of diversity) were 0.982, 0.950, 0.846, and 0.757 for PFGE, spa typing, DL typing, and spa clonal clustering, respectively. DL typing and spa clonal clustering showed the highest concordance, calculated by adjusted Rand's coefficients. The 6 USA300 isolates grouped homogeneously into distinct PFGE and DL clusters, and all belonged to spa type t008 and spa-CC 008. Among the three methods, DL proved to be rapid and easy to perform. DL typing qualifies for initial screening during outbreak investigation. However, compared to PFGE and spa typing, DL typing has limited discriminatory power and therefore should be complemented by more discriminative methods in isolates that share identical DL patterns. PMID:21307215

Know thy eHealth user: Development of biopsychosocial personas from a study of older adults with heart failure.

PubMed

Holden, Richard J; Kulanthaivel, Anand; Purkayastha, Saptarshi; Goggins, Kathryn M; Kripalani, Sunil

2017-12-01

Personas are a canonical user-centered design method increasingly used in health informatics research. Personas-empirically-derived user archetypes-can be used by eHealth designers to gain a robust understanding of their target end users such as patients. To develop biopsychosocial personas of older patients with heart failure using quantitative analysis of survey data. Data were collected using standardized surveys and medical record abstraction from 32 older adults with heart failure recently hospitalized for acute heart failure exacerbation. Hierarchical cluster analysis was performed on a final dataset of n=30. Nonparametric analyses were used to identify differences between clusters on 30 clustering variables and seven outcome variables. Six clusters were produced, ranging in size from two to eight patients per cluster. Clusters differed significantly on these biopsychosocial domains and subdomains: demographics (age, sex); medical status (comorbid diabetes); functional status (exhaustion, household work ability, hygiene care ability, physical ability); psychological status (depression, health literacy, numeracy); technology (Internet availability); healthcare system (visit by home healthcare, trust in providers); social context (informal caregiver support, cohabitation, marital status); and economic context (employment status). Tabular and narrative persona descriptions provide an easy reference guide for informatics designers. Personas development using approaches such as clustering of structured survey data is an important tool for health informatics professionals. We describe insights from our study of patients with heart failure, then recommend a generic ten-step personas development process. Methods strengths and limitations of the study and of personas development generally are discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Cluster analysis of spontaneous preterm birth phenotypes identifies potential associations among preterm birth mechanisms

PubMed Central

Esplin, M Sean; Manuck, Tracy A.; Varner, Michael W.; Christensen, Bryce; Biggio, Joseph; Bukowski, Radek; Parry, Samuel; Zhang, Heping; Huang, Hao; Andrews, William; Saade, George; Sadovsky, Yoel; Reddy, Uma M.; Ilekis, John

2015-01-01

Objective We sought to employ an innovative tool based on common biological pathways to identify specific phenotypes among women with spontaneous preterm birth (SPTB), in order to enhance investigators' ability to identify to highlight common mechanisms and underlying genetic factors responsible for SPTB. Study Design A secondary analysis of a prospective case-control multicenter study of SPTB. All cases delivered a preterm singleton at SPTB ≤34.0 weeks gestation. Each woman was assessed for the presence of underlying SPTB etiologies. A hierarchical cluster analysis was used to identify groups of women with homogeneous phenotypic profiles. One of the phenotypic clusters was selected for candidate gene association analysis using VEGAS software. Results 1028 women with SPTB were assigned phenotypes. Hierarchical clustering of the phenotypes revealed five major clusters. Cluster 1 (N=445) was characterized by maternal stress, cluster 2 (N=294) by premature membrane rupture, cluster 3 (N=120) by familial factors, and cluster 4 (N=63) by maternal comorbidities. Cluster 5 (N=106) was multifactorial, characterized by infection (INF), decidual hemorrhage (DH) and placental dysfunction (PD). These three phenotypes were highly correlated by Chi-square analysis [PD and DH (p<2.2e-6); PD and INF (p=6.2e-10); INF and DH (p=0.0036)]. Gene-based testing identified the INS (insulin) gene as significantly associated with cluster 3 of SPTB. Conclusion We identified 5 major clusters of SPTB based on a phenotype tool and hierarchal clustering. There was significant correlation between several of the phenotypes. The INS gene was associated with familial factors underlying SPTB. PMID:26070700

Patterns of Food Parenting Practices and Children's Intake of Energy-Dense Snack Foods.

PubMed

Gevers, Dorus W M; Kremers, Stef P J; de Vries, Nanne K; van Assema, Patricia

2015-05-27

Most previous studies of parental influences on children's diets included just a single or a few types of food parenting practices, while parents actually employ multiple types of practices. Our objective was to investigate the clustering of parents regarding food parenting practices and to characterize the clusters in terms of background characteristics and children's intake of energy-dense snack foods. A sample of Dutch parents of children aged 4-12 was recruited by a research agency to fill out an online questionnaire. A hierarchical cluster analysis (n = 888) was performed, followed by k-means clustering. ANOVAs, ANCOVAs and chi-square tests were used to investigate associations between cluster membership, parental and child background characteristics, as well as children's intake of energy-dense snack foods. Four distinct patterns were discovered: "high covert control and rewarding", "low covert control and non-rewarding", "high involvement and supportive" and "low involvement and indulgent". The "high involvement and supportive" cluster was found to be most favorable in terms of children's intake. Several background factors characterized cluster membership. This study expands the current knowledge about parental influences on children's diets. Interventions should focus on increasing parental involvement in food parenting.

A high fat diet containing saturated but not unsaturated fatty acids enhances T cell receptor clustering on the nanoscale.

PubMed

Shaikh, Saame Raza; Boyle, Sarah; Edidin, Michael

2015-09-01

Cell culture studies show that the nanoscale lateral organization of surface receptors, their clustering or dispersion, can be altered by changing the lipid composition of the membrane bilayer. However, little is known about similar changes in vivo, which can be effected by changing dietary lipids. We describe the use of a newly developed method, k-space image correlation spectroscopy, kICS, for analysis of quantum dot fluorescence to show that a high fat diet can alter the nanometer-scale clustering of the murine T cell receptor, TCR, on the surface of naive CD4(+) T cells. We found that diets enriched primarily in saturated fatty acids increased TCR nanoscale clustering to a level usually seen only on activated cells. Diets enriched in monounsaturated or n-3 polyunsaturated fatty acids had no effect on TCR clustering. Also none of the high fat diets affected TCR clustering on the micrometer scale. Furthermore, the effect of the diets was similar in young and middle aged mice. Our data establish proof-of-principle that TCR nanoscale clustering is sensitive to the composition of dietary fat. Copyright © 2015 Elsevier Ltd. All rights reserved.

Kinetics of Forming Aldehydes in Frying Oils and Their Distribution in French Fries Revealed by LC-MS-Based Chemometrics.

PubMed

Wang, Lei; Csallany, A Saari; Kerr, Brian J; Shurson, Gerald C; Chen, Chi

2016-05-18

In this study, the kinetics of aldehyde formation in heated frying oils was characterized by 2-hydrazinoquinoline derivatization, liquid chromatography-mass spectrometry (LC-MS) analysis, principal component analysis (PCA), and hierarchical cluster analysis (HCA). The aldehydes contributing to time-dependent separation of heated soybean oil (HSO) in a PCA model were grouped by the HCA into three clusters (A1, A2, and B) on the basis of their kinetics and fatty acid precursors. The increases of 4-hydroxynonenal (4-HNE) and the A2-to-B ratio in HSO were well-correlated with the duration of thermal stress. Chemometric and quantitative analysis of three frying oils (soybean, corn, and canola oils) and French fry extracts further supported the associations between aldehyde profiles and fatty acid precursors and also revealed that the concentrations of pentanal, hexanal, acrolein, and the A2-to-B ratio in French fry extracts were more comparable to their values in the frying oils than other unsaturated aldehydes. All of these results suggest the roles of specific aldehydes or aldehyde clusters as novel markers of the lipid oxidation status for frying oils or fried foods.

Changes in the structure of nuclei between the magic neutron numbers 50 and 82 as indicated by a rotating-cluster analysis of the energy values of the first 2+ excited states of isotopes of cadmium, tin, and tellurium

PubMed Central

Pauling, Linus

1981-01-01

Values of R, the radius of rotation of the rotating cluster, are calculated from the observed values of the energy of the lowest 2+ states of the even isotopes of 48Cd, 50Sn, and 52Te with the assumption that the cluster is α, p2, and α, respectively. R shows a maximum at ≈N = 58, a minimum at ≈N = 62, and a second maximum at ≈N = 70. The increase to the first maximum is interpreted as resulting from the overcrowding of spherons (alphas and tritons) in the mantle (outer layer) of the nuclei, causing the cluster to change from rotating in the mantle to skimming over its surface; the decrease to the minimum results from the addition of three dineutrons to the core, expanding the mantle and permitting the rotating cluster to begin to drop back into it; and the increase to the second maximum results from the overcrowding of the larger mantle surrounding the core containing the semi-magic number 14 of neutrons rather than the magic number 8 for N = 50. The decrease after the second maximum results from the further increase in the number of core neutrons to 20, corresponding to the magic number 82. Some additional evidence for the change to an intermediate structure between N = 50 and N = 82 is also discussed. PMID:16593084

Elucidation of the Pattern of the Onset of Male Lower Urinary Tract Symptoms Using Cluster Analysis: Efficacy of Tamsulosin in Each Symptom Group.

PubMed

Aikawa, Ken; Kataoka, Masao; Ogawa, Soichiro; Akaihata, Hidenori; Sato, Yuichi; Yabe, Michihiro; Hata, Junya; Koguchi, Tomoyuki; Kojima, Yoshiyuki; Shiragasawa, Chihaya; Kobayashi, Toshimitsu; Yamaguchi, Osamu

2015-08-01

To present a new grouping of male patients with lower urinary tract symptoms (LUTS) based on symptom patterns and clarify whether the therapeutic effect of α1-blocker differs among the groups. We performed secondary analysis of anonymous data from 4815 patients enrolled in a postmarketing surveillance study of tamsulosin in Japan. Data on 7 International Prostate Symptom Score (IPSS) items at the initial visit were used in the cluster analysis. IPSS and quality of life (QOL) scores before and after tamsulosin treatment for 12 weeks were assessed in each cluster. Partial correlation coefficients were also obtained for IPSS and QOL scores based on changes before and after treatment. Five symptom groups were identified by cluster analysis of IPSS. On their symptom profile, each cluster was labeled as minimal type (cluster 1), multiple severe type (cluster 2), weak stream type (cluster 3), storage type (cluster 4), and voiding type (cluster 5). Prevalence and the mean symptom score were significantly improved in almost all symptoms in all clusters by tamsulosin treatment. Nocturia and weak stream had the strongest effect on QOL in clusters 1, 2, and 4 and clusters 3 and 5, respectively. The study clarified that 5 characteristic symptom patterns exist by cluster analysis of IPSS in male patients with LUTS. Tamsulosin improved various symptoms and QOL in each symptom group. The study reports many male patients with LUTS being satisfied with monotherapy using tamsulosin and suggests the usefulness of α1-blockers as a drug of first choice. Copyright © 2015 Elsevier Inc. All rights reserved.

Strategic groups, performance, and strategic response in the nursing home industry.

PubMed

Zinn, J S; Aaronson, W E; Rosko, M D

1994-06-01

This study examines the effect of strategic group membership on nursing home performance and strategic behavior. Data from the 1987 Medicare and Medicaid Automated Certification Survey were combined with data from the 1987 and 1989 Pennsylvania Long Term Care Facility Questionnaire. The sample consisted of 383 Pennsylvania nursing homes. Cluster analysis was used to place the 383 nursing homes into strategic groups on the basis of variables measuring scope and resource deployment. Performance was measured by indicators of the quality of nursing home care (rates of pressure ulcers, catheterization, and restraint usage) and efficiency in services provision. Changes in Medicare participation after passage of the 1988 Medicare Catastrophic Coverage Act (MCCA) measured strategic behavior. MANOVA and Turkey HSD post hoc means tests determined if significant differences were associated with strategic group membership. Cluster analysis produced an optimal seven-group solution. Differences in group means were significant for the clustering, performance, and conduct variables (p < .0001). Strategic groups characterized by facilities providing a continuum of care services had the best patient care outcomes. The most efficient groups were characterized by facilities with high Medicare census. While all strategic groups increased Medicare census following passage of the MCCA, those dominated by for-profits had the greatest increases. Our analysis demonstrates that strategic orientation influences nursing home response to regulatory initiatives, a factor that should be recognized in policy formation directed at nursing home reform.

The Best of Both Worlds: Building on the COPUS and RTOP Observation Protocols to Easily and Reliably Measure Various Levels of Reformed Instructional Practice

PubMed Central

Lund, Travis J.; Pilarz, Matthew; Velasco, Jonathan B.; Chakraverty, Devasmita; Rosploch, Kaitlyn; Undersander, Molly; Stains, Marilyne

2015-01-01

Researchers, university administrators, and faculty members are increasingly interested in measuring and describing instructional practices provided in science, technology, engineering, and mathematics (STEM) courses at the college level. Specifically, there is keen interest in comparing instructional practices between courses, monitoring changes over time, and mapping observed practices to research-based teaching. While increasingly common observation protocols (Reformed Teaching Observation Protocol [RTOP] and Classroom Observation Protocol in Undergraduate STEM [COPUS]) at the postsecondary level help achieve some of these goals, they also suffer from weaknesses that limit their applicability. In this study, we leverage the strengths of these protocols to provide an easy method that enables the reliable and valid characterization of instructional practices. This method was developed empirically via a cluster analysis using observations of 269 individual class periods, corresponding to 73 different faculty members, 28 different research-intensive institutions, and various STEM disciplines. Ten clusters, called COPUS profiles, emerged from this analysis; they represent the most common types of instructional practices enacted in the classrooms observed for this study. RTOP scores were used to validate the alignment of the 10 COPUS profiles with reformed teaching. Herein, we present a detailed description of the cluster analysis method, the COPUS profiles, and the distribution of the COPUS profiles across various STEM courses at research-intensive universities. PMID:25976654

Multiscale visual quality assessment for cluster analysis with self-organizing maps

NASA Astrophysics Data System (ADS)

Bernard, Jürgen; von Landesberger, Tatiana; Bremm, Sebastian; Schreck, Tobias

2011-01-01

Cluster analysis is an important data mining technique for analyzing large amounts of data, reducing many objects to a limited number of clusters. Cluster visualization techniques aim at supporting the user in better understanding the characteristics and relationships among the found clusters. While promising approaches to visual cluster analysis already exist, these usually fall short of incorporating the quality of the obtained clustering results. However, due to the nature of the clustering process, quality plays an important aspect, as for most practical data sets, typically many different clusterings are possible. Being aware of clustering quality is important to judge the expressiveness of a given cluster visualization, or to adjust the clustering process with refined parameters, among others. In this work, we present an encompassing suite of visual tools for quality assessment of an important visual cluster algorithm, namely, the Self-Organizing Map (SOM) technique. We define, measure, and visualize the notion of SOM cluster quality along a hierarchy of cluster abstractions. The quality abstractions range from simple scalar-valued quality scores up to the structural comparison of a given SOM clustering with output of additional supportive clustering methods. The suite of methods allows the user to assess the SOM quality on the appropriate abstraction level, and arrive at improved clustering results. We implement our tools in an integrated system, apply it on experimental data sets, and show its applicability.

Cluster and Multiple Correspondence Analyses in Rheumatology: Paths to Uncovering Relationships in a Sea of Data.

PubMed

Han, Lu; Benseler, Susanne M; Tyrrell, Pascal N

2018-05-01

Rheumatic diseases encompass a wide range of conditions caused by inflammation and dysregulation of the immune system resulting in organ damage. Research in these heterogeneous diseases benefits from multivariate methods. The aim of this review was to describe and evaluate current literature in rheumatology regarding cluster analysis and correspondence analysis. A systematic review showed an increase in studies making use of these 2 methods. However, standardization in how these methods are applied and reported is needed. Researcher expertise was determined to be the main barrier to considering these approaches, whereas education and collaborating with a biostatistician were suggested ways forward. Copyright © 2018 Elsevier Inc. All rights reserved.

Measuring the Indonesian provinces competitiveness by using PCA technique

NASA Astrophysics Data System (ADS)

Runita, Ditha; Fajriyah, Rohmatul

2017-12-01

Indonesia is a country which has vast teritoty. It has 34 provinces. Building local competitiveness is critical to enhance the long-term national competitiveness especially for a country as diverse as Indonesia. A competitive local government can attract and maintain successful firms and increase living standards for its inhabitants, because investment and skilled workers gravitate from uncompetitive regions to more competitive ones. Altough there are other methods to measuring competitiveness, but here we have demonstrated a simple method using principal component analysis (PCA). It can directly be applied to correlated, multivariate data. The analysis on Indonesian provinces provides 3 clusters based on the competitiveness measurement and the clusters are Bad, Good and Best perform provinces.

OGLE II Eclipsing Binaries In The LMC: Analysis With Class

NASA Astrophysics Data System (ADS)

Devinney, Edward J.; Prsa, A.; Guinan, E. F.; DeGeorge, M.

2011-01-01

The Eclipsing Binaries (EBs) via Artificial Intelligence (EBAI) Project is applying machine learning techniques to elucidate the nature of EBs. Previously, Prsa, et al. applied artificial neural networks (ANNs) trained on physically-realistic Wilson-Devinney models to solve the light curves of the 1882 detached EBs in the LMC discovered by the OGLE II Project (Wyrzykowski, et al.) fully automatically, bypassing the need for manually-derived starting solutions. A curious result is the non-monotonic distribution of the temperature ratio parameter T2/T1, featuring a subsidiary peak noted previously by Mazeh, et al. in an independent analysis using the EBOP EB solution code (Tamuz, et al.). To explore this and to gain a fuller understanding of the multivariate EBAI LMC observational plus solutions data, we have employed automatic clustering and advanced visualization (CAV) techniques. Clustering the OGLE II data aggregates objects that are similar with respect to many parameter dimensions. Measures of similarity for example, could include the multidimensional Euclidean Distance between data objects, although other measures may be appropriate. Applying clustering, we find good evidence that the T2/T1 subsidiary peak is due to evolved binaries, in support of Mazeh et al.'s speculation. Further, clustering suggests that the LMC detached EBs occupying the main sequence region belong to two distinct classes. Also identified as a separate cluster in the multivariate data are stars having a Period-I band relation. Derekas et al. had previously found a Period-K band relation for LMC EBs discovered by the MACHO Project (Alcock, et al.). We suggest such CAV techniques will prove increasingly useful for understanding the large, multivariate datasets increasingly being produced in astronomy. We are grateful for the support of this research from NSF/RUI Grant AST-05-75042 f.

A real world study on the genetic, cognitive and psychopathological differences of obese patients clustered according to eating behaviours.

PubMed

Caroleo, Mariarita; Primerano, Amedeo; Rania, Marianna; Aloi, Matteo; Pugliese, Valentina; Magliocco, Fabio; Fazia, Gilda; Filippo, Andrea; Sinopoli, Flora; Ricchio, Marco; Arturi, Franco; Jimenez-Murcia, Susana; Fernandez-Aranda, Fernando; De Fazio, Pasquale; Segura-Garcia, Cristina

2018-02-01

Considering that specific genetic profiles, psychopathological conditions and neurobiological systems underlie human behaviours, the phenotypic differentiation of obese patients according to eating behaviours should be investigated. The aim of this study was to classify obese patients according to their eating behaviours and to compare these clusters in regard to psychopathology, personality traits, neurocognitive patterns and genetic profiles. A total of 201 obese outpatients seeking weight reduction treatment underwent a dietetic visit, psychological and psychiatric assessment and genotyping for SCL6A2 polymorphisms. Eating behaviours were clustered through two-step cluster analysis, and these clusters were subsequently compared. Two groups emerged: cluster 1 contained patients with predominantly prandial hyperphagia, social eating, an increased frequency of the long allele of the 5-HTTLPR and low scores in all tests; and cluster 2 included patients with more emotionally related eating behaviours (emotional eating, grazing, binge eating, night eating, post-dinner eating, craving for carbohydrates), dysfunctional personality traits, neurocognitive impairment, affective disorders and increased frequencies of the short (S) allele and the S/S genotype. Aside from binge eating, dysfunctional eating behaviours were useful symptoms to identify two different phenotypes of obese patients from a comprehensive set of parameters (genetic, clinical, personality and neuropsychology) in this sample. Grazing and emotional eating were the most important predictors for classifying obese patients, followed by binge eating. This clustering overcomes the idea that 'binging' is the predominant altered eating behaviour, and could help physicians other than psychiatrists to identify whether an obese patient has an eating disorder. Finally, recognising different types of obesity may not only allow a more comprehensive understanding of this illness, but also make it possible to tailor patient-specific treatment pathways. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Analysis of Changes in Recent Tuberculosis Transmission Patterns after a Sharp Increase in Immigration▿

PubMed Central

Iñigo, Jesús; García de Viedma, Darío; Arce, Araceli; Palenque, Elia; Alonso Rodríguez, Noelia; Rodríguez, Elena; Ruiz Serrano, María Jesús; Andrés, Sandra; Bouza, Emilio; Chaves, Fernando

2007-01-01

We conducted a population-based molecular epidemiological study of tuberculosis (TB) in Madrid, Spain (2002 to 2004), to define transmission patterns and factors associated with clustering. We particularly focused on examining how the increase in TB cases among immigrants in recent years (2.8% in 1997 to 1999 to 36.2% during the current study) was modifying transmission patterns. Mycobacterium tuberculosis isolates obtained from patients living in nine districts of Madrid (1,459,232 inhabitants) were genotyped. The TB case rate among foreign-born people was three to four times that of Spanish-born people, and the median time from arrival to the onset of treatment was 22.4 months. During the study period, 227 (36.3%) patients were grouped in 64 clusters, and 115 (50.7%) of them were in 21 clusters with mixed Spanish-born and foreign-born patients. Three of the 21 mixed clusters accounted for 21.1% of clustered patients. Twenty-two of 38 (57.9%) immigrants in mixed clusters were infected with TB strains that had already been identified in the native population in 1997 to 1999, including the three most prevalent strains. Factors identified as independent predictors of clustering were homelessness (odds ratio [OR], 2.3; 95% confidence interval [95% CI], 1.2 to 4.5; P = 0.011) and to be born in Spain (OR, 1.8; 95% CI, 1.2 to 2.6; P = 0.002). The results indicated that (i) TB transmission was higher in Spanish-born people, associated mainly with homelessness, (ii) that foreign-born people were much less likely to be clustered, suggesting a higher percentage of infection before arriving in Spain, and (iii) that an extensive transmission between Spanish- and foreign-born populations, caused mainly by autochthonous strains, was taking place in Madrid. PMID:17108076

Impact of non-uniform correlation structure on sample size and power in multiple-period cluster randomised trials.

PubMed

Kasza, J; Hemming, K; Hooper, R; Matthews, Jns; Forbes, A B

2017-01-01

Stepped wedge and cluster randomised crossover trials are examples of cluster randomised designs conducted over multiple time periods that are being used with increasing frequency in health research. Recent systematic reviews of both of these designs indicate that the within-cluster correlation is typically taken account of in the analysis of data using a random intercept mixed model, implying a constant correlation between any two individuals in the same cluster no matter how far apart in time they are measured: within-period and between-period intra-cluster correlations are assumed to be identical. Recently proposed extensions allow the within- and between-period intra-cluster correlations to differ, although these methods require that all between-period intra-cluster correlations are identical, which may not be appropriate in all situations. Motivated by a proposed intensive care cluster randomised trial, we propose an alternative correlation structure for repeated cross-sectional multiple-period cluster randomised trials in which the between-period intra-cluster correlation is allowed to decay depending on the distance between measurements. We present results for the variance of treatment effect estimators for varying amounts of decay, investigating the consequences of the variation in decay on sample size planning for stepped wedge, cluster crossover and multiple-period parallel-arm cluster randomised trials. We also investigate the impact of assuming constant between-period intra-cluster correlations instead of decaying between-period intra-cluster correlations. Our results indicate that in certain design configurations, including the one corresponding to the proposed trial, a correlation decay can have an important impact on variances of treatment effect estimators, and hence on sample size and power. An R Shiny app allows readers to interactively explore the impact of correlation decay.

Genetic structure of Cantharellus formosus populations in a second-growth temperate rain forest of the Pacific Northwest

USGS Publications Warehouse

Redman, Regina S.; Ranson, Judith; Rodriguez, Rusty J.

2006-01-01

Cantharellus formosus growing on the Olympic Peninsula of the Pacific Northwest was sampled from September – November 1995 for genetic analysis. A total of ninety-six basidiomes from five clusters separated from one another by 3 - 25 meters were genetically characterized by PCR analysis of 13 arbitrary loci and rDNA sequences. The number of basidiomes in each cluster varied from 15 to 25 and genetic analysis delineated 15 genets among the clusters. Analysis of variance utilizing thirteen apPCR generated genetic molecular markers and PCR amplification of the ribosomal ITS regions indicated that 81.41% of the genetic variation occurred between clusters and 18.59% within clusters. Proximity of the basidiomes within a cluster was not an indicator of genotypic similarity. The molecular profiles of each cluster were distinct and defined as unique populations containing 2 - 6 genets. The monitoring and analysis of this species through non-lethal sampling and future applications is discussed.

Characterizing cognitive heterogeneity on the schizophrenia-bipolar disorder spectrum.

PubMed

Van Rheenen, T E; Lewandowski, K E; Tan, E J; Ospina, L H; Ongur, D; Neill, E; Gurvich, C; Pantelis, C; Malhotra, A K; Rossell, S L; Burdick, K E

2017-07-01

Current group-average analysis suggests quantitative but not qualitative cognitive differences between schizophrenia (SZ) and bipolar disorder (BD). There is increasing recognition that cognitive within-group heterogeneity exists in both disorders, but it remains unclear as to whether between-group comparisons of performance in cognitive subgroups emerging from within each of these nosological categories uphold group-average findings. We addressed this by identifying cognitive subgroups in large samples of SZ and BD patients independently, and comparing their cognitive profiles. The utility of a cross-diagnostic clustering approach to understanding cognitive heterogeneity in these patients was also explored. Hierarchical clustering analyses were conducted using cognitive data from 1541 participants (SZ n = 564, BD n = 402, healthy control n = 575). Three qualitatively and quantitatively similar clusters emerged within each clinical group: a severely impaired cluster, a mild-moderately impaired cluster and a relatively intact cognitive cluster. A cross-diagnostic clustering solution also resulted in three subgroups and was superior in reducing cognitive heterogeneity compared with disorder clustering independently. Quantitative SZ-BD cognitive differences commonly seen using group averages did not hold when cognitive heterogeneity was factored into our sample. Members of each corresponding subgroup, irrespective of diagnosis, might be manifesting the outcome of differences in shared cognitive risk factors.

Differential global structural changes in the core particle of yeast and mouse proteasome induced by ligand binding

PubMed Central

Arciniega, Marcelino; Beck, Philipp; Lange, Oliver F.; Groll, Michael; Huber, Robert

2014-01-01

Two clusters of configurations of the main proteolytic subunit β5 were identified by principal component analysis of crystal structures of the yeast proteasome core particle (yCP). The apo-cluster encompasses unliganded species and complexes with nonpeptidic ligands, and the pep-cluster comprises complexes with peptidic ligands. The murine constitutive CP structures conform to the yeast system, with the apo-form settled in the apo-cluster and the PR-957 (a peptidic ligand) complex in the pep-cluster. In striking contrast, the murine immune CP classifies into the pep-cluster in both the apo and the PR-957–liganded species. The two clusters differ essentially by multiple small structural changes and a domain motion enabling enclosure of the peptidic ligand and formation of specific hydrogen bonds in the pep-cluster. The immune CP species is in optimal peptide binding configuration also in its apo form. This favors productive ligand binding and may help to explain the generally increased functional activity of the immunoproteasome. Molecular dynamics simulations of the representative murine species are consistent with the experimentally observed configurations. A comparison of all 28 subunits of the unliganded species with the peptidic liganded forms demonstrates a greatly enhanced plasticity of β5 and suggests specific signaling pathways to other subunits. PMID:24979800

Planck/SDSS Cluster Mass and Gas Scaling Relations for a Volume-Complete redMaPPer Sample

NASA Astrophysics Data System (ADS)

Jimeno, Pablo; Diego, Jose M.; Broadhurst, Tom; De Martino, I.; Lazkoz, Ruth

2018-04-01

Using Planck satellite data, we construct Sunyaev-Zel'dovich (SZ) gas pressure profiles for a large, volume-complete sample of optically selected clusters. We have defined a sample of over 8,000 redMaPPer clusters from the Sloan Digital Sky Survey (SDSS), within the volume-complete redshift region 0.100 < z < 0.325, for which we construct SZ effect maps by stacking Planck data over the full range of richness. Dividing the sample into richness bins we simultaneously solve for the mean cluster mass in each bin together with the corresponding radial pressure profile parameters, employing an MCMC analysis. These profiles are well detected over a much wider range of cluster mass and radius than previous work, showing a clear trend towards larger break radius with increasing cluster mass. Our SZ-based masses fall ˜16% below the mass-richness relations from weak lensing, in a similar fashion as the "hydrostatic bias" related with X-ray derived masses. Finally, we derive a tight Y500-M500 relation over a wide range of cluster mass, with a power law slope equal to 1.70 ± 0.07, that agrees well with the independent slope obtained by the Planck team with an SZ-selected cluster sample, but extends to lower masses with higher precision.

[Achene morphology cluster analysis of Taraxacum F. H. Wigg. from northeast China and molecule systematics evidence determined by SRAP].

PubMed

Li, Hai-juan; Zhao, Xin; Jia, Qing-fei; Li, Tian-lai; Ning, Wei

2012-08-01

The achenes morphological and micro-morphological characteristics of six species of genus Taraxacum from northeastern China as well as SRAP cluster analysis were observed for their classification evidences. The achenes were observed by microscope and EPMA. Cluster analysis was given on the basis of the size, shape, cone proportion, color and surface sculpture of achenes. The Taraxacum inter-species achene shape characteristic difference is obvious, particularly spinulose distribution and size, achene color and achene size; with the Taraxacum plant achene shape the cluster method T. antungense Kitag. and the T. urbanum Kitag. should combine for the identical kind; the achene morphology cluster analysis and the SRAP tagged molecule systematics's cluster result retrieves in the table with "the Chinese flora". The class group to divide the result is consistent. Taraxacum plant achene shape characteristic stable conservative, may carry on the inter-species division and the sibship analysis according to the achene shape characteristic combination difference; the achene morphology cluster analysis as well as the SRAP tagged molecule systematics confirmation support dandelion classification result of "the Chinese flora".

Exploratory Item Classification Via Spectral Graph Clustering

PubMed Central

Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

2017-01-01

Large-scale assessments are supported by a large item pool. An important task in test development is to assign items into scales that measure different characteristics of individuals, and a popular approach is cluster analysis of items. Classical methods in cluster analysis, such as the hierarchical clustering, K-means method, and latent-class analysis, often induce a high computational overhead and have difficulty handling missing data, especially in the presence of high-dimensional responses. In this article, the authors propose a spectral clustering algorithm for exploratory item cluster analysis. The method is computationally efficient, effective for data with missing or incomplete responses, easy to implement, and often outperforms traditional clustering algorithms in the context of high dimensionality. The spectral clustering algorithm is based on graph theory, a branch of mathematics that studies the properties of graphs. The algorithm first constructs a graph of items, characterizing the similarity structure among items. It then extracts item clusters based on the graphical structure, grouping similar items together. The proposed method is evaluated through simulations and an application to the revised Eysenck Personality Questionnaire. PMID:29033476

Sulfur in Cometary Dust

NASA Technical Reports Server (NTRS)

Fomenkova, M. N.

1997-01-01

The computer-intensive project consisted of the analysis and synthesis of existing data on composition of comet Halley dust particles. The main objective was to obtain a complete inventory of sulfur containing compounds in the comet Halley dust by building upon the existing classification of organic and inorganic compounds and applying a variety of statistical techniques for cluster and cross-correlational analyses. A student hired for this project wrote and tested the software to perform cluster analysis. The following tasks were carried out: (1) selecting the data from existing database for the proposed project; (2) finding access to a standard library of statistical routines for cluster analysis; (3) reformatting the data as necessary for input into the library routines; (4) performing cluster analysis and constructing hierarchical cluster trees using three methods to define the proximity of clusters; (5) presenting the output results in different formats to facilitate the interpretation of the obtained cluster trees; (6) selecting groups of data points common for all three trees as stable clusters. We have also considered the chemistry of sulfur in inorganic compounds.

Performance analysis of clustering techniques over microarray data: A case study

NASA Astrophysics Data System (ADS)

Dash, Rasmita; Misra, Bijan Bihari

2018-03-01

Handling big data is one of the major issues in the field of statistical data analysis. In such investigation cluster analysis plays a vital role to deal with the large scale data. There are many clustering techniques with different cluster analysis approach. But which approach suits a particular dataset is difficult to predict. To deal with this problem a grading approach is introduced over many clustering techniques to identify a stable technique. But the grading approach depends on the characteristic of dataset as well as on the validity indices. So a two stage grading approach is implemented. In this study the grading approach is implemented over five clustering techniques like hybrid swarm based clustering (HSC), k-means, partitioning around medoids (PAM), vector quantization (VQ) and agglomerative nesting (AGNES). The experimentation is conducted over five microarray datasets with seven validity indices. The finding of grading approach that a cluster technique is significant is also established by Nemenyi post-hoc hypothetical test.

[Temporal analysis of mortality due to intimate partner violence in Spain].

PubMed

Vives, Carmen; Caballero, Pablo; Álvarez-Dardet, Carlos

2004-01-01

To analyze the temporal distribution of mortality due to violence by intimate partners (VIP) and to identify possible temporal clusters in women deaths by VIP in Spain. We performed a descriptive epidemiological study based on the VIP deaths included in the database of the Federation of Divorced and Separated Women (1998-2003). The epidemic index (EI) was calculated as the ratio between the actual number of VIP deaths in a given month from January to July 2003 and the median number in the same month in the five preceding years. A Poisson model was used to analyze the distribution by years (1998-2002), seasons, months, and days. Simple regression analysis was performed with three-monthly means. A temporal cluster analysis was also carried out. In 2003, the EI of VIP mortality was high in January (EI = 1.6), March (EI = 1.2), May (EI = 1.5), June (EI = 2), and July (EI = 2.5). Compared with 1998 and Sundays, respectively, mortality due to VIP was significantly increased in 2001 (relative risk, RR = 1.52; 95% confidence interval [CI], 1.05-2.20) and on Mondays (RR = 1.77; 95%CI, 1.13-2.76). The regression analyses confirmed an increase between the first three-month period of 1998 and the last three-month period of 2001. There were no differences between seasons and months. No temporal clusters of deaths were detected. VIP is currently an increasing epidemic in Spain with no clear temporal pattern. Political and legal efforts to reduce this problem do not seem to be successful.

The myeloproliferative neoplasms, unclassifiable: clinical and pathological considerations.

PubMed

Gianelli, Umberto; Cattaneo, Daniele; Bossi, Anna; Cortinovis, Ivan; Boiocchi, Leonardo; Liu, Yen-Chun; Augello, Claudia; Bonometti, Arturo; Fiori, Stefano; Orofino, Nicola; Guidotti, Francesca; Orazi, Attilio; Iurlo, Alessandra

2017-02-01

In this study, we investigate in detail the morphological, clinical and molecular features of 71 consecutive patients with a diagnosis of myeloproliferative neoplasms, unclassifiable. We performed a meticulous morphological analysis and found that most of the cases displayed a hypercellular bone marrow (70%) with normal erythropoiesis without left-shifting (59%), increased granulopoiesis with left-shifting (73%) and increased megakaryocytes with loose clustering (96%). Megakaryocytes displayed frequent giant forms with hyperlobulated or bulbous nuclei and/or other maturation defects. Interestingly, more than half of the cases displayed severe bone marrow fibrosis (59%). Median values of hemoglobin level and white blood cells count were all within the normal range; in contrast, median platelets count and lactate dehydrogenase were increased. Little less than half of the patients (44%) showed splenomegaly. JAK2V617F mutation was detected in 72% of all patients. Among the JAK2-negative cases, MPLW515L mutation was found in 17% and CALR mutations in 67% of the investigated cases, respectively. Finally, by multiple correspondence analysis of the morphological profiles, we found that all but four of the cases could be grouped in three morphological clusters with some features similar to those of the classic BCR-ABL1-negative myeloproliferative neoplasms. Analysis of the clinical parameters in these three clusters revealed discrepancies with the morphological profile in about 55% of the patients. In conclusion, we found that the category of myeloproliferative neoplasm, unclassifiable is heterogeneous but identification of different subgroups is possible and should be recommended for a better management of these patients.

Clusters of Insomnia Disorder: An Exploratory Cluster Analysis of Objective Sleep Parameters Reveals Differences in Neurocognitive Functioning, Quantitative EEG, and Heart Rate Variability.

PubMed

Miller, Christopher B; Bartlett, Delwyn J; Mullins, Anna E; Dodds, Kirsty L; Gordon, Christopher J; Kyle, Simon D; Kim, Jong Won; D'Rozario, Angela L; Lee, Rico S C; Comas, Maria; Marshall, Nathaniel S; Yee, Brendon J; Espie, Colin A; Grunstein, Ronald R

2016-11-01

To empirically derive and evaluate potential clusters of Insomnia Disorder through cluster analysis from polysomnography (PSG). We hypothesized that clusters would differ on neurocognitive performance, sleep-onset measures of quantitative ( q )-EEG and heart rate variability (HRV). Research volunteers with Insomnia Disorder (DSM-5) completed a neurocognitive assessment and overnight PSG measures of total sleep time (TST), wake time after sleep onset (WASO), and sleep onset latency (SOL) were used to determine clusters. From 96 volunteers with Insomnia Disorder, cluster analysis derived at least two clusters from objective sleep parameters: Insomnia with normal objective sleep duration (I-NSD: n = 53) and Insomnia with short sleep duration (I-SSD: n = 43). At sleep onset, differences in HRV between I-NSD and I-SSD clusters suggest attenuated parasympathetic activity in I-SSD (P < 0.05). Preliminary work suggested three clusters by retaining the I-NSD and splitting the I-SSD cluster into two: I-SSD A (n = 29): defined by high WASO and I-SSD B (n = 14): a second I-SSD cluster with high SOL and medium WASO. The I-SSD B cluster performed worse than I-SSD A and I-NSD for sustained attention (P ≤ 0.05). In an exploratory analysis, q -EEG revealed reduced spectral power also in I-SSD B before (Delta, Alpha, Beta-1) and after sleep-onset (Beta-2) compared to I-SSD A and I-NSD (P ≤ 0.05). Two insomnia clusters derived from cluster analysis differ in sleep onset HRV. Preliminary data suggest evidence for three clusters in insomnia with differences for sustained attention and sleep-onset q -EEG. Insomnia 100 sleep study: Australia New Zealand Clinical Trials Registry (ANZCTR) identification number 12612000049875. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=347742. © 2016 Associated Professional Sleep Societies, LLC.

Muscle ischaemia associated with NXP2 autoantibodies: a severe subtype of juvenile dermatomyositis.

PubMed

Aouizerate, Jessie; De Antonio, Marie; Bader-Meunier, Brigitte; Barnerias, Christine; Bodemer, Christine; Isapof, Arnaud; Quartier, Pierre; Melki, Isabelle; Charuel, Jean-Luc; Bassez, Guillaume; Desguerre, Isabelle; Gherardi, Romain K; Authier, François-Jérôme; Gitiaux, Cyril

2018-05-01

Myositis-specific autoantibodies (MSAs) are increasingly used to delineate distinct subgroups of JDM. The aim of our study was to explore without a priori hypotheses whether MSAs are associated with distinct clinical-pathological changes and severity in a monocentric JDM cohort. Clinical, biological and histological findings from 23 JDM patients were assessed. Twenty-six histopathological parameters were subjected to multivariate analysis. Autoantibodies included anti-NXP2 (9/23), anti-TIF1γ (4/23), anti-MDA5 (2/23), no MSAs (8/23). Multivariate analysis yielded two histopathological clusters. Cluster 1 (n = 11) showed a more severe and ischaemic pattern than cluster 2 (n = 12) assessed by: total score severity ⩾ 20 (100.0% vs 25.0%); visual analogic score ⩾6 (100.0% vs 25.0%); the vascular domain score >1 (100.0% vs 41.7%); microinfarcts (100% vs 58.3%); ischaemic myofibrillary loss (focal punched-out vacuoles) (90.9 vs 25%); and obvious capillary loss (81.8% vs 16.7). Compared with cluster 2, patients in cluster 1 had strikingly more often anti-NXP2 antibodies (7/11 vs 2/12), more pronounced muscle weakness, more gastrointestinal involvement and required more aggressive treatment. Furthermore, patients with anti-NXP2 antibodies, mostly assigned in the first cluster, also displayed more severe muscular disease, requiring more aggressive treatment and having a lower remission rate during the follow-up period. Marked muscle ischaemic involvement and the presence of anti-NXP2 autoantibodies are associated with more severe forms of JDM.

The impact of clinical, demographic and risk factors on rates of HIV transmission: a population-based phylogenetic analysis in British Columbia, Canada.

PubMed

Poon, Art F Y; Joy, Jeffrey B; Woods, Conan K; Shurgold, Susan; Colley, Guillaume; Brumme, Chanson J; Hogg, Robert S; Montaner, Julio S G; Harrigan, P Richard

2015-03-15

The diversification of human immunodeficiency virus (HIV) is shaped by its transmission history. We therefore used a population based province wide HIV drug resistance database in British Columbia (BC), Canada, to evaluate the impact of clinical, demographic, and behavioral factors on rates of HIV transmission. We reconstructed molecular phylogenies from 27,296 anonymized bulk HIV pol sequences representing 7747 individuals in BC-about half the estimated HIV prevalence in BC. Infections were grouped into clusters based on phylogenetic distances, as a proxy for variation in transmission rates. Rates of cluster expansion were reconstructed from estimated dates of HIV seroconversion. Our criteria grouped 4431 individuals into 744 clusters largely separated with respect to risk factors, including large established clusters predominated by injection drug users and more-recently emerging clusters comprising men who have sex with men. The mean log10 viral load of an individual's phylogenetic neighborhood (composed of 5 other individuals with shortest phylogenetic distances) increased their odds of appearing in a cluster by >2-fold per log10 viruses per milliliter. Hotspots of ongoing HIV transmission can be characterized in near real time by the secondary analysis of HIV resistance genotypes, providing an important potential resource for targeting public health initiatives for HIV prevention. © The Author 2014. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Clustering determines the dynamics of complex contagions in multiplex networks

NASA Astrophysics Data System (ADS)

Zhuang, Yong; Arenas, Alex; Yaǧan, Osman

2017-01-01

We present the mathematical analysis of generalized complex contagions in a class of clustered multiplex networks. The model is intended to understand spread of influence, or any other spreading process implying a threshold dynamics, in setups of interconnected networks with significant clustering. The contagion is assumed to be general enough to account for a content-dependent linear threshold model, where each link type has a different weight (for spreading influence) that may depend on the content (e.g., product, rumor, political view) that is being spread. Using the generating functions formalism, we determine the conditions, probability, and expected size of the emergent global cascades. This analysis provides a generalization of previous approaches and is especially useful in problems related to spreading and percolation. The results present nontrivial dependencies between the clustering coefficient of the networks and its average degree. In particular, several phase transitions are shown to occur depending on these descriptors. Generally speaking, our findings reveal that increasing clustering decreases the probability of having global cascades and their size, however, this tendency changes with the average degree. There exists a certain average degree from which on clustering favors the probability and size of the contagion. By comparing the dynamics of complex contagions over multiplex networks and their monoplex projections, we demonstrate that ignoring link types and aggregating network layers may lead to inaccurate conclusions about contagion dynamics, particularly when the correlation of degrees between layers is high.

Multilevel Analysis of Trachomatous Trichiasis and Corneal Opacity in Nigeria: The Role of Environmental and Climatic Risk Factors on the Distribution of Disease.

PubMed

Smith, Jennifer L; Sivasubramaniam, Selvaraj; Rabiu, Mansur M; Kyari, Fatima; Solomon, Anthony W; Gilbert, Clare

2015-01-01

The distribution of trachoma in Nigeria is spatially heterogeneous, with large-scale trends observed across the country and more local variation within areas. Relative contributions of individual and cluster-level risk factors to the geographic distribution of disease remain largely unknown. The primary aim of this analysis is to assess the relationship between climatic factors and trachomatous trichiasis (TT) and/or corneal opacity (CO) due to trachoma in Nigeria, while accounting for the effects of individual risk factors and spatial correlation. In addition, we explore the relative importance of variation in the risk of trichiasis and/or corneal opacity (TT/CO) at different levels. Data from the 2007 National Blindness and Visual Impairment Survey were used for this analysis, which included a nationally representative sample of adults aged 40 years and above. Complete data were available from 304 clusters selected using a multi-stage stratified cluster-random sampling strategy. All participants (13,543 individuals) were interviewed and examined by an ophthalmologist for the presence or absence of TT and CO. In addition to field-collected data, remotely sensed climatic data were extracted for each cluster and used to fit Bayesian hierarchical logistic models to disease outcome. The risk of TT/CO was associated with factors at both the individual and cluster levels, with approximately 14% of the total variation attributed to the cluster level. Beyond established individual risk factors (age, gender and occupation), there was strong evidence that environmental/climatic factors at the cluster-level (lower precipitation, higher land surface temperature, higher mean annual temperature and rural classification) were also associated with a greater risk of TT/CO. This study establishes the importance of large-scale risk factors in the geographical distribution of TT/CO in Nigeria, supporting anecdotal evidence that environmental conditions are associated with increased risk in this context and highlighting their potential use in improving estimates of disease burden at large scales.

MCAM: multiple clustering analysis methodology for deriving hypotheses and insights from high-throughput proteomic datasets.

PubMed

Naegle, Kristen M; Welsch, Roy E; Yaffe, Michael B; White, Forest M; Lauffenburger, Douglas A

2011-07-01

Advances in proteomic technologies continue to substantially accelerate capability for generating experimental data on protein levels, states, and activities in biological samples. For example, studies on receptor tyrosine kinase signaling networks can now capture the phosphorylation state of hundreds to thousands of proteins across multiple conditions. However, little is known about the function of many of these protein modifications, or the enzymes responsible for modifying them. To address this challenge, we have developed an approach that enhances the power of clustering techniques to infer functional and regulatory meaning of protein states in cell signaling networks. We have created a new computational framework for applying clustering to biological data in order to overcome the typical dependence on specific a priori assumptions and expert knowledge concerning the technical aspects of clustering. Multiple clustering analysis methodology ('MCAM') employs an array of diverse data transformations, distance metrics, set sizes, and clustering algorithms, in a combinatorial fashion, to create a suite of clustering sets. These sets are then evaluated based on their ability to produce biological insights through statistical enrichment of metadata relating to knowledge concerning protein functions, kinase substrates, and sequence motifs. We applied MCAM to a set of dynamic phosphorylation measurements of the ERRB network to explore the relationships between algorithmic parameters and the biological meaning that could be inferred and report on interesting biological predictions. Further, we applied MCAM to multiple phosphoproteomic datasets for the ERBB network, which allowed us to compare independent and incomplete overlapping measurements of phosphorylation sites in the network. We report specific and global differences of the ERBB network stimulated with different ligands and with changes in HER2 expression. Overall, we offer MCAM as a broadly-applicable approach for analysis of proteomic data which may help increase the current understanding of molecular networks in a variety of biological problems. © 2011 Naegle et al.

Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis.

PubMed

Goldbaum, Michael H; Jang, Gil-Jin; Bowd, Chris; Hao, Jiucang; Zangwill, Linda M; Liebmann, Jeffrey; Girkin, Christopher; Jung, Tzyy-Ping; Weinreb, Robert N; Sample, Pamela A

2009-12-01

To determine if the patterns uncovered with variational Bayesian-independent component analysis-mixture model (VIM) applied to a large set of normal and glaucomatous fields obtained with the Swedish Interactive Thresholding Algorithm (SITA) are distinct, recognizable, and useful for modeling the severity of the field loss. SITA fields were obtained with the Humphrey Visual Field Analyzer (Carl Zeiss Meditec, Inc, Dublin, California) on 1,146 normal eyes and 939 glaucoma eyes from subjects followed by the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study. VIM modifies independent component analysis (ICA) to develop separate sets of ICA axes in the cluster of normal fields and the 2 clusters of abnormal fields. Of 360 models, the model with the best separation of normal and glaucomatous fields was chosen for creating the maximally independent axes. Grayscale displays of fields generated by VIM on each axis were compared. SITA fields most closely associated with each axis and displayed in grayscale were evaluated for consistency of pattern at all severities. The best VIM model had 3 clusters. Cluster 1 (1,193) was mostly normal (1,089, 95% specificity) and had 2 axes. Cluster 2 (596) contained mildly abnormal fields (513) and 2 axes; cluster 3 (323) held mostly moderately to severely abnormal fields (322) and 5 axes. Sensitivity for clusters 2 and 3 combined was 88.9%. The VIM-generated field patterns differed from each other and resembled glaucomatous defects (eg, nasal step, arcuate, temporal wedge). SITA fields assigned to an axis resembled each other and the VIM-generated patterns for that axis. Pattern severity increased in the positive direction of each axis by expansion or deepening of the axis pattern. VIM worked well on SITA fields, separating them into distinctly different yet recognizable patterns of glaucomatous field defects. The axis and pattern properties make VIM a good candidate as a preliminary process for detecting progression.

Penicillin production in industrial strain Penicillium chrysogenum P2niaD18 is not dependent on the copy number of biosynthesis genes.

PubMed

Ziemons, Sandra; Koutsantas, Katerina; Becker, Kordula; Dahlmann, Tim; Kück, Ulrich

2017-02-16

Multi-copy gene integration into microbial genomes is a conventional tool for obtaining improved gene expression. For Penicillium chrysogenum, the fungal producer of the beta-lactam antibiotic penicillin, many production strains carry multiple copies of the penicillin biosynthesis gene cluster. This discovery led to the generally accepted view that high penicillin titers are the result of multiple copies of penicillin genes. Here we investigated strain P2niaD18, a production line that carries only two copies of the penicillin gene cluster. We performed pulsed-field gel electrophoresis (PFGE), quantitative qRT-PCR, and penicillin bioassays to investigate production, deletion and overexpression strains generated in the P. chrysogenum P2niaD18 background, in order to determine the copy number of the penicillin biosynthesis gene cluster, and study the expression of one penicillin biosynthesis gene, and the penicillin titer. Analysis of production and recombinant strain showed that the enhanced penicillin titer did not depend on the copy number of the penicillin gene cluster. Our assumption was strengthened by results with a penicillin null strain lacking pcbC encoding isopenicillin N synthase. Reintroduction of one or two copies of the cluster into the pcbC deletion strain restored transcriptional high expression of the pcbC gene, but recombinant strains showed no significantly different penicillin titer compared to parental strains. Here we present a molecular genetic analysis of production and recombinant strains in the P2niaD18 background carrying different copy numbers of the penicillin biosynthesis gene cluster. Our analysis shows that the enhanced penicillin titer does not strictly depend on the copy number of the cluster. Based on these overall findings, we hypothesize that instead, complex regulatory mechanisms are prominently implicated in increased penicillin biosynthesis in production strains.

CLUSFAVOR 5.0: hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles

PubMed Central

Peterson, Leif E

2002-01-01

CLUSFAVOR (CLUSter and Factor Analysis with Varimax Orthogonal Rotation) 5.0 is a Windows-based computer program for hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles. CLUSFAVOR 5.0 standardizes input data; sorts data according to gene-specific coefficient of variation, standard deviation, average and total expression, and Shannon entropy; performs hierarchical cluster analysis using nearest-neighbor, unweighted pair-group method using arithmetic averages (UPGMA), or furthest-neighbor joining methods, and Euclidean, correlation, or jack-knife distances; and performs principal-component analysis. PMID:12184816

DICON: interactive visual analysis of multidimensional clusters.

PubMed

Cao, Nan; Gotz, David; Sun, Jimeng; Qu, Huamin

2011-12-01

Clustering as a fundamental data analysis technique has been widely used in many analytic applications. However, it is often difficult for users to understand and evaluate multidimensional clustering results, especially the quality of clusters and their semantics. For large and complex data, high-level statistical information about the clusters is often needed for users to evaluate cluster quality while a detailed display of multidimensional attributes of the data is necessary to understand the meaning of clusters. In this paper, we introduce DICON, an icon-based cluster visualization that embeds statistical information into a multi-attribute display to facilitate cluster interpretation, evaluation, and comparison. We design a treemap-like icon to represent a multidimensional cluster, and the quality of the cluster can be conveniently evaluated with the embedded statistical information. We further develop a novel layout algorithm which can generate similar icons for similar clusters, making comparisons of clusters easier. User interaction and clutter reduction are integrated into the system to help users more effectively analyze and refine clustering results for large datasets. We demonstrate the power of DICON through a user study and a case study in the healthcare domain. Our evaluation shows the benefits of the technique, especially in support of complex multidimensional cluster analysis. © 2011 IEEE

Live-cell superresolution microscopy reveals the organization of RNA polymerase in the bacterial nucleoid

PubMed Central

Stracy, Mathew; Lesterlin, Christian; Garza de Leon, Federico; Uphoff, Stephan; Zawadzki, Pawel; Kapanidis, Achillefs N.

2015-01-01

Despite the fundamental importance of transcription, a comprehensive analysis of RNA polymerase (RNAP) behavior and its role in the nucleoid organization in vivo is lacking. Here, we used superresolution microscopy to study the localization and dynamics of the transcription machinery and DNA in live bacterial cells, at both the single-molecule and the population level. We used photoactivated single-molecule tracking to discriminate between mobile RNAPs and RNAPs specifically bound to DNA, either on promoters or transcribed genes. Mobile RNAPs can explore the whole nucleoid while searching for promoters, and spend 85% of their search time in nonspecific interactions with DNA. On the other hand, the distribution of specifically bound RNAPs shows that low levels of transcription can occur throughout the nucleoid. Further, clustering analysis and 3D structured illumination microscopy (SIM) show that dense clusters of transcribing RNAPs form almost exclusively at the nucleoid periphery. Treatment with rifampicin shows that active transcription is necessary for maintaining this spatial organization. In faster growth conditions, the fraction of transcribing RNAPs increases, as well as their clustering. Under these conditions, we observed dramatic phase separation between the densest clusters of RNAPs and the densest regions of the nucleoid. These findings show that transcription can cause spatial reorganization of the nucleoid, with movement of gene loci out of the bulk of DNA as levels of transcription increase. This work provides a global view of the organization of RNA polymerase and transcription in living cells. PMID:26224838

Alerts in electronic medical records to promote a colorectal cancer screening programme: a cluster randomised controlled trial in primary care.

PubMed

Guiriguet, Carolina; Muñoz-Ortiz, Laura; Burón, Andrea; Rivero, Irene; Grau, Jaume; Vela-Vallespín, Carmen; Vilarrubí, Mercedes; Torres, Miquel; Hernández, Cristina; Méndez-Boo, Leonardo; Toràn, Pere; Caballeria, Llorenç; Macià, Francesc; Castells, Antoni

2016-07-01

Participation rates in colorectal cancer screening are below recommended European targets. To evaluate the effectiveness of an alert in primary care electronic medical records (EMRs) to increase individuals' participation in an organised, population-based colorectal cancer screening programme when compared with usual care. Cluster randomised controlled trial in primary care centres of Barcelona, Spain. Participants were males and females aged 50-69 years, who were invited to the first round of a screening programme based on the faecal immunochemical test (FIT) (n = 41 042), and their primary care professional. The randomisation unit was the physician cluster (n = 130) and patients were blinded to the study group. The control group followed usual care as per the colorectal cancer screening programme. In the intervention group, as well as usual care, an alert to health professionals (cluster level) to promote screening was introduced in the individual's primary care EMR for 1 year. The main outcome was colorectal cancer screening participation at individual participant level. In total, 67 physicians and 21 619 patients (intervention group) and 63 physicians and 19 423 patients (control group) were randomised. In the intention-to-treat analysis screening participation was 44.1% and 42.2% respectively (odds ratio 1.08, 95% confidence interval [CI] = 0.97 to 1.20, P = 0.146). However, in the per-protocol analysis screening uptake in the intervention group showed a statistically significant increase, after adjusting for potential confounders (OR, 1.11; 95% CI = 1.02 to 1.22; P = 0.018). The use of an alert in an individual's primary care EMR is associated with a statistically significant increased uptake of an organised, FIT-based colorectal cancer screening programme in patients attending primary care centres. © British Journal of General Practice 2016.

Cluster Correspondence Analysis.

PubMed

van de Velden, M; D'Enza, A Iodice; Palumbo, F

2017-03-01

A method is proposed that combines dimension reduction and cluster analysis for categorical data by simultaneously assigning individuals to clusters and optimal scaling values to categories in such a way that a single between variance maximization objective is achieved. In a unified framework, a brief review of alternative methods is provided and we show that the proposed method is equivalent to GROUPALS applied to categorical data. Performance of the methods is appraised by means of a simulation study. The results of the joint dimension reduction and clustering methods are compared with the so-called tandem approach, a sequential analysis of dimension reduction followed by cluster analysis. The tandem approach is conjectured to perform worse when variables are added that are unrelated to the cluster structure. Our simulation study confirms this conjecture. Moreover, the results of the simulation study indicate that the proposed method also consistently outperforms alternative joint dimension reduction and clustering methods.

Towards Effective Clustering Techniques for the Analysis of Electric Power Grids

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

Hogan, Emilie A.; Cotilla Sanchez, Jose E.; Halappanavar, Mahantesh

2013-11-30

Clustering is an important data analysis technique with numerous applications in the analysis of electric power grids. Standard clustering techniques are oblivious to the rich structural and dynamic information available for power grids. Therefore, by exploiting the inherent topological and electrical structure in the power grid data, we propose new methods for clustering with applications to model reduction, locational marginal pricing, phasor measurement unit (PMU or synchrophasor) placement, and power system protection. We focus our attention on model reduction for analysis based on time-series information from synchrophasor measurement devices, and spectral techniques for clustering. By comparing different clustering techniques onmore » two instances of realistic power grids we show that the solutions are related and therefore one could leverage that relationship for a computational advantage. Thus, by contrasting different clustering techniques we make a case for exploiting structure inherent in the data with implications for several domains including power systems.« less

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

PubMed

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

2014-11-01

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

X-ray and optical substructures of the DAFT/FADA survey clusters

NASA Astrophysics Data System (ADS)

Guennou, L.; Durret, F.; Adami, C.; Lima Neto, G. B.

2013-04-01

We have undertaken the DAFT/FADA survey with the double aim of setting constraints on dark energy based on weak lensing tomography and of obtaining homogeneous and high quality data for a sample of 91 massive clusters in the redshift range 0.4-0.9 for which there were HST archive data. We have analysed the XMM-Newton data available for 42 of these clusters to derive their X-ray temperatures and luminosities and search for substructures. Out of these, a spatial analysis was possible for 30 clusters, but only 23 had deep enough X-ray data for a really robust analysis. This study was coupled with a dynamical analysis for the 26 clusters having at least 30 spectroscopic galaxy redshifts in the cluster range. Altogether, the X-ray sample of 23 clusters and the optical sample of 26 clusters have 14 clusters in common. We present preliminary results on the coupled X-ray and dynamical analyses of these 14 clusters.

Identifying novel phenotypes of acute heart failure using cluster analysis of clinical variables.

PubMed

Horiuchi, Yu; Tanimoto, Shuzou; Latif, A H M Mahbub; Urayama, Kevin Y; Aoki, Jiro; Yahagi, Kazuyuki; Okuno, Taishi; Sato, Yu; Tanaka, Tetsu; Koseki, Keita; Komiyama, Kota; Nakajima, Hiroyoshi; Hara, Kazuhiro; Tanabe, Kengo

2018-07-01

Acute heart failure (AHF) is a heterogeneous disease caused by various cardiovascular (CV) pathophysiology and multiple non-CV comorbidities. We aimed to identify clinically important subgroups to improve our understanding of the pathophysiology of AHF and inform clinical decision-making. We evaluated detailed clinical data of 345 consecutive AHF patients using non-hierarchical cluster analysis of 77 variables, including age, sex, HF etiology, comorbidities, physical findings, laboratory data, electrocardiogram, echocardiogram and treatment during hospitalization. Cox proportional hazards regression analysis was performed to estimate the association between the clusters and clinical outcomes. Three clusters were identified. Cluster 1 (n=108) represented "vascular failure". This cluster had the highest average systolic blood pressure at admission and lung congestion with type 2 respiratory failure. Cluster 2 (n=89) represented "cardiac and renal failure". They had the lowest ejection fraction (EF) and worst renal function. Cluster 3 (n=148) comprised mostly older patients and had the highest prevalence of atrial fibrillation and preserved EF. Death or HF hospitalization within 12-month occurred in 23% of Cluster 1, 36% of Cluster 2 and 36% of Cluster 3 (p=0.034). Compared with Cluster 1, risk of death or HF hospitalization was 1.74 (95% CI, 1.03-2.95, p=0.037) for Cluster 2 and 1.82 (95% CI, 1.13-2.93, p=0.014) for Cluster 3. Cluster analysis may be effective in producing clinically relevant categories of AHF, and may suggest underlying pathophysiology and potential utility in predicting clinical outcomes. Copyright © 2018 Elsevier B.V. All rights reserved.

Mixture modelling for cluster analysis.

PubMed

McLachlan, G J; Chang, S U

2004-10-01

Cluster analysis via a finite mixture model approach is considered. With this approach to clustering, the data can be partitioned into a specified number of clusters g by first fitting a mixture model with g components. An outright clustering of the data is then obtained by assigning an observation to the component to which it has the highest estimated posterior probability of belonging; that is, the ith cluster consists of those observations assigned to the ith component (i = 1,..., g). The focus is on the use of mixtures of normal components for the cluster analysis of data that can be regarded as being continuous. But attention is also given to the case of mixed data, where the observations consist of both continuous and discrete variables.

Clusters of Insomnia Disorder: An Exploratory Cluster Analysis of Objective Sleep Parameters Reveals Differences in Neurocognitive Functioning, Quantitative EEG, and Heart Rate Variability

PubMed Central

Miller, Christopher B.; Bartlett, Delwyn J.; Mullins, Anna E.; Dodds, Kirsty L.; Gordon, Christopher J.; Kyle, Simon D.; Kim, Jong Won; D'Rozario, Angela L.; Lee, Rico S.C.; Comas, Maria; Marshall, Nathaniel S.; Yee, Brendon J.; Espie, Colin A.; Grunstein, Ronald R.

2016-01-01

Study Objectives: To empirically derive and evaluate potential clusters of Insomnia Disorder through cluster analysis from polysomnography (PSG). We hypothesized that clusters would differ on neurocognitive performance, sleep-onset measures of quantitative (q)-EEG and heart rate variability (HRV). Methods: Research volunteers with Insomnia Disorder (DSM-5) completed a neurocognitive assessment and overnight PSG measures of total sleep time (TST), wake time after sleep onset (WASO), and sleep onset latency (SOL) were used to determine clusters. Results: From 96 volunteers with Insomnia Disorder, cluster analysis derived at least two clusters from objective sleep parameters: Insomnia with normal objective sleep duration (I-NSD: n = 53) and Insomnia with short sleep duration (I-SSD: n = 43). At sleep onset, differences in HRV between I-NSD and I-SSD clusters suggest attenuated parasympathetic activity in I-SSD (P < 0.05). Preliminary work suggested three clusters by retaining the I-NSD and splitting the I-SSD cluster into two: I-SSD A (n = 29): defined by high WASO and I-SSD B (n = 14): a second I-SSD cluster with high SOL and medium WASO. The I-SSD B cluster performed worse than I-SSD A and I-NSD for sustained attention (P ≤ 0.05). In an exploratory analysis, q-EEG revealed reduced spectral power also in I-SSD B before (Delta, Alpha, Beta-1) and after sleep-onset (Beta-2) compared to I-SSD A and I-NSD (P ≤ 0.05). Conclusions: Two insomnia clusters derived from cluster analysis differ in sleep onset HRV. Preliminary data suggest evidence for three clusters in insomnia with differences for sustained attention and sleep-onset q-EEG. Clinical Trial Registration: Insomnia 100 sleep study: Australia New Zealand Clinical Trials Registry (ANZCTR) identification number 12612000049875. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=347742. Citation: Miller CB, Bartlett DJ, Mullins AE, Dodds KL, Gordon CJ, Kyle SD, Kim JW, D'Rozario AL, Lee RS, Comas M, Marshall NS, Yee BJ, Espie CA, Grunstein RR. Clusters of Insomnia Disorder: an exploratory cluster analysis of objective sleep parameters reveals differences in neurocognitive functioning, quantitative EEG, and heart rate variability. SLEEP 2016;39(11):1993–2004. PMID:27568796

Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis

PubMed Central

Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao

2015-01-01

Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383

Association between differential gene expression and body mass index among endometrial cancers from The Cancer Genome Atlas Project.

PubMed

Roque, Dario R; Makowski, Liza; Chen, Ting-Huei; Rashid, Naim; Hayes, D Neil; Bae-Jump, Victoria

2016-08-01

The Cancer Genome Atlas (TCGA) identified four integrated clusters for endometrial cancer (EC): POLE, MSI, CNL and CNH. We evaluated differences in gene expression profiles of obese and non-obese women with EC and examined the association of body mass index (BMI) within the clusters identified in TCGA. TCGA RNAseq data was used to identify genes related to increasing BMI among ECs. The POLE, MSI and CNL clusters were composed mostly of endometrioid EC. Patient BMI was compared between these three clusters with one-way ANOVA. Association between gene expression and BMI was also assessed while adjusting for confounding effects of potential confounding factors. p-Values testing the association between gene expression and BMI were adjusted for multiple hypothesis testing over the 20,531 genes considered. Mean BMI was statistically different between the ECs in the CNL (35.8) versus POLE (29.8) cluster (p=0.006) and approached significance for the MSI (33.0) versus CNL (35.8) cluster (p=0.05). 181 genes were significantly up- or down-regulated with increasing BMI in endometrioid EC (q-value<0.01), including LPL, IRS-1, IGFBP4, IGFBP7 and the progesterone receptor. DAVID functional annotation analysis revealed significant enrichment in "cell cycle" (adjusted p-value=1.5E-5) and "DNA metabolic processes" (adjusted p-value=1E-3) for the identified genes. Obesity related genes were found to be upregulated with increasing BMI among endometrioid ECs. Patients with POLE tumors have the lowest median BMI when compared to MSI and CNL. Given the heterogeneity among endometrioid EC, consideration should be given to abandoning the Type I and II classification of EC tumors. Copyright © 2016 Elsevier Inc. All rights reserved.

Food choice and food consumption frequency for Uruguayan consumers.

PubMed

Ares, Gastón; Gámbaro, Adriana

2008-05-01

The aims of the present work were to study motives underlying Uruguayan consumers' food choice behaviour and to study the consumption frequency of some selected food items. A modification of the Food Choice Questionnaire and a food frequency questionnaire was administered to a group of 200 Uruguayan consumers. Feeling good and safety, sensory appeal and health and nutrient content were rated as the most important factors, while familiarity was rated as the least important. Using hierarchical cluster analysis, three clusters with different choice patterns were identified. Frequency of consumption of fruits, vegetables, milk and dairy products, and whole cereals, increased as the importance attributed to health and nutrition increased; consumption of fatty foods decreased.

A population-based study of prevalence trends and geospatial analysis of hypospadias and cryptorchidism compared with non-endocrine mediated congenital anomalies.

PubMed

Lane, Ciaran; Boxall, James; MacLellan, Dawn; Anderson, Peter A; Dodds, Linda; Romao, Rodrigo L P

2017-06-01

Several reports have suggested an increase in the prevalence of hypospadias and cryptorchidism over the last few decades. Endocrine disruption caused by exposure to environmental chemicals has been postulated as a possible cause. The objectives of our study were: 1) to determine whether the prevalence of hypospadias and cryptorchidism is increasing compared with other congenital anomalies not known to be mediated by endocrine factors; and 2) to perform a geospatial analysis of these congenital malformations looking for clustering that could offer insight into environmental risk factors. Data were obtained from the Nova Scotia ATLEE Perinatal Database containing the perinatal records of all live births in Nova Scotia, Canada since 1988. Records from 1988 to 2013 defined the study cohort. Overall prevalence rates and prevalence trends by year were calculated for hypospadias, cryptorchidism, gastroschisis, and clubfoot. County of residence was collected and spatial autocorrelation testing for clustering was performed for each of the congenital anomalies. There were 258,147 live births during the study period. Overall prevalence rates for the four malformations over the study period were: hypospadias 78 per 10,000 male births, cryptorchidism 75 per 10,000 male births, clubfoot 24 per 10,000 total births, and gastroschisis 4 per 10,000 total births. Incidence rate ratios per year for hypospadias, cryptorchidism, clubfoot, and gastroschisis were 1.00 (0.99-1.01), 0.99 (0.98-1.00), 0.98 (0.97-0.99), and 1.04 (1.04-1.07), respectively. During the study period, the prevalence rates in the region were unchanged for hypospadias, slightly reduced for cryptorchidism and clubfoot, and rising for gastroschisis (Figure). Spatial autocorrelation testing revealed statistically significant clustering for hypospadias (p = 0.03) and cryptorchidism (p = 0.03), while no spatial autocorrelation was observed for the other malformations. Contrary to previous studies we show that hypospadias and cryptorchidism prevalence rates are not increasing over time in our region. Nonetheless, rates for these conditions in our area are high compared with other regions of the world. Local clustering of these congenital anomalies without clustering of the control, non-endocrine mediated congenital malformations supports a possible unique spatial distribution associated with environmental exposure. The hotspots identified for hypospadias and cryptorchidism are associated with intense agricultural activity. Our study found no increase in hypospadias and cryptorchidism prevalence over a 26-year period compared with other congenital anomalies not known to be associated with endocrine factors. Geospatial analysis supports high clustering for hypospadias and cryptorchidism in areas of intense agricultural activity. Copyright © 2017 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.

Characterization of electrically-active defects in ultraviolet light-emitting diodes with laser-based failure analysis techniques

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

Miller, Mary A.; Tangyunyong, Paiboon; Cole, Edward I.

2016-01-14

Laser-based failure analysis techniques demonstrate the ability to quickly and non-intrusively screen deep ultraviolet light-emitting diodes (LEDs) for electrically-active defects. In particular, two laser-based techniques, light-induced voltage alteration and thermally-induced voltage alteration, generate applied voltage maps (AVMs) that provide information on electrically-active defect behavior including turn-on bias, density, and spatial location. Here, multiple commercial LEDs were examined and found to have dark defect signals in the AVM indicating a site of reduced resistance or leakage through the diode. The existence of the dark defect signals in the AVM correlates strongly with an increased forward-bias leakage current. This increased leakage ismore » not present in devices without AVM signals. Transmission electron microscopy analysis of a dark defect signal site revealed a dislocation cluster through the pn junction. The cluster included an open core dislocation. Even though LEDs with few dark AVM defect signals did not correlate strongly with power loss, direct association between increased open core dislocation densities and reduced LED device performance has been presented elsewhere [M. W. Moseley et al., J. Appl. Phys. 117, 095301 (2015)].« less

A hierarchical cluster analysis of normal-tension glaucoma using spectral-domain optical coherence tomography parameters.

PubMed

Bae, Hyoung Won; Ji, Yongwoo; Lee, Hye Sun; Lee, Naeun; Hong, Samin; Seong, Gong Je; Sung, Kyung Rim; Kim, Chan Yun

2015-01-01

Normal-tension glaucoma (NTG) is a heterogenous disease, and there is still controversy about subclassifications of this disorder. On the basis of spectral-domain optical coherence tomography (SD-OCT), we subdivided NTG with hierarchical cluster analysis using optic nerve head (ONH) parameters and retinal nerve fiber layer (RNFL) thicknesses. A total of 200 eyes of 200 NTG patients between March 2011 and June 2012 underwent SD-OCT scans to measure ONH parameters and RNFL thicknesses. We classified NTG into homogenous subgroups based on these variables using a hierarchical cluster analysis, and compared clusters to evaluate diverse NTG characteristics. Three clusters were found after hierarchical cluster analysis. Cluster 1 (62 eyes) had the thickest RNFL and widest rim area, and showed early glaucoma features. Cluster 2 (60 eyes) was characterized by the largest cup/disc ratio and cup volume, and showed advanced glaucomatous damage. Cluster 3 (78 eyes) had small disc areas in SD-OCT and were comprised of patients with significantly younger age, longer axial length, and greater myopia than the other 2 groups. A hierarchical cluster analysis of SD-OCT scans divided NTG patients into 3 groups based upon ONH parameters and RNFL thicknesses. It is anticipated that the small disc area group comprised of younger and more myopic patients may show unique features unlike the other 2 groups.

Bacterial community comparisons by taxonomy-supervised analysis independent of sequence alignment and clustering

PubMed Central

Sul, Woo Jun; Cole, James R.; Jesus, Ederson da C.; Wang, Qiong; Farris, Ryan J.; Fish, Jordan A.; Tiedje, James M.

2011-01-01

High-throughput sequencing of 16S rRNA genes has increased our understanding of microbial community structure, but now even higher-throughput methods to the Illumina scale allow the creation of much larger datasets with more samples and orders-of-magnitude more sequences that swamp current analytic methods. We developed a method capable of handling these larger datasets on the basis of assignment of sequences into an existing taxonomy using a supervised learning approach (taxonomy-supervised analysis). We compared this method with a commonly used clustering approach based on sequence similarity (taxonomy-unsupervised analysis). We sampled 211 different bacterial communities from various habitats and obtained ∼1.3 million 16S rRNA sequences spanning the V4 hypervariable region by pyrosequencing. Both methodologies gave similar ecological conclusions in that β-diversity measures calculated by using these two types of matrices were significantly correlated to each other, as were the ordination configurations and hierarchical clustering dendrograms. In addition, our taxonomy-supervised analyses were also highly correlated with phylogenetic methods, such as UniFrac. The taxonomy-supervised analysis has the advantages that it is not limited by the exhaustive computation required for the alignment and clustering necessary for the taxonomy-unsupervised analysis, is more tolerant of sequencing errors, and allows comparisons when sequences are from different regions of the 16S rRNA gene. With the tremendous expansion in 16S rRNA data acquisition underway, the taxonomy-supervised approach offers the potential to provide more rapid and extensive community comparisons across habitats and samples. PMID:21873204

Cluster analysis of spontaneous preterm birth phenotypes identifies potential associations among preterm birth mechanisms.

PubMed

Esplin, M Sean; Manuck, Tracy A; Varner, Michael W; Christensen, Bryce; Biggio, Joseph; Bukowski, Radek; Parry, Samuel; Zhang, Heping; Huang, Hao; Andrews, William; Saade, George; Sadovsky, Yoel; Reddy, Uma M; Ilekis, John

2015-09-01

We sought to use an innovative tool that is based on common biologic pathways to identify specific phenotypes among women with spontaneous preterm birth (SPTB) to enhance investigators' ability to identify and to highlight common mechanisms and underlying genetic factors that are responsible for SPTB. We performed a secondary analysis of a prospective case-control multicenter study of SPTB. All cases delivered a preterm singleton at SPTB ≤34.0 weeks' gestation. Each woman was assessed for the presence of underlying SPTB causes. A hierarchic cluster analysis was used to identify groups of women with homogeneous phenotypic profiles. One of the phenotypic clusters was selected for candidate gene association analysis with the use of VEGAS software. One thousand twenty-eight women with SPTB were assigned phenotypes. Hierarchic clustering of the phenotypes revealed 5 major clusters. Cluster 1 (n = 445) was characterized by maternal stress; cluster 2 (n = 294) was characterized by premature membrane rupture; cluster 3 (n = 120) was characterized by familial factors, and cluster 4 (n = 63) was characterized by maternal comorbidities. Cluster 5 (n = 106) was multifactorial and characterized by infection (INF), decidual hemorrhage (DH), and placental dysfunction (PD). These 3 phenotypes were correlated highly by χ(2) analysis (PD and DH, P < 2.2e-6; PD and INF, P = 6.2e-10; INF and DH, (P = .0036). Gene-based testing identified the INS (insulin) gene as significantly associated with cluster 3 of SPTB. We identified 5 major clusters of SPTB based on a phenotype tool and hierarch clustering. There was significant correlation between several of the phenotypes. The INS gene was associated with familial factors that were underlying SPTB. Copyright © 2015 Elsevier Inc. All rights reserved.

Cluster analysis of the hot subdwarfs in the PG survey

NASA Technical Reports Server (NTRS)

Thejll, Peter; Charache, Darryl; Shipman, Harry L.

1989-01-01

Application of cluster analysis to the hot subdwarfs in the Palomar Green (PG) survey of faint blue high-Galactic-latitude objects is assessed, with emphasis on data noise and the number of clusters to subdivide the data into. The data used in the study are presented, and cluster analysis, using the CLUSTAN program, is applied to it. Distances are calculated using the Euclidean formula, and clustering is done by Ward's method. The results are discussed, and five groups representing natural divisions of the subdwarfs in the PG survey are presented.

Using Machine Learning Techniques in the Analysis of Oceanographic Data

NASA Astrophysics Data System (ADS)

Falcinelli, K. E.; Abuomar, S.

2017-12-01

Acoustic Doppler Current Profilers (ADCPs) are oceanographic tools capable of collecting large amounts of current profile data. Using unsupervised machine learning techniques such as principal component analysis, fuzzy c-means clustering, and self-organizing maps, patterns and trends in an ADCP dataset are found. Cluster validity algorithms such as visual assessment of cluster tendency and clustering index are used to determine the optimal number of clusters in the ADCP dataset. These techniques prove to be useful in analysis of ADCP data and demonstrate potential for future use in other oceanographic applications.

Atmospheric effects on cluster analyses. [for remote sensing application

NASA Technical Reports Server (NTRS)

Kiang, R. K.

1979-01-01

Ground reflected radiance, from which information is extracted through techniques of cluster analyses for remote sensing application, is altered by the atmosphere when it reaches the satellite. Therefore it is essential to understand the effects of the atmosphere on Landsat measurements, cluster characteristics and analysis accuracy. A doubling model is employed to compute the effective reflectivity, observed from the satellite, as a function of ground reflectivity, solar zenith angle and aerosol optical thickness for standard atmosphere. The relation between the effective reflectivity and ground reflectivity is approximately linear. It is shown that for a horizontally homogeneous atmosphere, the classification statistics from a maximum likelihood classifier remains unchanged under these transforms. If inhomogeneity is present, the divergence between clusters is reduced, and correlation between spectral bands increases. Radiance reflected by the background area surrounding the target may also reach the satellite. The influence of background reflectivity on effective reflectivity is discussed.

Do 'environmental bads' such as alcohol, fast food, tobacco, and gambling outlets cluster and co-locate in more deprived areas in Glasgow City, Scotland?

PubMed

Macdonald, Laura; Olsen, Jonathan R; Shortt, Niamh K; Ellaway, Anne

2018-05-01

This study utilised an innovative application of spatial cluster analysis to examine the socio-spatial patterning of outlets selling potentially health-damaging goods/services, such as alcohol, fast food, tobacco and gambling, within Glasgow City, Scotland. For all categories of outlets combined, numbers of clusters increased linearly from the least to the most income deprived areas (i.e. one cluster within the least deprived quintile to ten within the most deprived quintile). Co-location of individual types of outlets (alcohol, fast food, tobacco and gambling) within similar geographical areas was also evident. This type of research could influence interventions to tackle the co-occurrence of unhealthy behaviours and contribute to policies tackling higher numbers of 'environmental bads' within deprived areas. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

Modularization of biochemical networks based on classification of Petri net t-invariants.

PubMed

Grafahrend-Belau, Eva; Schreiber, Falk; Heiner, Monika; Sackmann, Andrea; Junker, Björn H; Grunwald, Stefanie; Speer, Astrid; Winder, Katja; Koch, Ina

2008-02-08

Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system. Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied. We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in Saccharomyces cerevisiae) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability. We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.

Modularization of biochemical networks based on classification of Petri net t-invariants

PubMed Central

Grafahrend-Belau, Eva; Schreiber, Falk; Heiner, Monika; Sackmann, Andrea; Junker, Björn H; Grunwald, Stefanie; Speer, Astrid; Winder, Katja; Koch, Ina

2008-01-01

Background Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior. With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system. Methods Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied. Results We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in Saccharomyces cerevisiae) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability. Conclusion We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis. PMID:18257938

Elemental Abundances in the Intracluster Gas and the Hot Galactic Coronae in Cluster A194

NASA Technical Reports Server (NTRS)

Forman, William R.

1997-01-01

We have completed the analysis of observations of the Coma cluster and are continuing analysis of A1367 both of which are shown to be merging clusters. Also, we are analyzing observations of the Centaurus cluster which we see as a merger based in both its temperature and surface brightness distributions. Attachment: Another collision for the coma cluster.

A Cluster of Legionella-Associated Pneumonia Cases in a Population of Military Recruits

DTIC Science & Technology

2007-06-01

this cluster may suggest a previously unrecognized suscep- FIG. 1. Phylogenic analysis of the training center strain (represented by the MCRD consensus...military recruits during population- based surveillance for pneumonia pathogens. Results were confirmed by sequence analysis . Cases cluster tightly...17 April 2007 A Legionella cluster was identified through retrospective PCR analysis of 240 throat swab samples from X-ray-confirmed pneumonia cases

Spatial analysis of malaria in Anhui province, China

PubMed Central

Zhang, Wenyi; Wang, Liping; Fang, Liqun; Ma, Jiaqi; Xu, Youfu; Jiang, Jiafu; Hui, Fengming; Wang, Jianjun; Liang, Song; Yang, Hong; Cao, Wuchun

2008-01-01

Background Malaria has re-emerged in Anhui Province, China, and this province was the most seriously affected by malaria during 2005–2006. It is necessary to understand the spatial distribution of malaria cases and to identify highly endemic areas for future public health planning and resource allocation in Anhui Province. Methods The annual average incidence at the county level was calculated using malaria cases reported between 2000 and 2006 in Anhui Province. GIS-based spatial analyses were conducted to detect spatial distribution and clustering of malaria incidence at the county level. Results The spatial distribution of malaria cases in Anhui Province from 2000 to 2006 was mapped at the county level to show crude incidence, excess hazard and spatial smoothed incidence. Spatial cluster analysis suggested 10 and 24 counties were at increased risk for malaria (P < 0.001) with the maximum spatial cluster sizes at < 50% and < 25% of the total population, respectively. Conclusion The application of GIS, together with spatial statistical techniques, provide a means to quantify explicit malaria risks and to further identify environmental factors responsible for the re-emerged malaria risks. Future public health planning and resource allocation in Anhui Province should be focused on the maximum spatial cluster region. PMID:18847489

Demographic characterization and spatial cluster analysis of human Salmonella 1,4,[5],12:i:- infections in Portugal: A 10year study.

PubMed

Seixas, R; Nunes, T; Machado, J; Tavares, L; Owen, S P; Bernardo, F; Oliveira, M

Salmonella 1,4,[5],12:i:- is presently considered one of the major serovars responsible for human salmonellosis worldwide. Due to its recent emergence, studies assessing the demographic characterization and spatial epidemiology of salmonellosis 1,4,[5],12:i:- at local- or country-level are lacking. In this study, a analysis was conducted over a 10year period, from 2000 to the first quarter of 2011 at the Portuguese National Laboratory in Portugal mainland, with a total of 215 Salmonella 1,4,[5],12:i:- serotyped isolates obtained from human infections by a passive surveillance system. Data regarding source, year and month of sampling, gender, age, district and municipality of the patients were registered. Descriptive statistical analysis and a spatial scan statistic combined with a geographic information system were employed to characterize the epidemiology and identify spatial clusters. Results showed that most districts have reports of Salmonella 1,4,[5],12:i:-, with a higher number of cases at the Portuguese coastland, including districts like Porto (n=60, 27.9%), Lisboa (n=29, 13.5%) and Aveiro (n=28, 13.0%). An increased incidence was observed in the period from 2004 to 2011 and most infections occurred during May and October. Spatial analysis revealed 4 clusters of higher than expected infection rates. Three were located in the north of Portugal, including two at the coastland (Cluster 1 [RR=3.58, p≤0.001] and 4 [RR=10.42 p≤0.230]), and one at the countryside (Cluster 3 [RR=17.76, p≤0.001]). A larger cluster was detected involving the center and south of Portugal (Cluster 2 [RR=4.85, p≤0.001]). The present study was elaborated with data provided by a passive surveillance system, which may originate an underestimation of disease burden. However, this is the first report describing the incidence and the distribution of areas with higher risk of infection in Portugal, revealing that Salmonella 1,4,[5],12:i:- displayed a significant geographic clustering and these areas should be further evaluated to identify risk factors in order to establish prevention programs. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

A scoping review of spatial cluster analysis techniques for point-event data.

PubMed

Fritz, Charles E; Schuurman, Nadine; Robertson, Colin; Lear, Scott

2013-05-01

Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

cluster trials v. 1.0

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

Mitchell, John; Castillo, Andrew

2016-09-21

This software contains a set of python modules – input, search, cluster, analysis; these modules read input files containing spatial coordinates and associated attributes which can be used to perform nearest neighbor search (spatial indexing via kdtree), cluster analysis/identification, and calculation of spatial statistics for analysis.

Patterns of comorbidity in community-dwelling older people hospitalised for fall-related injury: A cluster analysis

PubMed Central

2011-01-01

Background Community-dwelling older people aged 65+ years sustain falls frequently; these can result in physical injuries necessitating medical attention including emergency department care and hospitalisation. Certain health conditions and impairments have been shown to contribute independently to the risk of falling or experiencing a fall injury, suggesting that individuals with these conditions or impairments should be the focus of falls prevention. Since older people commonly have multiple conditions/impairments, knowledge about which conditions/impairments coexist in at-risk individuals would be valuable in the implementation of a targeted prevention approach. The objective of this study was therefore to examine the prevalence and patterns of comorbidity in this population group. Methods We analysed hospitalisation data from Victoria, Australia's second most populous state, to estimate the prevalence of comorbidity in patients hospitalised at least once between 2005-6 and 2007-8 for treatment of acute fall-related injuries. In patients with two or more comorbid conditions (multicomorbidity) we used an agglomerative hierarchical clustering method to cluster comorbidity variables and identify constellations of conditions. Results More than one in four patients had at least one comorbid condition and among patients with comorbidity one in three had multicomorbidity (range 2-7). The prevalence of comorbidity varied by gender, age group, ethnicity and injury type; it was also associated with a significant increase in the average cumulative length of stay per patient. The cluster analysis identified five distinct, biologically plausible clusters of comorbidity: cardiopulmonary/metabolic, neurological, sensory, stroke and cancer. The cardiopulmonary/metabolic cluster was the largest cluster among the clusters identified. Conclusions The consequences of comorbidity clustering in terms of falls and/or injury outcomes of hospitalised patients should be investigated by future studies. Our findings have particular relevance for falls prevention strategies, clinical practice and planning of follow-up services for these patients. PMID:21851627

Patterns of comorbidity in community-dwelling older people hospitalised for fall-related injury: a cluster analysis.

PubMed

Vu, Trang; Finch, Caroline F; Day, Lesley

2011-08-18

Community-dwelling older people aged 65+ years sustain falls frequently; these can result in physical injuries necessitating medical attention including emergency department care and hospitalisation. Certain health conditions and impairments have been shown to contribute independently to the risk of falling or experiencing a fall injury, suggesting that individuals with these conditions or impairments should be the focus of falls prevention. Since older people commonly have multiple conditions/impairments, knowledge about which conditions/impairments coexist in at-risk individuals would be valuable in the implementation of a targeted prevention approach. The objective of this study was therefore to examine the prevalence and patterns of comorbidity in this population group. We analysed hospitalisation data from Victoria, Australia's second most populous state, to estimate the prevalence of comorbidity in patients hospitalised at least once between 2005-6 and 2007-8 for treatment of acute fall-related injuries. In patients with two or more comorbid conditions (multicomorbidity) we used an agglomerative hierarchical clustering method to cluster comorbidity variables and identify constellations of conditions. More than one in four patients had at least one comorbid condition and among patients with comorbidity one in three had multicomorbidity (range 2-7). The prevalence of comorbidity varied by gender, age group, ethnicity and injury type; it was also associated with a significant increase in the average cumulative length of stay per patient. The cluster analysis identified five distinct, biologically plausible clusters of comorbidity: cardiopulmonary/metabolic, neurological, sensory, stroke and cancer. The cardiopulmonary/metabolic cluster was the largest cluster among the clusters identified. The consequences of comorbidity clustering in terms of falls and/or injury outcomes of hospitalised patients should be investigated by future studies. Our findings have particular relevance for falls prevention strategies, clinical practice and planning of follow-up services for these patients.

Drug Prevention by Increasing Self-Esteem: Influence of Teaching Approaches and Gender on Different Consumption Groups

ERIC Educational Resources Information Center

Heyne, Thomas; Bogner, Franz X.

2013-01-01

Our study focused on an educational intervention designed to increase the self-esteem of low-achieving eighth graders. The intervention was a substance-specific life skills program built upon teacher-centered versus student-centered teaching methods. A cluster analysis identified four consumption groups prior to the intervention: A potentially…

Detecting hybridization between Iranian wild wolf (Canis lupus pallipes) and free-ranging domestic dog (Canis familiaris) by analysis of microsatellite markers.

PubMed

Khosravi, Rasoul; Rezaei, Hamid Reza; Kaboli, Mohammad

2013-01-01

The genetic threat due to hybridization with free-ranging dogs is one major concern in wolf conservation. The identification of hybrids and extent of hybridization is important in the conservation and management of wolf populations. Genetic variation was analyzed at 15 unlinked loci in 28 dogs, 28 wolves, four known hybrids, two black wolves, and one dog with abnormal traits in Iran. Pritchard's model, multivariate ordination by principal component analysis and neighbor joining clustering were used for population clustering and individual assignment. Analysis of genetic variation showed that genetic variability is high in both wolf and dog populations in Iran. Values of H(E) in dog and wolf samples ranged from 0.75-0.92 and 0.77-0.92, respectively. The results of AMOVA showed that the two groups of dog and wolf were significantly different (F(ST) = 0.05 and R(ST) = 0.36; P < 0.001). In each of the three methods, wolf and dog samples were separated into two distinct clusters. Two dark wolves were assigned to the wolf cluster. Also these models detected D32 (dog with abnormal traits) and some other samples, which were assigned to more than one cluster and could be a hybrid. This study is the beginning of a genetic study in wolf populations in Iran, and our results reveal that as in other countries, hybridization between wolves and dogs is sporadic in Iran and can be a threat to wolf populations if human perturbations increase.

Soil chemistry and pollution study of a closed landfill site at Ampar Tenang, Selangor, Malaysia.

PubMed

Mohd Adnan, Siti Nur Syahirah Binti; Yusoff, Sumiani; Piaw, Chua Yan

2013-06-01

A total of 20 landfills are located in State of Selangor, Malaysia. This includes the Ampar Tenang landfill site, which was closed on 26 January 2010. It was reported that the landfill has been upgraded to a level I type of sanitary classification. However, the dumpsite area is not being covered according to the classification. In addition, municipal solid waste was dumped directly on top of the unlined natural alluvium formation. This does not only contaminate surface and subsurface soils, but also initiates the potential risk of groundwater pollution. Based on previous studies, the Ampar Tenang soil has been proven to no longer be capable of preventing pollution migration. In this study, metal concentrations of soil samples up to 30 m depth were analyzed based on statistical analysis. It is very significant because research of this type has not been carried out before. The subsurface soils were significantly polluted by arsenic (As), lead (Pb), iron (Fe), copper (Cu) and aluminium (Al). As and Pb exceeded the safe limit values of 5.90 mg/kg and 31.00 mg/kg, respectively, based on Provincial Sediment Quality Guidelines for Metals and the Interim Sediment Quality Values. Furthermore, only Cu concentrations showed a significantly decreasing trend with increasing depth. Most metals were found on clay-type soils based on the cluster analysis method. Moreover, the analysis also differentiates two clusters: cluster I-Pb, As, zinc, Cu, manganese, calcium, sodium, magnesium, potassium and Fe; cluster II-Al. Different clustering may suggest a different contamination source of metals.

Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers

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

Sreepathi, Sarat; Kumar, Jitendra; Mills, Richard T.

A proliferation of data from vast networks of remote sensing platforms (satellites, unmanned aircraft systems (UAS), airborne etc.), observational facilities (meteorological, eddy covariance etc.), state-of-the-art sensors, and simulation models offer unprecedented opportunities for scientific discovery. Unsupervised classification is a widely applied data mining approach to derive insights from such data. However, classification of very large data sets is a complex computational problem that requires efficient numerical algorithms and implementations on high performance computing (HPC) platforms. Additionally, increasing power, space, cooling and efficiency requirements has led to the deployment of hybrid supercomputing platforms with complex architectures and memory hierarchies like themore » Titan system at Oak Ridge National Laboratory. The advent of such accelerated computing architectures offers new challenges and opportunities for big data analytics in general and specifically, large scale cluster analysis in our case. Although there is an existing body of work on parallel cluster analysis, those approaches do not fully meet the needs imposed by the nature and size of our large data sets. Moreover, they had scaling limitations and were mostly limited to traditional distributed memory computing platforms. We present a parallel Multivariate Spatio-Temporal Clustering (MSTC) technique based on k-means cluster analysis that can target hybrid supercomputers like Titan. We developed a hybrid MPI, CUDA and OpenACC implementation that can utilize both CPU and GPU resources on computational nodes. We describe performance results on Titan that demonstrate the scalability and efficacy of our approach in processing large ecological data sets.« less

Transcriptomic analysis of neuregulin-1 regulated genes following ischemic stroke by computational identification of promoter binding sites: A role for the ETS-1 transcription factor.

PubMed

Surles-Zeigler, Monique C; Li, Yonggang; Distel, Timothy J; Omotayo, Hakeem; Ge, Shaokui; Ford, Byron D

2018-01-01

Ischemic stroke is a major cause of mortality in the United States. We previously showed that neuregulin-1 (NRG1) was neuroprotective in rat models of ischemic stroke. We used gene expression profiling to understand the early cellular and molecular mechanisms of NRG1's effects after the induction of ischemia. Ischemic stroke was induced by middle cerebral artery occlusion (MCAO). Rats were allocated to 3 groups: (1) control, (2) MCAO and (3) MCAO + NRG1. Cortical brain tissues were collected three hours following MCAO and NRG1 treatment and subjected to microarray analysis. Data and statistical analyses were performed using R/Bioconductor platform alongside Genesis, Ingenuity Pathway Analysis and Enrichr software packages. There were 2693 genes differentially regulated following ischemia and NRG1 treatment. These genes were organized by expression patterns into clusters using a K-means clustering algorithm. We further analyzed genes in clusters where ischemia altered gene expression, which was reversed by NRG1 (clusters 4 and 10). NRG1, IRS1, OPA3, and POU6F1 were central linking (node) genes in cluster 4. Conserved Transcription Factor Binding Site Finder (CONFAC) identified ETS-1 as a potential transcriptional regulator of NRG1 suppressed genes following ischemia. A transcription factor activity array showed that ETS-1 activity was increased 2-fold, 3 hours following ischemia and this activity was attenuated by NRG1. These findings reveal key early transcriptional mechanisms associated with neuroprotection by NRG1 in the ischemic penumbra.

Classification and discrimination of pediatric patients undergoing open heart surgery with and without methylprednisolone treatment by cytomics

NASA Astrophysics Data System (ADS)

Bocsi, Jozsef; Mittag, Anja; Pierzchalski, Arkadiusz; Osmancik, Pavel; Dähnert, Ingo; Tárnok, Attila

2011-02-01

Introduction: Methylprednisolone (MP) is frequently preoperatively administered in children undergoing open heart surgery. The aim of this medication is to inhibit overshooting immune responses. Earlier studies demonstrated cellular and humoral immunological changes in pediatric patients undergoing heart surgeries with and without MP administration. Here in a retrospective study we investigated the modulation of the cellular immune response by MP. The aim was to identify suitable parameters characterizing MP effects by cluster analysis. Methods: Blood samples were analysed from two aged matched groups with surgical correction of septum defects. Group without MP treatment consisted of 10 patients; MP was administered on 21 patients (median dose: 11mg/kg) before cardiopulmonary bypass (CPB). EDTA anticoagulated blood was obtained 24 h preoperatively, after anesthesia, at CPB begin and end (CPB2), 4h, 24h, 48h after surgery, at discharge and at out-patient followup (8.2; 3.3-12.2 month after surgery; median and IQR). Flow cytometry showed the biggest MP relevant changes at CPB2 and 4h postoperatively. They were used for clustering analysis. Classification was made by discriminant analysis and cluster analysis by means of Genes@work software. Results & conclusion: 146 parameters were obtained from analysis. Cross-validation revealed several parameters being able to discriminate between MP groups and to identify immune modulation. MP administration resulted in a delayed activation of monocytes, increased ratio of neutrophils, reduced T-lymphocytes counts. Cluster analysis demonstrated that classification of patients is possible based on the identified cytomics parameters. Further investigation of these parameters might help to understand the MP effects in pediatric open heart surgery.

Formation of multiply charged ions from large molecules using massive-cluster impact.

PubMed

Mahoney, J F; Cornett, D S; Lee, T D

1994-05-01

Massive-cluster impact is demonstrated to be an effective ionization technique for the mass analysis of proteins as large as 17 kDa. The design of the cluster source permits coupling to both magnetic-sector and quadrupole mass spectrometers. Mass spectra are characterized by the almost total absence of chemical background and a predominance of multiply charged ions formed from 100% glycerol matrix. The number of charge states produced by the technique is observed to range from +3 to +9 for chicken egg lysozyme (14,310 Da). The lower m/z values provided by higher charge states increase the effective mass range of analyses performed with conventional ionization by fast-atom bombardment or liquid secondary ion mass spectrometry.

Spiral Arm Morphology in Cluster Environment

NASA Astrophysics Data System (ADS)

Choi, Isaac Yeoun-Gyu; Ann, Hong Bae

2011-10-01

We examine the dependence of the morphology of spiral galaxies on the environment using the KIAS Value Added Galaxy Catalog (VAGC) which is derived from the Sloan Digital Sky Survey (SDSS) DR7. Our goal is to understand whether the local environment or global conditions dominate in determining the morphology of spiral galaxies. For the analysis, we conduct a morphological classification of galaxies in 20 X-ray selected Abell clusters up to z˜0.06, using SDSS color images and the X-ray data from the Northern ROSAT All-Sky (NORAS) catalog. We analyze the distribution of arm classes along the clustercentric radius as well as that of Hubble types. To segregate the effect of local environment from the global environment, we compare the morphological distribution of galaxies in two X-lay luminosity groups, the low-Lx clusters (Lx < 0.15×1044erg/s) and high-Lx clusters (Lx > 1.8×1044erg/s). We find that the morphology-clustercentric relation prevails in the cluster envirnment although there is a brake near the cluster virial radius. The grand design arms comprise about 40% of the cluster spiral galaxies with a weak morphology-clustercentric radius relation for the arm classes, in the sense that flocculent galaxies tend to increase outward, regardless of the X-ray luminosity. From the cumulative radial distribution of cluster galaxies, we found that the low-Lx clusters are fully virialized while the high-Lx clusters are not.

Cluster analysis applied to localized dispersion curves in East Asia: the limits of surface wave resolution

NASA Astrophysics Data System (ADS)

Witek, M.; van der Lee, S.; Kang, T. S.; Chang, S. J.; Ning, J.; Ning, S.

2017-12-01

We have measured Rayleigh wave group velocity dispersion curves from one year of station-pair cross-correlations of continuous vertical-component broadband data from 1082 seismic stations in regional networks across China, Korea, Taiwan, and Japan for the year 2011. We use the measurements to map local dispersion anomalies for periods in the range 6-40 s. We combined our ambient noise data set with the earthquake group velocity data set of Ma et al. (2014), and then applied agglomerative hierarchical clustering to the localized dispersion curves. We find that the dispersion curves naturally organize themselves into distinct tectonic regions. For our distribution of interstation distances, only 8 distinct regions need to be defined. Additional clusters reduce the overall data misfit by increasingly smaller amounts. The size and number of clusters needed to suitably predict the data may give an indication of the resolving power of the data set. The regions that emerge from the cluster analysis include Tibet, the Sea of Japan, the South China Block and the Korean peninsula, the Ordos and Yangtze cratons, and Mesozoic rift basins such as the Songliao, Bohai Bay and Ulleung basins. We also performed a traditional inversion for 3D S-velocity structure, and the resulting model fits the data as well as the 8-cluster model, while both models fit the earthquake data and ambient noise data better than the LITHO1.0 model of Pasyanos et al. (2014). Our 3D model of the crust and upper mantle confirms many of the features seen in previous studies of the region, most notably the lithospheric thinning going from west to east and low velocity zones in the crust on the Tibetan periphery. We conclude that cluster analysis is able to greatly reduce the dimensionality of surface wave dispersion data, in the sense that a data set of over half a million dispersion curves is sufficiently predicted by appropriately averaging over a relatively small set of distinct tectonic regions. The resulting clustered model objectively quantifies the more intuitive ways in which we usually tend to interpret tomographic models.

Rosacea assessment by erythema index and principal component analysis segmentation maps

NASA Astrophysics Data System (ADS)

Kuzmina, Ilona; Rubins, Uldis; Saknite, Inga; Spigulis, Janis

2017-12-01

RGB images of rosacea were analyzed using segmentation maps of principal component analysis (PCA) and erythema index (EI). Areas of segmented clusters were compared to Clinician's Erythema Assessment (CEA) values given by two dermatologists. The results show that visible blood vessels are segmented more precisely on maps of the erythema index and the third principal component (PC3). In many cases, a distribution of clusters on EI and PC3 maps are very similar. Mean values of clusters' areas on these maps show a decrease of the area of blood vessels and erythema and an increase of lighter skin area after the therapy for the patients with diagnosis CEA = 2 on the first visit and CEA=1 on the second visit. This study shows that EI and PC3 maps are more useful than the maps of the first (PC1) and second (PC2) principal components for indicating vascular structures and erythema on the skin of rosacea patients and therapy monitoring.

Analysis of the heat capacity of nanoclusters of FCC metals on the example of Al, Ni, Cu, Pd, and Au

NASA Astrophysics Data System (ADS)

Gafner, Yu. Ya.; Gafner, S. L.; Zamulin, I. S.; Redel, L. V.; Baidyshev, V. S.

2015-06-01

The heat capacity of ideal nickel, copper, gold, aluminum, and palladium fcc clusters with diameter of up to 6 nm has been studied in the temperature range of 150-800 K in terms of the molecular-dynamics theory using a tight-binding potential. The heat capacity of individual metallic nanoclusters has been found to exceed that characteristic of the bulk state, but by no more than 16-20%, even in the case of very small clusters. To explain the discrepancy between the simulated data and the experimental results on the compacted metals, aluminum and palladium samples with 80% theoretical density have also been investigated. Based on the simulation results and analysis of the experimental data, it has been established that the increased heat capacity of the compacted nanomaterials does not depend on the enhanced heat capacity of the individual clusters but rather, can be due to either the disordered state of the nanomaterial or a significant content of impurities (mainly, hydrogen).

Metabolic Clustering Analysis as a Strategy for Compound Selection in the Drug Discovery Pipeline for Leishmaniasis.

PubMed

Armitage, Emily G; Godzien, Joanna; Peña, Imanol; López-Gonzálvez, Ángeles; Angulo, Santiago; Gradillas, Ana; Alonso-Herranz, Vanesa; Martín, Julio; Fiandor, Jose M; Barrett, Michael P; Gabarro, Raquel; Barbas, Coral

2018-05-18

A lack of viable hits, increasing resistance, and limited knowledge on mode of action is hindering drug discovery for many diseases. To optimize prioritization and accelerate the discovery process, a strategy to cluster compounds based on more than chemical structure is required. We show the power of metabolomics in comparing effects on metabolism of 28 different candidate treatments for Leishmaniasis (25 from the GSK Leishmania box, two analogues of Leishmania box series, and amphotericin B as a gold standard treatment), tested in the axenic amastigote form of Leishmania donovani. Capillary electrophoresis-mass spectrometry was applied to identify the metabolic profile of Leishmania donovani, and principal components analysis was used to cluster compounds on potential mode of action, offering a medium throughput screening approach in drug selection/prioritization. The comprehensive and sensitive nature of the data has also made detailed effects of each compound obtainable, providing a resource to assist in further mechanistic studies and prioritization of these compounds for the development of new antileishmanial drugs.

Density functional theory and surface reactivity study of bimetallic AgnYm (n+m = 10) clusters

NASA Astrophysics Data System (ADS)

Hussain, Riaz; Hussain, Abdullah Ijaz; Chatha, Shahzad Ali Shahid; Hussain, Riaz; Hanif, Usman; Ayub, Khurshid

2018-06-01

Density functional theory calculations have been performed on pure silver (Agn), yttrium (Ym) and bimetallic silver yttrium clusters AgnYm (n + m = 2-10) for reactivity descriptors in order to realize sites for nucleophilic and electrophilic attack. The reactivity descriptors of the clusters, studied as a function of cluster size and shape, reveal the presence of different type of reactive sites in a cluster. The size and shape of the pure silver, yttrium and bimetallic silver yttrium cluster (n = 2-10) strongly influences the number and position of active sites for an electrophilic and/or nucleophilic attack. The trends of reactivities through reactivity descriptors are confirmed through comparison with experimental data for CO binding with silver clusters. Moreover, the adsorption of CO on bimetallic silver yttrium clusters is also evaluated. The trends of binding energies support the reactivity descriptors values. Doping of pure cluster with the other element also influence the hardness, softness and chemical reactivity of the clusters. The softness increases as we increase the number of silver atoms in the cluster, whereas the hardness decreases. The chemical reactivity increases with silver doping whereas it decreases by increasing yttrium concentration. Silver atoms are nucleophilic in small clusters but changed to electrophilic in large clusters.

Sub-grouping patients with non-specific low back pain based on cluster analysis of discriminatory clinical items.

PubMed

Billis, Evdokia; McCarthy, Christopher J; Roberts, Chris; Gliatis, John; Papandreou, Maria; Gioftsos, George; Oldham, Jacqueline A

2013-02-01

To identify potential subgroups amongst patients with non-specific low back pain based on a consensus list of potentially discriminatory examination items. Exploratory study. A convenience sample of 106 patients with non-specific low back pain (43 males, 63 females, mean age 36 years, standard deviation 15.9 years) and 7 physiotherapists. Based on 3 focus groups and a two-round Delphi involving 23 health professionals and a random stratified sample of 150 physiotherapists, respectively, a comprehensive examination list comprising the most "discriminatory" items was compiled. Following reliability analysis, the most reliable clinical items were assessed with a sample of patients with non-specific low back pain. K-means cluster analysis was conducted for 2-, 3- and 4-cluster options to explore for meaningful homogenous subgroups. The most clinically meaningful cluster was a two-subgroup option, comprising a small group (n = 24) with more severe clinical presentation (i.e. more widespread pain, functional and sleeping problems, other symptoms, increased investigations undertaken, more severe clinical signs, etc.) and a larger less dysfunctional group (n = 80). A number of potentially discriminatory clinical items were identified by health professionals and sub-classified, based on a sample of patients with non-specific low back pain, into two subgroups. However, further work is needed to validate this classification process.

Custom-made foot orthoses: an analysis of prescription characteristics from an Australian commercial orthotic laboratory.

PubMed

Menz, Hylton B; Allan, Jamie J; Bonanno, Daniel R; Landorf, Karl B; Murley, George S

2017-01-01

Foot orthoses are widely used in the prevention and treatment of foot disorders. The aim of this study was to describe characteristics of custom-made foot orthosis prescriptions from a Australian podiatric orthotic laboratory. One thousand consecutive foot orthosis prescription forms were obtained from a commercial prescription foot orthosis laboratory located in Melbourne, Victoria, Australia (Footwork Podiatric Laboratory). Each item from the prescription form was documented in relation to orthosis type, cast correction, arch fill technique, cast modifications, shell material, shell modifications and cover material. Cluster analysis and discriminant function analysis were applied to identify patterns in the prescription data. Prescriptions were obtained from 178 clinical practices across Australia and Hong Kong, with patients ranging in age from 5 to 92 years. Three broad categories ('clusters') were observed that were indicative of increasing 'control' of rearfoot pronation. A combination of five variables (rearfoot cast correction, cover shape, orthosis type, forefoot cast correction and plantar fascial accommodation) was able to identify these clusters with an accuracy of 70%. Significant differences between clusters were observed in relation to age and sex of the patient and the geographic location of the prescribing clinician. Foot orthosis prescriptions are complex, but can be broadly classified into three categories. Selection of these prescription subtypes appears to be influenced by both patient factors (age and sex) and clinician factors (clinic location).

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks.

PubMed

Li, Min; Li, Dongyan; Tang, Yu; Wu, Fangxiang; Wang, Jianxin

2017-08-31

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster.

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks

PubMed Central

Li, Min; Li, Dongyan; Tang, Yu; Wang, Jianxin

2017-01-01

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster. PMID:28858211

Diffusion of oxygen interstitials in UO2+x using kinetic Monte Carlo simulations: Role of O/M ratio and sensitivity analysis

NASA Astrophysics Data System (ADS)

Behera, Rakesh K.; Watanabe, Taku; Andersson, David A.; Uberuaga, Blas P.; Deo, Chaitanya S.

2016-04-01

Oxygen interstitials in UO2+x significantly affect the thermophysical properties and microstructural evolution of the oxide nuclear fuel. In hyperstoichiometric Urania (UO2+x), these oxygen interstitials form different types of defect clusters, which have different migration behavior. In this study we have used kinetic Monte Carlo (kMC) to evaluate diffusivities of oxygen interstitials accounting for mono- and di-interstitial clusters. Our results indicate that the predicted diffusivities increase significantly at higher non-stoichiometry (x > 0.01) for di-interstitial clusters compared to a mono-interstitial only model. The diffusivities calculated at higher temperatures compare better with experimental values than at lower temperatures (< 973 K). We have discussed the resulting activation energies achieved for diffusion with all the mono- and di-interstitial models. We have carefully performed sensitivity analysis to estimate the effect of input di-interstitial binding energies on the predicted diffusivities and activation energies. While this article only discusses mono- and di-interstitials in evaluating oxygen diffusion response in UO2+x, future improvements to the model will primarily focus on including energetic definitions of larger stable interstitial clusters reported in the literature. The addition of larger clusters to the kMC model is expected to improve the comparison of oxygen transport in UO2+x with experiment.

Social phobia subtypes in the general population revealed by cluster analysis.

PubMed

Furmark, T; Tillfors, M; Stattin, H; Ekselius, L; Fredrikson, M

2000-11-01

Epidemiological data on subtypes of social phobia are scarce and their defining features are debated. Hence, the present study explored the prevalence and descriptive characteristics of empirically derived social phobia subgroups in the general population. To reveal subtypes, data on social distress, functional impairment, number of social fears and criteria fulfilled for avoidant personality disorder were extracted from a previously published epidemiological study of 188 social phobics and entered into an hierarchical cluster analysis. Criterion validity was evaluated by comparing clusters on the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS). Finally, profile analyses were performed in which clusters were compared on a set of sociodemographic and descriptive characteristics. Three clusters emerged, consisting of phobics scoring either high (generalized subtype), intermediate (non-generalized subtype) or low (discrete subtype) on all variables. Point prevalence rates were 2.0%, 5.9% and 7.7% respectively. All subtypes were distinguished on both SPS and SIAS. Generalized or severe social phobia tended to be over-represented among individuals with low levels of educational attainment and social support. Overall, public-speaking was the most common fear. Although categorical distinctions may be used, the present data suggest that social phobia subtypes in the general population mainly differ dimensionally along a mild moderate-severe continuum, and that the number of cases declines with increasing severity.

Periorbital melasma: Hierarchical cluster analysis of clinical features in Asian patients.

PubMed

Jung, Y S; Bae, J M; Kim, B J; Kang, J-S; Cho, S B

2017-11-01

Studies have shown melasma lesions to be distributed across the face in centrofacial, malar, and mandibular patterns. Meanwhile, however, melasma lesions of the periorbital area have yet to be thoroughly described. We analyzed normal and ultraviolet light-exposed photographs of patients with melasma. The periorbital melasma lesions were measured according to anatomical reference points and a hierarchical cluster analysis was performed. The periorbital melasma lesions showed clinical features of fine and homogenous melasma pigmentation, involving both the upper and lower eyelids that extended to other anatomical sites with a darker and coarser appearance. The hierarchical cluster analysis indicated that patients with periorbital melasma can be categorized into two clusters according to the surface anatomy of the face. Significant differences between cluster 1 and cluster 2 were found in lateral distance and inferolateral distance, but not in medial distance and superior distance. Comparing the two clusters, patients in cluster 2 were found to be significantly older and more commonly accompanied by melasma lesions of the temple and medial cheek. Our hierarchical cluster analysis of periorbital melasma lesions demonstrated that Asian patients with periorbital melasma can be categorized into two clusters according to the surface anatomy of the face. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Cluster analysis of molecular simulation trajectories for systems where both conformation and orientation of the sampled states are important.

PubMed

Abramyan, Tigran M; Snyder, James A; Thyparambil, Aby A; Stuart, Steven J; Latour, Robert A

2016-08-05

Clustering methods have been widely used to group together similar conformational states from molecular simulations of biomolecules in solution. For applications such as the interaction of a protein with a surface, the orientation of the protein relative to the surface is also an important clustering parameter because of its potential effect on adsorbed-state bioactivity. This study presents cluster analysis methods that are specifically designed for systems where both molecular orientation and conformation are important, and the methods are demonstrated using test cases of adsorbed proteins for validation. Additionally, because cluster analysis can be a very subjective process, an objective procedure for identifying both the optimal number of clusters and the best clustering algorithm to be applied to analyze a given dataset is presented. The method is demonstrated for several agglomerative hierarchical clustering algorithms used in conjunction with three cluster validation techniques. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data

PubMed Central

Vieira, Verónica; Webster, Thomas; Weinberg, Janice; Aschengrau, Ann; Ozonoff, David

2005-01-01

Background The availability of geographic information from cancer and birth defect registries has increased public demands for investigation of perceived disease clusters. Many neighborhood-level cluster investigations are methodologically problematic, while maps made from registry data often ignore latency and many known risk factors. Population-based case-control and cohort studies provide a stronger foundation for spatial epidemiology because potential confounders and disease latency can be addressed. Methods We investigated the association between residence and colorectal, lung, and breast cancer on upper Cape Cod, Massachusetts (USA) using extensive data on covariates and residential history from two case-control studies for 1983–1993. We generated maps using generalized additive models, smoothing on longitude and latitude while adjusting for covariates. The resulting continuous surface estimates disease rates relative to the whole study area. We used permutation tests to examine the overall importance of location in the model and identify areas of increased and decreased risk. Results Maps of colorectal cancer were relatively flat. Assuming 15 years of latency, lung cancer was significantly elevated just northeast of the Massachusetts Military Reservation, although the result did not hold when we restricted to residences of longest duration. Earlier non-spatial epidemiology had found a weak association between lung cancer and proximity to gun and mortar positions on the reservation. Breast cancer hot spots tended to increase in magnitude as we increased latency and adjusted for covariates, indicating that confounders were partly hiding these areas. Significant breast cancer hot spots were located near known groundwater plumes and the Massachusetts Military Reservation. Discussion Spatial epidemiology of population-based case-control studies addresses many methodological criticisms of cluster studies and generates new exposure hypotheses. Our results provide evidence for spatial clustering of breast cancer on upper Cape Cod. The analysis suggests further investigation of the potential association between breast cancer and pollution plumes based on detailed exposure modeling. PMID:15955253

Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: an application of generalized additive models to case-control data.

PubMed

Vieira, Verónica; Webster, Thomas; Weinberg, Janice; Aschengrau, Ann; Ozonoff, David

2005-06-14

The availability of geographic information from cancer and birth defect registries has increased public demands for investigation of perceived disease clusters. Many neighborhood-level cluster investigations are methodologically problematic, while maps made from registry data often ignore latency and many known risk factors. Population-based case-control and cohort studies provide a stronger foundation for spatial epidemiology because potential confounders and disease latency can be addressed. We investigated the association between residence and colorectal, lung, and breast cancer on upper Cape Cod, Massachusetts (USA) using extensive data on covariates and residential history from two case-control studies for 1983-1993. We generated maps using generalized additive models, smoothing on longitude and latitude while adjusting for covariates. The resulting continuous surface estimates disease rates relative to the whole study area. We used permutation tests to examine the overall importance of location in the model and identify areas of increased and decreased risk. Maps of colorectal cancer were relatively flat. Assuming 15 years of latency, lung cancer was significantly elevated just northeast of the Massachusetts Military Reservation, although the result did not hold when we restricted to residences of longest duration. Earlier non-spatial epidemiology had found a weak association between lung cancer and proximity to gun and mortar positions on the reservation. Breast cancer hot spots tended to increase in magnitude as we increased latency and adjusted for covariates, indicating that confounders were partly hiding these areas. Significant breast cancer hot spots were located near known groundwater plumes and the Massachusetts Military Reservation. Spatial epidemiology of population-based case-control studies addresses many methodological criticisms of cluster studies and generates new exposure hypotheses. Our results provide evidence for spatial clustering of breast cancer on upper Cape Cod. The analysis suggests further investigation of the potential association between breast cancer and pollution plumes based on detailed exposure modeling.

Molecular simulation of flow-enhanced nucleation in n-eicosane melts under steady shear and uniaxial extension.

PubMed

Nicholson, David A; Rutledge, Gregory C

2016-12-28

Non-equilibrium molecular dynamics is used to study crystal nucleation of n-eicosane under planar shear and, for the first time, uniaxial extension. A method of analysis based on the mean first-passage time is applied to the simulation results in order to determine the effect of the applied flow field type and strain rate on the steady-state nucleation rate and a characteristic growth rate, as well as the effects on kinetic parameters associated with nucleation: the free energy barrier, critical nucleus size, and monomer attachment pre-factor. The onset of flow-enhanced nucleation (FEN) occurs at a smaller critical strain rate in extension as compared to shear. For strain rates larger than the critical rate, a rapid increase in the nucleation rate is accompanied by decreases in the free energy barrier and critical nucleus size, as well as an increase in chain extension. These observations accord with a mechanism in which FEN is caused by an increase in the driving force for crystallization due to flow-induced entropy reduction. At high applied strain rates, the free energy barrier, critical nucleus size, and degree of stretching saturate, while the monomer attachment pre-factor and degree of orientational order increase steadily. This trend is indicative of a significant diffusive contribution to the nucleation rate under intense flows that is correlated with the degree of global orientational order in a nucleating system. Both flow fields give similar results for all kinetic quantities with respect to the reduced strain rate, which we define as the ratio of the applied strain rate to the critical rate. The characteristic growth rate increases with increasing strain rate, and shows a correspondence with the nucleation rate that does not depend on the type of flow field applied. Additionally, a structural analysis of the crystalline clusters indicates that the flow field suppresses the compaction and crystalline ordering of clusters, leading to the formation of large articulated clusters under strong flow fields, and compact well-ordered clusters under weak flow fields.

Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

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

Data Analysis and Visualization; nternational Research Training Group ``Visualization of Large and Unstructured Data Sets,'' University of Kaiserslautern, Germany; Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA

2008-05-12

The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii)more » evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.« less

Determinants of the use of dietary supplements among secondary and high school students

PubMed

Gajda, Karolina; Zielińska, Monika; Ciecierska, Anna; Hamułka, Jadwiga

All over the world, including Poland, the sale of dietary supplements is increasing. More and more often, people including children and youths, use dietary supplements on their own initiative and without any medical indications or knowledge in this field. Analysis of the conditions of using the dietary supplements with vitamins and minerals among secondary school and high school students in Poland. The study included 396 students aged 13-18 years (249 girls and 147 boys). Authors’ questionnaire was used to evaluate the intake of dietary supplements. The use of cluster analysis allowed to distinguish groups of students with similar socio-demographic characteristics and the frequency of use of dietary supplements. In the studied population of students three clusters were created that significantly differed in socio-demographic characteristics. In cluster 1 and 2, were mostly students who used dietary supplements (respectively, 56% of respondents and 100%). In cluster 1 there were mostly students coming from rural areas and small city, with a worse financial situation, mainly boys (56%), while cluster 2 was dominated by girls (81%) living in a big city, coming from families with a good financial situation and who were more likely to be underweight (28.8%). In cluster 3 there were mostly older students (62%), not taking dietary supplements. In comparison to cluster 2, they had lower frequency of breakfast consumption (55% vs. 69%), but higher frequency of the consumption of soft drinks, fast-food, coffee as well as salt use at the table. The results show that the use of dietary supplements in adolescence is a common phenomenon and slightly conditioned by eating behaviors. This unfavorable habit of common dietary supplements intake observed among students indicates the need for education on the benefits and risks of the supplements usage.

A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa

PubMed Central

Petegrosso, Raphael; Tolar, Jakub

2018-01-01

Single-cell RNA sequencing (scRNA-seq) has been widely applied to discover new cell types by detecting sub-populations in a heterogeneous group of cells. Since scRNA-seq experiments have lower read coverage/tag counts and introduce more technical biases compared to bulk RNA-seq experiments, the limited number of sampled cells combined with the experimental biases and other dataset specific variations presents a challenge to cross-dataset analysis and discovery of relevant biological variations across multiple cell populations. In this paper, we introduce a method of variance-driven multitask clustering of single-cell RNA-seq data (scVDMC) that utilizes multiple single-cell populations from biological replicates or different samples. scVDMC clusters single cells in multiple scRNA-seq experiments of similar cell types and markers but varying expression patterns such that the scRNA-seq data are better integrated than typical pooled analyses which only increase the sample size. By controlling the variance among the cell clusters within each dataset and across all the datasets, scVDMC detects cell sub-populations in each individual experiment with shared cell-type markers but varying cluster centers among all the experiments. Applied to two real scRNA-seq datasets with several replicates and one large-scale droplet-based dataset on three patient samples, scVDMC more accurately detected cell populations and known cell markers than pooled clustering and other recently proposed scRNA-seq clustering methods. In the case study applied to in-house Recessive Dystrophic Epidermolysis Bullosa (RDEB) scRNA-seq data, scVDMC revealed several new cell types and unknown markers validated by flow cytometry. MATLAB/Octave code available at https://github.com/kuanglab/scVDMC. PMID:29630593

Malaria control and prevention towards elimination: data from an eleven-year surveillance in Shandong Province, China.

PubMed

Kong, Xiangli; Liu, Xin; Tu, Hong; Xu, Yan; Niu, Jianbing; Wang, Yongbin; Zhao, Changlei; Kou, Jingxuan; Feng, Jun

2017-01-31

Shandong Province experienced a declining malaria trend of local-acquired transmission, but the increasing imported malaria remains a challenge. Therefore, understanding the epidemiological characteristics of malaria and the control and elimination strategy and interventions is needed for better planning to achieve the overall elimination goal in Shandong Province. A retrospective study was conducted and all individual cases from a web-based reporting system were reviewed and analysed to explore malaria-endemic characteristics in Shandong from 2005 to 2015. Annual malaria incidence reported in 2005-2015 were geo-coded and matched to the county-level. Spatial cluster analysis was performed to evaluate any identified spatial disease clusters for statistical significance. The space-time cluster was detected with high rates through the retrospective space-time analysis scanning using the discrete Poisson model. The overall malaria incidence decreased to a low level during 2005-2015. In total, 1564 confirmed malaria cases were reported, 27.1% of which (n = 424) were indigenous cases. Most of the indigenous case (n = 339, 80.0%) occurred from June to October. However, the number and scale of imported cases have been increased but no significant difference was observed during months. Shandong is endemic for both Plasmodium vivax (n = 730) and Plasmodium falciparum (n = 674). The disease is mainly distributed in Southern (n = 710) and Eastern region (n = 424) of Shandong, such as Jinning (n = 214 [13.7%]), Weihai (n = 151 [9.7%]), and Yantai (n = 107 [6.8%]). Furthermore, the spatial cluster analysis of malaria cases from 2005 to 2015 indicated that the diseased was not randomly distributed. For indigenous cases, a total of 15 and 2 high-risk counties were determined from 2005 to 2009 (control phase) and from 2010 to 2015 (elimination phase), respectively. For imported cases, a total of 26 and 29 high-risk counties were determined from 2005 to 2009 (control phase) and from 2010 to 2015 (elimination phase), respectively. The method of spatial scan statistics identified different 13 significant spatial clusters between 2005 and 2015. The space-time clustering analysis determined that the most likely cluster included 14 and 19 counties for indigenous and imported, respectively. In order to cope with the requirements of malaria elimination phase, the surveillance system should be strengthened particularity on the frequent migration regions as well as the effective multisectoral cooperation and coordination mechanisms. Specific response packages should be tailored among different types of cities and capacity building should also be improved mainly focus on the emergence response and case management. Fund guarantees for scientific research should be maintained both during the elimination and post-elimination phase to consolidate the achievements of malaria elimination.

Cluster-based analysis of multi-model climate ensembles

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Assessment of Depression in a Rodent Model of Spinal Cord Injury

PubMed Central

Luedtke, Kelsey; Bouchard, Sioui Maldonado; Woller, Sarah A.; Funk, Mary Katherine; Aceves, Miriam

2014-01-01

Abstract Despite an increased incidence of depression in patients after spinal cord injury (SCI), there is no animal model of depression after SCI. To address this, we used a battery of established tests to assess depression after a rodent contusion injury. Subjects were acclimated to the tasks, and baseline scores were collected before SCI. Testing was conducted on days 9–10 (acute) and 19–20 (chronic) postinjury. To categorize depression, subjects' scores on each behavioral measure were averaged across the acute and chronic stages of injury and subjected to a principal component analysis. This analysis revealed a two-component structure, which explained 72.2% of between-subjects variance. The data were then analyzed with a hierarchical cluster analysis, identifying two clusters that differed significantly on the sucrose preference, open field, social exploration, and burrowing tasks. One cluster (9 of 26 subjects) displayed characteristics of depression. Using these data, a discriminant function analysis was conducted to derive an equation that could classify subjects as “depressed” on days 9–10. The discriminant function was used in a second experiment examining whether the depression-like symptoms could be reversed with the antidepressant, fluoxetine. Fluoxetine significantly decreased immobility in the forced swim test (FST) in depressed subjects identified with the equation. Subjects that were depressed and treated with saline displayed significantly increased immobility on the FST, relative to not depressed, saline-treated controls. These initial experiments validate our tests of depression, generating a powerful model system for further understanding the relationships between molecular changes induced by SCI and the development of depression. PMID:24564232

Simultaneous Two-Way Clustering of Multiple Correspondence Analysis

ERIC Educational Resources Information Center

Hwang, Heungsun; Dillon, William R.

2010-01-01

A 2-way clustering approach to multiple correspondence analysis is proposed to account for cluster-level heterogeneity of both respondents and variable categories in multivariate categorical data. Specifically, in the proposed method, multiple correspondence analysis is combined with k-means in a unified framework in which "k"-means is…

Cluster Analysis of Minnesota School Districts. A Research Report.

ERIC Educational Resources Information Center

Cleary, James

The term "cluster analysis" refers to a set of statistical methods that classify entities with similar profiles of scores on a number of measured dimensions, in order to create empirically based typologies. A 1980 Minnesota House Research Report employed cluster analysis to categorize school districts according to their relative mixtures…

Joint Analysis of X-Ray and Sunyaev-Zel'Dovich Observations of Galaxy Clusters Using an Analytic Model of the Intracluster Medium

NASA Technical Reports Server (NTRS)

Hasler, Nicole; Bulbul, Esra; Bonamente, Massimiliano; Carlstrom, John E.; Culverhouse, Thomas L.; Gralla, Megan; Greer, Christopher; Lamb, James W.; Hawkins, David; Hennessy, Ryan;

Description and typology of intensive Chios dairy sheep farms in Greece.

PubMed

Gelasakis, A I; Valergakis, G E; Arsenos, G; Banos, G

2012-06-01

The aim was to assess the intensified dairy sheep farming systems of the Chios breed in Greece, establishing a typology that may properly describe and characterize them. The study included the total of the 66 farms of the Chios sheep breeders' cooperative Macedonia. Data were collected using a structured direct questionnaire for in-depth interviews, including questions properly selected to obtain a general description of farm characteristics and overall management practices. A multivariate statistical analysis was used on the data to obtain the most appropriate typology. Initially, principal component analysis was used to produce uncorrelated variables (principal components), which would be used for the consecutive cluster analysis. The number of clusters was decided using hierarchical cluster analysis, whereas, the farms were allocated in 4 clusters using k-means cluster analysis. The identified clusters were described and afterward compared using one-way ANOVA or a chi-squared test. The main differences were evident on land availability and use, facility and equipment availability and type, expansion rates, and application of preventive flock health programs. In general, cluster 1 included newly established, intensive, well-equipped, specialized farms and cluster 2 included well-established farms with balanced sheep and feed/crop production. In cluster 3 were assigned small flock farms focusing more on arable crops than on sheep farming with a tendency to evolve toward cluster 2, whereas cluster 4 included farms representing a rather conservative form of Chios sheep breeding with low/intermediate inputs and choosing not to focus on feed/crop production. In the studied set of farms, 4 different farmer attitudes were evident: 1) farming disrupts sheep breeding; feed should be purchased and economies of scale will decrease costs (mainly cluster 1), 2) only exercise/pasture land is necessary; at least part of the feed (pasture) must be home-grown to decrease costs (clusters 1 and 4), 3) providing pasture to sheep is essential; on-farm feed production decreases costs (mainly cluster 3), and 4) large-scale farming (feed production and cash crops) does not disrupt sheep breeding; all feed must be produced on-farm to decrease costs (mainly cluster 3). Conducting a profitability analysis among different clusters, exploring and discovering the most beneficial levels of intensified management and capital investment should now be considered. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Associations between multiple health risk behaviors and mental health among Chinese college students.

PubMed

Ye, Yong-ling; Wang, Pei-gang; Qu, Geng-cong; Yuan, Shuai; Phongsavan, Philayrath; He, Qi-qiang

2016-01-01

Although there is substantial evidence that health risk behaviors increase risks of premature morbidity and mortality, little is known about the multiple health risk behaviors in Chinese college students. Here, we investigated the prevalence of multiple health risk behaviors and its relation to mental health among Chinese college students. A cross-sectional study was conducted in Wuhan, China from May to June 2012. The students reported their health risk behaviors using self-administered questionnaires. Depression and anxiety were assessed using the self-rating depression scale and self-rating anxiety scale, respectively. A total of 2422 college students (1433 males) aged 19.7 ± 1.2 years were participated in the study. The prevalence of physical inactivity, sleep disturbance, poor dietary behavior, Internet addiction disorder (IAD), frequent alcohol use and current smoking was 62.0, 42.6, 29.8, 22.3, 11.6 and 9.3%, respectively. Significantly increased risks for depression and anxiety were found among students with frequent alcohol use, sleep disturbance, poor dietary behavior and IAD. Two-step cluster analysis identified two different clusters. Participants in the cluster with more unhealthy behaviors showed significantly increased risk for depression (odds ratio (OR): 2.21; 95% confidence interval (CI): 1.83, 2.67) and anxiety (OR: 2.32; 95% CI: 1.85, 2.92). This study indicates that a relatively high prevalence of multiple health risk behaviors was found among Chinese college students. Furthermore, the clustering of health risk behaviors was significantly associated with increased risks for depression and anxiety.

An Approach to Cluster EU Member States into Groups According to Pathways of Salmonella in the Farm-to-Consumption Chain for Pork Products.

PubMed

Vigre, Håkan; Domingues, Ana Rita Coutinho Calado; Pedersen, Ulrik Bo; Hald, Tine

2016-03-01

The aim of the project as the cluster analysis was to in part to develop a generic structured quantitative microbiological risk assessment (QMRA) model of human salmonellosis due to pork consumption in EU member states (MSs), and the objective of the cluster analysis was to group the EU MSs according to the relative contribution of different pathways of Salmonella in the farm-to-consumption chain of pork products. In the development of the model, by selecting a case study MS from each cluster the model was developed to represent different aspects of pig production, pork production, and consumption of pork products across EU states. The objective of the cluster analysis was to aggregate MSs into groups of countries with similar importance of different pathways of Salmonella in the farm-to-consumption chain using available, and where possible, universal register data related to the pork production and consumption in each country. Based on MS-specific information about distribution of (i) small and large farms, (ii) small and large slaughterhouses, (iii) amount of pork meat consumed, and (iv) amount of sausages consumed we used nonhierarchical and hierarchical cluster analysis to group the MSs. The cluster solutions were validated internally using statistic measures and externally by comparing the clustered MSs with an estimated human incidence of salmonellosis due to pork products in the MSs. Finally, each cluster was characterized qualitatively using the centroids of the clusters. © 2016 Society for Risk Analysis.

Statistical Significance for Hierarchical Clustering

PubMed Central

Kimes, Patrick K.; Liu, Yufeng; Hayes, D. Neil; Marron, J. S.

2017-01-01

Summary Cluster analysis has proved to be an invaluable tool for the exploratory and unsupervised analysis of high dimensional datasets. Among methods for clustering, hierarchical approaches have enjoyed substantial popularity in genomics and other fields for their ability to simultaneously uncover multiple layers of clustering structure. A critical and challenging question in cluster analysis is whether the identified clusters represent important underlying structure or are artifacts of natural sampling variation. Few approaches have been proposed for addressing this problem in the context of hierarchical clustering, for which the problem is further complicated by the natural tree structure of the partition, and the multiplicity of tests required to parse the layers of nested clusters. In this paper, we propose a Monte Carlo based approach for testing statistical significance in hierarchical clustering which addresses these issues. The approach is implemented as a sequential testing procedure guaranteeing control of the family-wise error rate. Theoretical justification is provided for our approach, and its power to detect true clustering structure is illustrated through several simulation studies and applications to two cancer gene expression datasets. PMID:28099990

Cluster analysis of particulate matter (PM10) and black carbon (BC) concentrations

NASA Astrophysics Data System (ADS)

Žibert, Janez; Pražnikar, Jure

2012-09-01

The monitoring of air-pollution constituents like particulate matter (PM10) and black carbon (BC) can provide information about air quality and the dynamics of emissions. Air quality depends on natural and anthropogenic sources of emissions as well as the weather conditions. For a one-year period the diurnal concentrations of PM10 and BC in the Port of Koper were analysed by clustering days into similar groups according to the similarity of the BC and PM10 hourly derived day-profiles without any prior assumptions about working and non-working days, weather conditions or hot and cold seasons. The analysis was performed by using k-means clustering with the squared Euclidean distance as the similarity measure. The analysis showed that 10 clusters in the BC case produced 3 clusters with just one member day and 7 clusters that encompasses more than one day with similar BC profiles. Similar results were found in the PM10 case, where one cluster has a single-member day, while 7 clusters contain several member days. The clustering analysis revealed that the clusters with less pronounced bimodal patterns and low hourly and average daily concentrations for both types of measurements include the most days in the one-year analysis. A typical day profile of the BC measurements includes a bimodal pattern with morning and evening peaks, while the PM10 measurements reveal a less pronounced bimodality. There are also clusters with single-peak day-profiles. The BC data in such cases exhibit morning peaks, while the PM10 data consist of noon or afternoon single peaks. Single pronounced peaks can be explained by appropriate cluster wind speed profiles. The analysis also revealed some special day-profiles. The BC cluster with a high midnight peak at 30/04/2010 and the PM10 cluster with the highest observed concentration of PM10 at 01/05/2010 (208.0 μg m-3) coincide with 1 May, which is a national holiday in Slovenia and has very strong tradition of bonfire parties. The clustering of the diurnal concentration showed that various different day-profiles are presented in a cold period, while this is not the case for the hot season. Additional analysis of ship traffic and rain fall data showed that there is no statistically significant difference between the ship gross (bruto) registered tonnage (BRT) values in the case of BC and PM10 clusters, but that there is statistically significant differences between the rain fall in the BC and PM10 clusters. The wind-rose for clusters which included most days in the sampling period indicating that emitted PM10 and BC from Port of Koper were manly transported in the west direction over the sea and in the east direction, where there is in no populated area. Presented analysis showed that both BC and PM10 concentrations were driven by rain intensity and wind speed.

Spatiotemporal characteristics of severe dry and wet conditions in the Free State Province, South Africa

NASA Astrophysics Data System (ADS)

Mbiriri, M.; Mukwada, G.; Manatsa, D.

2018-02-01

This paper assesses the spatiotemporal characteristics of agricultural droughts and wet conditions in the Free State Province of South Africa for the period between 1960 and 2013. Since agriculturally, the Free State Province is considered the bread basket of the country, understanding the variability of drought and wet conditions becomes necessary. The Standardised Precipitation Index (SPI) computed from gridded monthly precipitation data was used to assess the rainfall extreme conditions. Hot spot analysis was used to divide the province into five homogenous clusters where the spatiotemporal characteristics for each cluster were analysed. The results show a west to east increase in seasonal average total precipitation. However, the eastern part of the province demonstrates higher occurrences of droughts, with SPI ≤ - 1.282. This is despite the observation that the region shows a recent increase in droughts unlike the western region. It is also noted that significant differences in drought/wet intensities between clusters are more pronounced during the early compared to the late summer period.

Using Cluster Analysis and ICP-MS to Identify Groups of Ecstasy Tablets in Sao Paulo State, Brazil.

PubMed

Maione, Camila; de Oliveira Souza, Vanessa Cristina; Togni, Loraine Rezende; da Costa, José Luiz; Campiglia, Andres Dobal; Barbosa, Fernando; Barbosa, Rommel Melgaço

2017-11-01

The variations found in the elemental composition in ecstasy samples result in spectral profiles with useful information for data analysis, and cluster analysis of these profiles can help uncover different categories of the drug. We provide a cluster analysis of ecstasy tablets based on their elemental composition. Twenty-five elements were determined by ICP-MS in tablets apprehended by Sao Paulo's State Police, Brazil. We employ the K-means clustering algorithm along with C4.5 decision tree to help us interpret the clustering results. We found a better number of two clusters within the data, which can refer to the approximated number of sources of the drug which supply the cities of seizures. The C4.5 model was capable of differentiating the ecstasy samples from the two clusters with high prediction accuracy using the leave-one-out cross-validation. The model used only Nd, Ni, and Pb concentration values in the classification of the samples. © 2017 American Academy of Forensic Sciences.

Exploring spatial patterns of farmland transactions and farmland use changes.

PubMed

Chang, Hsueh-Sheng; Chen, Tzu-Ling

2015-09-01

Strong economic incentives stimulate the conversion of farmland to non-farm uses possessing higher economic benefits, and rising land values can result in further conversions in the surrounding areas. However, previous studies focused exclusively on the analysis of attribute data, without concern for location or geographic information. Our study focuses on the application of spatial analysis method by exploring the magnitude and patterns of farmland use changes and farmland transactions in Tainan County in southwestern Taiwan. The results show that farmland use changes and transactions appear to cluster in specific locations-near urban planning areas, industrial parks, and science parks. Clustered farmland use changes indicate both excessive development of some farmland and possible protection of other farmland, while clustered farmland transactions indicate potential pressure for future conversion to non-farming uses. Overall, the spatial analyses indicate (without necessarily implying a cause-and-effect relationship) that the greater the farmland use change, the greater the number of farmland transactions. This approach to exploring the spatial patterns in and the interaction between farmland use change and farmland transactions can be applied to other regions facing increasing competition for farmland conversions and may be a useful tool for monitoring both urban expansion and increased farmland transactions. These occurrences should be closely monitored by governments to avoid excessive loss of farmland.

Is there an association between food patterns and life satisfaction among Norway's inhabitants ages 65 years and older?

PubMed

André, Beate; Canhão, Helena; Espnes, Geir A; Ferreira Rodrigues, Ana Maria; Gregorio, Maria João; Nguyen, Camilla; Sousa, Rute; Grønning, Kjersti

2017-03-01

The lack of information regarding older adults' health and lifestyles makes it difficult to design suitable interventions for people at risk of developing unhealth lifestyles. Therefore, there is a need to increase knowledge about older adults' food patterns and quality of life. Our aim was to determine associations among food patterns, anxiety, depression, and life satisfaction in Norwegian inhabitants ages 65+. The Nord-Trøndelag Health Study (The HUNT Study) is a large, population-based cohort study that includes data for 125 000 Norwegian participants. The cohort used for this study is wave three of the study, consisting of 11 619 participants age 65 and over. Cluster analysis was used to categorize the participants based on similarities in food consumption; two clusters were identified based on similarities regarding food consumption among participants. Significant differences between the clusters were found, as participants in the healthy food-patterns cluster had higher life satisfaction and lower anxiety and depression than those in the unhealthy food-patterns cluster. The associations among food patterns, anxiety, depression, and life satisfaction among older adults show the need for increased focus on interactions among food patterns, food consumption, and life satisfaction among the elderly in order to explore how society can influence these patterns. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Spatial correlations, clustering and percolation-like transitions in homicide crimes

NASA Astrophysics Data System (ADS)

Alves, L. G. A.; Lenzi, E. K.; Mendes, R. S.; Ribeiro, H. V.

2015-07-01

The spatial dynamics of criminal activities has been recently studied through statistical physics methods; however, models and results have been focusing on local scales (city level) and much less is known about these patterns at larger scales, e.g. at a country level. Here we report on a characterization of the spatial dynamics of the homicide crimes along the Brazilian territory using data from all cities (˜5000) in a period of more than thirty years. Our results show that the spatial correlation function in the per capita homicides decays exponentially with the distance between cities and that the characteristic correlation length displays an acute increasing trend in the latest years. We also investigate the formation of spatial clusters of cities via a percolation-like analysis, where clustering of cities and a phase-transition-like behavior describing the size of the largest cluster as a function of a homicide threshold are observed. This transition-like behavior presents evolutive features characterized by an increasing in the homicide threshold (where the transitions occur) and by a decreasing in the transition magnitudes (length of the jumps in the cluster size). We believe that our work sheds new light on the spatial patterns of criminal activities at large scales, which may contribute for better political decisions and resources allocation as well as opens new possibilities for modeling criminal activities by setting up fundamental empirical patterns at large scales.

Retrospective space-time cluster analysis of whooping cough, re-emergence in Barcelona, Spain, 2000-2011.

PubMed

Solano, Rubén; Gómez-Barroso, Diana; Simón, Fernando; Lafuente, Sarah; Simón, Pere; Rius, Cristina; Gorrindo, Pilar; Toledo, Diana; Caylà, Joan A

2014-05-01

A retrospective, space-time study of whooping cough cases reported to the Public Health Agency of Barcelona, Spain between the years 2000 and 2011 is presented. It is based on 633 individual whooping cough cases and the 2006 population census from the Spanish National Statistics Institute, stratified by age and sex at the census tract level. Cluster identification was attempted using space-time scan statistic assuming a Poisson distribution and restricting temporal extent to 7 days and spatial distance to 500 m. Statistical calculations were performed with Stata 11 and SatScan and mapping was performed with ArcGis 10.0. Only clusters showing statistical significance (P <0.05) were mapped. The most likely cluster identified included five census tracts located in three neighbourhoods in central Barcelona during the week from 17 to 23 August 2011. This cluster included five cases compared with the expected level of 0.0021 (relative risk = 2436, P <0.001). In addition, 11 secondary significant space-time clusters were detected with secondary clusters occurring at different times and localizations. Spatial statistics is felt to be useful by complementing epidemiological surveillance systems through visualizing excess in the number of cases in space and time and thus increase the possibility of identifying outbreaks not reported by the surveillance system.

Identification of crystalline structures in jet-cooled acetylene large clusters studied by two-dimensional correlation infrared spectroscopy

NASA Astrophysics Data System (ADS)

Matsumoto, Yoshiteru; Yoshiura, Ryuto; Honma, Kenji

2017-07-01

We investigated the crystalline structures of jet-cooled acetylene (C2H2) large clusters by laser spectroscopy and chemometrics. The CH stretching vibrations of the C2H2 large clusters were observed by infrared (IR) cavity ringdown spectroscopy. The IR spectra of C2H2 clusters were measured under the conditions of various concentrations of C2H2/He mixture gas for supersonic jets. Upon increasing the gas concentration from 1% to 10%, we observed a rapid intensity enhancement for a band in the IR spectra. The strong dependence of the intensity on the gas concentration indicates that the band was assigned to CH stretching vibrations of the large clusters. An analysis of the IR spectra by two-dimensional correlation spectroscopy revealed that the IR absorption due to the C2H2 large cluster is decomposed into two CH stretching vibrations. The vibrational frequencies of the two bands are almost equivalent to the IR absorption of the pure- and poly-crystalline orthorhombic structures in the aerosol particles. The characteristic temperature behavior of the IR spectra implies the existence of the other large cluster, which is discussed in terms of the phase transition of a bulk crystal.

Obesigenic families: parents’ physical activity and dietary intake patterns predict girls’ risk of overweight

PubMed Central

Davison, K Krahnstoever; Birch, L Lipps

2008-01-01

OBJECTIVE To determine whether obesigenic families can be identified based on mothers’ and fathers’ dietary and activity patterns. METHODS A total of 197 girls and their parents were assessed when girls were 5 y old; 192 families were reassessed when girls were 7 y old. Measures of parents’ physical activity and dietary intake were obtained and entered into a cluster analysis to assess whether distinct family clusters could be identified. Girls’ skinfold thickness and body mass index (BMI) were also assessed and were used to examine the predictive validity of the clusters. RESULTS Obesigenic and a non-obesigenic family clusters were identified. Mothers and fathers in the obesigenic cluster reported high levels of dietary intake and low levels of physical activity, while mothers and fathers in the non-obesigenic cluster reported low levels of dietary intake and high levels of activity. Girls from families in the obesigenic cluster had significantly higher BMI and skinfold thickness values at age 7 and showed significantly greater increases in BMI and skinfold thickness from ages 5 to 7 y than girls from non-obesigenic families; differences were reduced but not eliminated after controlling for parents’ BMI. CONCLUSIONS Obesigenic families, defined in terms of parents’ activity and dietary patterns, can be used predict children’s risk of obesity. PMID:12187395

Water-Soluble Phosphine-Protected Au₁₁ Clusters: Synthesis, Electronic Structure, and Chiral Phase Transfer in a Synergistic Fashion.

PubMed

Yao, Hiroshi; Iwatsu, Mana

2016-04-05

Synthesis of atomically precise, water-soluble phosphine-protected gold clusters is still currently limited probably due to a stability issue. We here present the synthesis, magic-number isolation, and exploration of the electronic structures as well as the asymmetric conversion of triphenylphosphine monosulfonate (TPPS)-protected gold clusters. Electrospray ionization mass spectrometry and elemental analysis result in the primary formation of Au11(TPPS)9Cl undecagold cluster compound. Magnetic circular dichroism (MCD) spectroscopy clarifies that extremely weak transitions are present in the low-energy region unresolved in the UV-vis absorption, which can be due to the Faraday B-terms based on the magnetically allowed transitions in the cluster. Asymmetric conversion without changing the nuclearity is remarkable by the chiral phase transfer in a synergistic fashion, which yields a rather small anisotropy factor (g-factor) of at most (2.5-7.0) × 10(-5). Quantum chemical calculations for model undecagold cluster compounds are then used to evaluate the optical and chiroptical responses induced by the chiral phase transfer. On this basis, we find that the Au core distortion is ignorable, and the chiral ion-pairing causes a slight increase in the CD response of the Au11 cluster.

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

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.

Principal components derived from CSF inflammatory profiles predict outcome in survivors after severe traumatic brain injury.

PubMed

Kumar, Raj G; Rubin, Jonathan E; Berger, Rachel P; Kochanek, Patrick M; Wagner, Amy K

2016-03-01

Studies have characterized absolute levels of multiple inflammatory markers as significant risk factors for poor outcomes after traumatic brain injury (TBI). However, inflammatory marker concentrations are highly inter-related, and production of one may result in the production or regulation of another. Therefore, a more comprehensive characterization of the inflammatory response post-TBI should consider relative levels of markers in the inflammatory pathway. We used principal component analysis (PCA) as a dimension-reduction technique to characterize the sets of markers that contribute independently to variability in cerebrospinal (CSF) inflammatory profiles after TBI. Using PCA results, we defined groups (or clusters) of individuals (n=111) with similar patterns of acute CSF inflammation that were then evaluated in the context of outcome and other relevant CSF and serum biomarkers collected days 0-3 and 4-5 post-injury. We identified four significant principal components (PC1-PC4) for CSF inflammation from days 0-3, and PC1 accounted for the greatest (31%) percentage of variance. PC1 was characterized by relatively higher CSF sICAM-1, sFAS, IL-10, IL-6, sVCAM-1, IL-5, and IL-8 levels. Cluster analysis then defined two distinct clusters, such that individuals in cluster 1 had highly positive PC1 scores and relatively higher levels of CSF cortisol, progesterone, estradiol, testosterone, brain derived neurotrophic factor (BDNF), and S100b; this group also had higher serum cortisol and lower serum BDNF. Multinomial logistic regression analyses showed that individuals in cluster 1 had a 10.9 times increased likelihood of GOS scores of 2/3 vs. 4/5 at 6 months compared to cluster 2, after controlling for covariates. Cluster group did not discriminate between mortality compared to GOS scores of 4/5 after controlling for age and other covariates. Cluster groupings also did not discriminate mortality or 12 month outcomes in multivariate models. PCA and cluster analysis establish that a subset of CSF inflammatory markers measured in days 0-3 post-TBI may distinguish individuals with poor 6-month outcome, and future studies should prospectively validate these findings. PCA of inflammatory mediators after TBI could aid in prognostication and in identifying patient subgroups for therapeutic interventions. Copyright © 2015 Elsevier Inc. All rights reserved.

Characterization of herbaspirillum- and limnobacter-related strains isolated from young volcanic deposits in miyake-jima island, Japan.

PubMed

Lu, Hongsheng; Fujimura, Reiko; Sato, Yoshinori; Nanba, Kenji; Kamijo, Takashi; Ohta, Hiroyuki

2008-01-01

The role of microbes in the early development of ecosystems on new volcanic materials seems to be crucial to primary plant succession but is not well characterized. Here we analyzed the bacterial community colonizing 22-year-old volcanic deposits of the Miyake-jima Island (Japan) using culture-based and 16S rRNA gene clone library methods. The majority of 91 bacterial isolates were placed phylogenetically in two clusters (A and B) of the Betaproteobacteria. Cluster A (82% of isolates) was related to the genus Limnobacter and Cluster B (9%) was affiliated with the Herbaspirillum clade. The clone library analysis supported the predominance of Cluster B rather than Cluster A. Strain KP1-50 of Cluster B was able to grow on a mineral medium under an atmosphere of H(2), O(2), and CO(2) (85:5:10), and characterized by its large-subunit gene of ribulose 1,5-bisphosphate carboxylase/oxygenase (rbcL) and nitrogenase reductase gene (nifH). In contrast, strains of Cluster A did not grow chemolithoautotrophically with H(2), O(2), and CO(2) but increased their cell biomass with the addition of thiosulfate to the succinate medium, suggesting the use of thiosulfate as an energy source. From phenotypic characterization, it was suggested that the Cluster A and B strains were novel species in the genus Limnobacter and Herbaspirillum, respectively.

Planck/SDSS cluster mass and gas scaling relations for a volume-complete redMaPPer sample

NASA Astrophysics Data System (ADS)

Jimeno, Pablo; Diego, Jose M.; Broadhurst, Tom; De Martino, I.; Lazkoz, Ruth

2018-07-01

Using Planck satellite data, we construct Sunyaev-Zel'dovich (SZ) gas pressure profiles for a large, volume-complete sample of optically selected clusters. We have defined a sample of over 8000 redMaPPer clusters from the Sloan Digital Sky Survey, within the volume-complete redshift region 0.100

Structural parameters of young star clusters: fractal analysis

NASA Astrophysics Data System (ADS)

Hetem, A.

2017-07-01

A unified view of star formation in the Universe demand detailed and in-depth studies of young star clusters. This work is related to our previous study of fractal statistics estimated for a sample of young stellar clusters (Gregorio-Hetem et al. 2015, MNRAS 448, 2504). The structural properties can lead to significant conclusions about the early stages of cluster formation: 1) virial conditions can be used to distinguish warm collapsed; 2) bound or unbound behaviour can lead to conclusions about expansion; and 3) fractal statistics are correlated to the dynamical evolution and age. The technique of error bars estimation most used in the literature is to adopt inferential methods (like bootstrap) to estimate deviation and variance, which are valid only for an artificially generated cluster. In this paper, we expanded the number of studied clusters, in order to enhance the investigation of the cluster properties and dynamic evolution. The structural parameters were compared with fractal statistics and reveal that the clusters radial density profile show a tendency of the mean separation of the stars increase with the average surface density. The sample can be divided into two groups showing different dynamic behaviour, but they have the same dynamic evolution, since the entire sample was revealed as being expanding objects, for which the substructures do not seem to have been completely erased. These results are in agreement with the simulations adopting low surface densities and supervirial conditions.

Clustering and flow around a sphere moving into a grain cloud.

PubMed

Seguin, A; Lefebvre-Lepot, A; Faure, S; Gondret, P

2016-06-01

A bidimensional simulation of a sphere moving at constant velocity into a cloud of smaller spherical grains far from any boundaries and without gravity is presented with a non-smooth contact dynamics method. A dense granular "cluster" zone builds progressively around the moving sphere until a stationary regime appears with a constant upstream cluster size. The key point is that the upstream cluster size increases with the initial solid fraction [Formula: see text] but the cluster packing fraction takes an about constant value independent of [Formula: see text]. Although the upstream cluster size around the moving sphere diverges when [Formula: see text] approaches a critical value, the drag force exerted by the grains on the sphere does not. The detailed analysis of the local strain rate and local stress fields made in the non-parallel granular flow inside the cluster allows us to extract the local invariants of the two tensors: dilation rate, shear rate, pressure and shear stress. Despite different spatial variations of these invariants, the local friction coefficient μ appears to depend only on the local inertial number I as well as the local solid fraction, which means that a local rheology does exist in the present non-parallel flow. The key point is that the spatial variations of I inside the cluster do not depend on the sphere velocity and explore only a small range around the value one.

Towards Development of Clustering Applications for Large-Scale Comparative Genotyping and Kinship Analysis Using Y-Short Tandem Repeats.

PubMed

Seman, Ali; Sapawi, Azizian Mohd; Salleh, Mohd Zaki

2015-06-01

Y-chromosome short tandem repeats (Y-STRs) are genetic markers with practical applications in human identification. However, where mass identification is required (e.g., in the aftermath of disasters with significant fatalities), the efficiency of the process could be improved with new statistical approaches. Clustering applications are relatively new tools for large-scale comparative genotyping, and the k-Approximate Modal Haplotype (k-AMH), an efficient algorithm for clustering large-scale Y-STR data, represents a promising method for developing these tools. In this study we improved the k-AMH and produced three new algorithms: the Nk-AMH I (including a new initial cluster center selection), the Nk-AMH II (including a new dominant weighting value), and the Nk-AMH III (combining I and II). The Nk-AMH III was the superior algorithm, with mean clustering accuracy that increased in four out of six datasets and remained at 100% in the other two. Additionally, the Nk-AMH III achieved a 2% higher overall mean clustering accuracy score than the k-AMH, as well as optimal accuracy for all datasets (0.84-1.00). With inclusion of the two new methods, the Nk-AMH III produced an optimal solution for clustering Y-STR data; thus, the algorithm has potential for further development towards fully automatic clustering of any large-scale genotypic data.

Spectroscopic Confirmation of Five Galaxy Clusters at z > 1.25 in the 2500 deg^2 SPT-SZ Survey

NASA Astrophysics Data System (ADS)

Khullar, Gourav; Bleem, Lindsey; Bayliss, Matthew; Gladders, Michael; South Pole Telescope (SPT) Collaboration

2018-06-01

We present spectroscopic confirmation of 5 galaxy clusters at 1.25 < z < 1.5, discovered in the 2500 deg2 South Pole Telescope Sunyaev-Zel’dovich (SPT-SZ) survey. These clusters, taken from a nearly redshift-independent mass-limited sample of clusters, have multi-wavelength follow-up imaging data from the X-ray to the near-IR, and currently form the most homogenous massive high-redshift cluster sample in existence. We briefly describe the analysis pipeline used on the low S/N spectra of these faint galaxies, and describing the multiple techniques used to extract robust redshifts from a combination of absorption-line (Ca II H&K doublet - λλ3934,3968Å) and emission-line ([OII] λλ3727,3729Å) spectral features. We present several ensemble analyses of cluster member galaxies that demonstrate the reliability of the measured redshifts. We also identify modest [OII] emission and pronounced CN and Hδ absorption in a composite stacked spectrum of 28 low S/N passive galaxy spectra with redshifts derived primarily from Ca II H&K features. This work increases the number of spectroscopically-confirmed SPT-SZ galaxy clusters at z > 1.25 from 2 to 7, further demonstrating the efficacy of SZ selection for the highest redshift massive clusters, and enabling further detailed study of these confirmed systems.

Batch Computed Tomography Analysis of Projectiles

DTIC Science & Technology

2016-05-01

error calculation. Projectiles are then grouped together according to the similarity of their components. Also discussed is graphical- cluster analysis...ballistic, armor, grouping, clustering 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF...Fig. 10 Graphical structure of 15 clusters of the jacket/core radii profiles with plots of the profiles contained within each cluster . The size of

Investigating Faculty Familiarity with Assessment Terminology by Applying Cluster Analysis to Interpret Survey Data

ERIC Educational Resources Information Center

Raker, Jeffrey R.; Holme, Thomas A.

2014-01-01

A cluster analysis was conducted with a set of survey data on chemistry faculty familiarity with 13 assessment terms. Cluster groupings suggest a high, middle, and low overall familiarity with the terminology and an independent high and low familiarity with terms related to fundamental statistics. The six resultant clusters were found to be…

Relationship between damage clustering and mortality in systemic lupus erythematosus in early and late stages of the disease: cluster analyses in a large cohort from the Spanish Society of Rheumatology Lupus Registry.

PubMed

Pego-Reigosa, José María; Lois-Iglesias, Ana; Rúa-Figueroa, Íñigo; Galindo, María; Calvo-Alén, Jaime; de Uña-Álvarez, Jacobo; Balboa-Barreiro, Vanessa; Ibáñez Ruan, Jesús; Olivé, Alejandro; Rodríguez-Gómez, Manuel; Fernández Nebro, Antonio; Andrés, Mariano; Erausquin, Celia; Tomero, Eva; Horcada Rubio, Loreto; Uriarte Isacelaya, Esther; Freire, Mercedes; Montilla, Carlos; Sánchez-Atrio, Ana I; Santos-Soler, Gregorio; Zea, Antonio; Díez, Elvira; Narváez, Javier; Blanco-Alonso, Ricardo; Silva-Fernández, Lucía; Ruiz-Lucea, María Esther; Fernández-Castro, Mónica; Hernández-Beriain, José Ángel; Gantes-Mora, Marian; Hernández-Cruz, Blanca; Pérez-Venegas, José; Pecondón-Español, Ángela; Marras Fernández-Cid, Carlos; Ibáñez-Barcelo, Mónica; Bonilla, Gema; Torrente-Segarra, Vicenç; Castellví, Iván; Alegre, Juan José; Calvet, Joan; Marenco de la Fuente, José Luis; Raya, Enrique; Vázquez-Rodríguez, Tomás Ramón; Quevedo-Vila, Víctor; Muñoz-Fernández, Santiago; Otón, Teresa; Rahman, Anisur; López-Longo, Francisco Javier

2016-07-01

To identify patterns (clusters) of damage manifestations within a large cohort of SLE patients and evaluate the potential association of these clusters with a higher risk of mortality. This is a multicentre, descriptive, cross-sectional study of a cohort of 3656 SLE patients from the Spanish Society of Rheumatology Lupus Registry. Organ damage was ascertained using the Systemic Lupus International Collaborating Clinics Damage Index. Using cluster analysis, groups of patients with similar patterns of damage manifestations were identified. Then, overall clusters were compared as well as the subgroup of patients within every cluster with disease duration shorter than 5 years. Three damage clusters were identified. Cluster 1 (80.6% of patients) presented a lower amount of individuals with damage (23.2 vs 100% in clusters 2 and 3, P < 0.001). Cluster 2 (11.4% of patients) was characterized by musculoskeletal damage in all patients. Cluster 3 (8.0% of patients) was the only group with cardiovascular damage, and this was present in all patients. The overall mortality rate of patients in clusters 2 and 3 was higher than that in cluster 1 (P < 0.001 for both comparisons) and in patients with disease duration shorter than 5 years as well. In a large cohort of SLE patients, cardiovascular and musculoskeletal damage manifestations were the two dominant forms of damage to sort patients into clinically meaningful clusters. Both in early and late stages of the disease, there was a significant association of these clusters with an increased risk of mortality. Physicians should pay special attention to the early prevention of damage in these two systems. © The Author 2016. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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.

NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways.

PubMed

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Sand, Olivier; Janky, Rekin's; Vanderstocken, Gilles; Deville, Yves; van Helden, Jacques

2008-07-01

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources.

Identification and characterization of near-fatal asthma phenotypes by cluster analysis.

PubMed

Serrano-Pariente, J; Rodrigo, G; Fiz, J A; Crespo, A; Plaza, V

2015-09-01

Near-fatal asthma (NFA) is a heterogeneous clinical entity and several profiles of patients have been described according to different clinical, pathophysiological and histological features. However, there are no previous studies that identify in a unbiased way--using statistical methods such as clusters analysis--different phenotypes of NFA. Therefore, the aim of the present study was to identify and to characterize phenotypes of near fatal asthma using a cluster analysis. Over a period of 2 years, 33 Spanish hospitals enrolled 179 asthmatics admitted for an episode of NFA. A cluster analysis using two-steps algorithm was performed from data of 84 of these cases. The analysis defined three clusters of patients with NFA: cluster 1, the largest, including older patients with clinical and therapeutic criteria of severe asthma; cluster 2, with an high proportion of respiratory arrest (68%), impaired consciousness level (82%) and mechanical ventilation (93%); and cluster 3, which included younger patients, characterized by an insufficient anti-inflammatory treatment and frequent sensitization to Alternaria alternata and soybean. These results identify specific asthma phenotypes involved in NFA, confirming in part previous findings observed in studies with a clinical approach. The identification of patients with a specific NFA phenotype could suggest interventions to prevent future severe asthma exacerbations. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Strategic groups, performance, and strategic response in the nursing home industry.

PubMed Central

Zinn, J S; Aaronson, W E; Rosko, M D

1994-01-01

OBJECTIVE. This study examines the effect of strategic group membership on nursing home performance and strategic behavior. DATA SOURCES AND STUDY SETTING. Data from the 1987 Medicare and Medicaid Automated Certification Survey were combined with data from the 1987 and 1989 Pennsylvania Long Term Care Facility Questionnaire. The sample consisted of 383 Pennsylvania nursing homes. STUDY DESIGN. Cluster analysis was used to place the 383 nursing homes into strategic groups on the basis of variables measuring scope and resource deployment. Performance was measured by indicators of the quality of nursing home care (rates of pressure ulcers, catheterization, and restraint usage) and efficiency in services provision. Changes in Medicare participation after passage of the 1988 Medicare Catastrophic Coverage Act (MCCA) measured strategic behavior. MANOVA and Turkey HSD post hoc means tests determined if significant differences were associated with strategic group membership. FINDINGS. Cluster analysis produced an optimal seven-group solution. Differences in group means were significant for the clustering, performance, and conduct variables (p < .0001). Strategic groups characterized by facilities providing a continuum of care services had the best patient care outcomes. The most efficient groups were characterized by facilities with high Medicare census. While all strategic groups increased Medicare census following passage of the MCCA, those dominated by for-profits had the greatest increases. CONCLUSIONS. Our analysis demonstrates that strategic orientation influences nursing home response to regulatory initiatives, a factor that should be recognized in policy formation directed at nursing home reform. PMID:8005789

Weighing the giants- V. Galaxy cluster scaling relations

NASA Astrophysics Data System (ADS)

Mantz, Adam B.; Allen, Steven W.; Morris, R. Glenn; von der Linden, Anja; Applegate, Douglas E.; Kelly, Patrick L.; Burke, David L.; Donovan, David; Ebeling, Harald

2016-12-01

We present constraints on the scaling relations of galaxy cluster X-ray luminosity, temperature and gas mass (and derived quantities) with mass and redshift, employing masses from robust weak gravitational lensing measurements. These are the first such results obtained from an analysis that simultaneously accounts for selection effects and the underlying mass function, and directly incorporates lensing data to constrain total masses. Our constraints on the scaling relations and their intrinsic scatters are in good agreement with previous studies, and reinforce a picture in which departures from self-similar scaling laws are primarily limited to cluster cores. However, the data are beginning to reveal new features that have implications for cluster astrophysics and provide new tests for hydrodynamical simulations. We find a positive correlation in the intrinsic scatters of luminosity and temperature at fixed mass, which is related to the dynamical state of the clusters. While the evolution of the nominal scaling relations over the redshift range 0.0 < z < 0.5 is consistent with self-similarity, we find tentative evidence that the luminosity and temperature scatters, respectively, decrease and increase with redshift. Physically, this likely related to the development of cool cores and the rate of major mergers. We also examine the scaling relations of redMaPPer richness and Compton Y from Planck. While the richness-mass relation is in excellent agreement with recent work, the measured Y-mass relation departs strongly from that assumed in the Planck cluster cosmology analysis. The latter result is consistent with earlier comparisons of lensing and Planck scaling relation-derived masses.

Constraining AGN triggering mechanisms through the clustering analysis of active black holes

NASA Astrophysics Data System (ADS)

Gatti, M.; Shankar, F.; Bouillot, V.; Menci, N.; Lamastra, A.; Hirschmann, M.; Fiore, F.

2016-02-01

The triggering mechanisms for active galactic nuclei (AGN) are still debated. Some of the most popular ones include galaxy interactions (IT) and disc instabilities (DIs). Using an advanced semi-analytic model (SAM) of galaxy formation, coupled to accurate halo occupation distribution modelling, we investigate the imprint left by each separate triggering process on the clustering strength of AGN at small and large scales. Our main results are as follows: (I) DIs, irrespective of their exact implementation in the SAM, tend to fall short in triggering AGN activity in galaxies at the centre of haloes with Mh > 1013.5 h-1 M⊙. On the contrary, the IT scenario predicts abundance of active central galaxies that generally agrees well with observations at every halo mass. (II) The relative number of satellite AGN in DIs at intermediate-to-low luminosities is always significantly higher than in IT models, especially in groups and clusters. The low AGN satellite fraction predicted for the IT scenario might suggest that different feeding modes could simultaneously contribute to the triggering of satellite AGN. (III) Both scenarios are quite degenerate in matching large-scale clustering measurements, suggesting that the sole average bias might not be an effective observational constraint. (IV) Our analysis suggests the presence of both a mild luminosity and a more consistent redshift dependence in the AGN clustering, with AGN inhabiting progressively less massive dark matter haloes as the redshift increases. We also discuss the impact of different observational selection cuts in measuring AGN clustering, including possible discrepancies between optical and X-ray surveys.

Erratum: Weighing the giants – V. Galaxy cluster scaling relations

DOE PAGES

Mantz, Adam B.; Allen, Steven W.; Morris, R. Glenn; ...

2017-02-21

We present constraints on the scaling relations of galaxy cluster X-ray luminosity, temperature and gas mass (and derived quantities) with mass and redshift, employing masses from robust weak gravitational lensing measurements. These are the first such results obtained from an analysis that simultaneously accounts for selection effects and the underlying mass function, and directly incorporates lensing data to constrain total masses. Our constraints on the scaling relations and their intrinsic scatters are in good agreement with previous studies, and reinforce a picture in which departures from self-similar scaling laws are primarily limited to cluster cores. However, the data are beginningmore » to reveal new features that have implications for cluster astrophysics and provide new tests for hydrodynamical simulations. We find a positive correlation in the intrinsic scatters of luminosity and temperature at fixed mass, which is related to the dynamical state of the clusters. While the evolution of the nominal scaling relations over the redshift range 0.0 < z < 0.5 is consistent with self similarity, we find tentative evidence that the luminosity and temperature scatters respectively decrease and increase with redshift. Physically, this likely related to the development of cool cores and the rate of major mergers. We also examine the scaling relations of redMaPPer richness and Compton Y from Planck. While the richness{mass relation is in excellent agreement with recent work, the measured Y {mass relation departs strongly from that assumed in the Planck cluster cosmology analysis. Furthermore, the latter result is consistent with earlier comparisons of lensing and Planck scaling-relation-derived masses.« less

When the wind goes out of the sail - declining recovery expectations in the first weeks of back pain.

PubMed

Carstens, J K P; Shaw, W S; Boersma, K; Reme, S E; Pransky, G; Linton, S J

2014-02-01

Expectations for recovery are a known predictor for returning to work. Most studies seem to conclude that the higher the expectancy the better the outcome. However, the development of expectations over time is rarely researched and experimental studies show that realistic expectations rather than high expectancies are the most adaptive. This study aims to explore patterns of stability and change in expectations for recovery during the first weeks of a back-pain episode and how these patterns relate to other psychological variables and outcome. The study included 496 volunteer patients seeking treatment for work-related, acute back pain. The participants were measured with self-report scales of depression, fear of pain, life impact of pain, catastrophizing and expectations for recovery at two time points. A follow-up focusing on recovery and return to work was conducted 3 months later. A cluster analysis was conducted, categorizing the data on the trajectories of recovery expectations. Cluster analysis revealed four clusters regarding the development of expectations for recovery during a 2-week period after pain onset. Three out of four clusters showed stability in their expectations as well as corresponding levels of proximal psychological factors. The fourth cluster showed increases in distress and a decrease in expectations for recovery. This cluster also has poor odds ratios for returning to work and recovery. Decreases in expectancies for recovery seem as important as baseline values in terms of outcome, which has clinical and theoretical implications. © 2013 European Pain Federation - EFIC®

Characteristics of HIV-infected U.S. Army soldiers linked in molecular transmission clusters, 2001-2012

PubMed Central

Jagodzinski, Linda L.; Liu, Ying; Pham, Peter T.; Kijak, Gustavo H.; Tovanabutra, Sodsai; McCutchan, Francine E.; Scoville, Stephanie L.; Cersovsky, Steven B.; Michael, Nelson L.; Scott, Paul T.; Peel, Sheila A.

2017-01-01

Objective Recent surveillance data suggests the United States (U.S.) Army HIV epidemic is concentrated among men who have sex with men. To identify potential targets for HIV prevention strategies, the relationship between demographic and clinical factors and membership within transmission clusters based on baseline pol sequences of HIV-infected Soldiers from 2001 through 2012 were analyzed. Methods We conducted a retrospective analysis of baseline partial pol sequences, demographic and clinical characteristics available for all Soldiers in active service and newly-diagnosed with HIV-1 infection from January 1, 2001 through December 31, 2012. HIV-1 subtype designations and transmission clusters were identified from phylogenetic analysis of sequences. Univariate and multivariate logistic regression models were used to evaluate and adjust for the association between characteristics and cluster membership. Results Among 518 of 995 HIV-infected Soldiers with available partial pol sequences, 29% were members of a transmission cluster. Assignment to a southern U.S. region at diagnosis and year of diagnosis were independently associated with cluster membership after adjustment for other significant characteristics (p<0.10) of age, race, year of diagnosis, region of duty assignment, sexually transmitted infections, last negative HIV test, antiretroviral therapy, and transmitted drug resistance. Subtyping of the pol fragment indicated HIV-1 subtype B infection predominated (94%) among HIV-infected Soldiers. Conclusion These findings identify areas to explore as HIV prevention targets in the U.S. Army. An increased frequency of current force testing may be justified, especially among Soldiers assigned to duty in installations with high local HIV prevalence such as southern U.S. states. PMID:28759645

Weighing the giants– V. Galaxy cluster scaling relations

DOE PAGES

Mantz, Adam B.; Allen, Steven W.; Morris, R. Glenn; ...

2016-09-07

Here, we present constraints on the scaling relations of galaxy cluster X-ray luminosity, temperature and gas mass (and derived quantities) with mass and redshift, employing masses from robust weak gravitational lensing measurements. These are the first such results obtained from an analysis that simultaneously accounts for selection effects and the underlying mass function, and directly incorporates lensing data to constrain total masses. Our constraints on the scaling relations and their intrinsic scatters are in good agreement with previous studies, and reinforce a picture in which departures from self-similar scaling laws are primarily limited to cluster cores. However, the data aremore » beginning to reveal new features that have implications for cluster astrophysics and provide new tests for hydrodynamical simulations. We find a positive correlation in the intrinsic scatters of luminosity and temperature at fixed mass, which is related to the dynamical state of the clusters. While the evolution of the nominal scaling relations over the redshift range 0.0 < z < 0.5 is consistent with self-similarity, we find tentative evidence that the luminosity and temperature scatters, respectively, decrease and increase with redshift. Physically, this likely related to the development of cool cores and the rate of major mergers. We also examine the scaling relations of redMaPPer richness and Compton Y from Planck. While the richness–mass relation is in excellent agreement with recent work, the measured Y–mass relation departs strongly from that assumed in the Planck cluster cosmology analysis. Furthermore, the latter result is consistent with earlier comparisons of lensing and Planck scaling relation-derived masses.« less