Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level changes in gene expression

PubMed Central

Poole, William; Leinonen, Kalle; Shmulevich, Ilya

2017-01-01

Cancer researchers have long recognized that somatic mutations are not uniformly distributed within genes. However, most approaches for identifying cancer mutations focus on either the entire-gene or single amino-acid level. We have bridged these two methodologies with a multiscale mutation clustering algorithm that identifies variable length mutation clusters in cancer genes. We ran our algorithm on 539 genes using the combined mutation data in 23 cancer types from The Cancer Genome Atlas (TCGA) and identified 1295 mutation clusters. The resulting mutation clusters cover a wide range of scales and often overlap with many kinds of protein features including structured domains, phosphorylation sites, and known single nucleotide variants. We statistically associated these multiscale clusters with gene expression and drug response data to illuminate the functional and clinical consequences of mutations in our clusters. Interestingly, we find multiple clusters within individual genes that have differential functional associations: these include PTEN, FUBP1, and CDH1. This methodology has potential implications in identifying protein regions for drug targets, understanding the biological underpinnings of cancer, and personalizing cancer treatments. Toward this end, we have made the mutation clusters and the clustering algorithm available to the public. Clusters and pathway associations can be interactively browsed at m2c.systemsbiology.net. The multiscale mutation clustering algorithm is available at https://github.com/IlyaLab/M2C. PMID:28170390

Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level changes in gene expression.

PubMed

Poole, William; Leinonen, Kalle; Shmulevich, Ilya; Knijnenburg, Theo A; Bernard, Brady

2017-02-01

Cancer researchers have long recognized that somatic mutations are not uniformly distributed within genes. However, most approaches for identifying cancer mutations focus on either the entire-gene or single amino-acid level. We have bridged these two methodologies with a multiscale mutation clustering algorithm that identifies variable length mutation clusters in cancer genes. We ran our algorithm on 539 genes using the combined mutation data in 23 cancer types from The Cancer Genome Atlas (TCGA) and identified 1295 mutation clusters. The resulting mutation clusters cover a wide range of scales and often overlap with many kinds of protein features including structured domains, phosphorylation sites, and known single nucleotide variants. We statistically associated these multiscale clusters with gene expression and drug response data to illuminate the functional and clinical consequences of mutations in our clusters. Interestingly, we find multiple clusters within individual genes that have differential functional associations: these include PTEN, FUBP1, and CDH1. This methodology has potential implications in identifying protein regions for drug targets, understanding the biological underpinnings of cancer, and personalizing cancer treatments. Toward this end, we have made the mutation clusters and the clustering algorithm available to the public. Clusters and pathway associations can be interactively browsed at m2c.systemsbiology.net. The multiscale mutation clustering algorithm is available at https://github.com/IlyaLab/M2C.

A clustering approach to identify severe bronchiolitis profiles in children

PubMed Central

Dumas, Orianne; Mansbach, Jonathan M; Jartti, Tuomas; Hasegawa, Kohei; Sullivan, Ashley F; Piedra, Pedro A; Camargo, Carlos A

2016-01-01

Objective Although bronchiolitis is generally considered a single disease, recent studies suggest heterogeneity. We aimed to identify severe bronchiolitis profiles using a clustering approach. Methods We analyzed data from two prospective, multi-center cohorts of children younger than 2 years hospitalized with bronchiolitis, one in the U.S. (2007–2010 winter seasons, n=2,207) and one in Finland (2008–2010 winter seasons, n=408). Severe bronchiolitis profiles were determined by latent class analysis, classifying children based on clinical factors and viral etiology. Results In the U.S. study, four profiles were identified. Profile A (12%) was characterized by history of wheezing and eczema, wheezing at the ED presentation and rhinovirus infection. Profile B (36%) included children with wheezing at the ED presentation, but, in contrast to profile A, most did not have history of wheezing or eczema; this profile had the largest probability of RSV-infection. Profile C (34%) was the most severely ill group, with longer hospital stay and moderate-to-severe retractions. Profile D (17%) had the least severe illness, including non-wheezing children with shorter length-of-stay. Two of these profiles (A and D) were replicated in the Finnish cohort; a third group (“BC”) included Finnish children with characteristics of profiles B and/or C in the U.S. population. Conclusion Several distinct clinical profiles (phenotypes) were identified by a clustering approach in two multicenter studies of children hospitalized for bronchiolitis. The observed heterogeneity has important implications for future research on the etiology, management and long-term outcomes of bronchiolitis, such as future risk of childhood asthma. PMID:27339060

Identifying sighting clusters of endangered taxa with historical records.

PubMed

Duffy, Karl J

2011-04-01

The probability and time of extinction of taxa is often inferred from statistical analyses of historical records. Many of these analyses require the exclusion of multiple records within a unit of time (i.e., a month or a year). Nevertheless, spatially explicit, temporally aggregated data may be useful for identifying clusters of sightings (i.e., sighting clusters) in space and time. Identification of sighting clusters highlights changes in the historical recording of endangered taxa. I used two methods to identify sighting clusters in historical records: the Ederer-Myers-Mantel (EMM) test and the space-time permutation scan (STPS). I applied these methods to the spatially explicit sighting records of three species of orchids that are listed as endangered in the Republic of Ireland under the Wildlife Act (1976): Cephalanthera longifolia, Hammarbya paludosa, and Pseudorchis albida. Results with the EMM test were strongly affected by the choice of the time interval, and thus the number of temporal samples, used to examine the records. For example, sightings of P. albida clustered when the records were partitioned into 20-year temporal samples, but not when they were partitioned into 22-year temporal samples. Because the statistical power of EMM was low, it will not be useful when data are sparse. Nevertheless, the STPS identified regions that contained sighting clusters because it uses a flexible scanning window (defined by cylinders of varying size that move over the study area and evaluate the likelihood of clustering) to detect them, and it identified regions with high and regions with low rates of orchid sightings. The STPS analyses can be used to detect sighting clusters of endangered species that may be related to regions of extirpation and may assist in the categorization of threat status. ©2010 Society for Conservation Biology.

Merging K-means with hierarchical clustering for identifying general-shaped groups.

PubMed

Peterson, Anna D; Ghosh, Arka P; Maitra, Ranjan

2018-01-01

Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and K -means clustering are two approaches but have different strengths and weaknesses. For instance, hierarchical clustering identifies groups in a tree-like structure but suffers from computational complexity in large datasets while K -means clustering is efficient but designed to identify homogeneous spherically-shaped clusters. We present a hybrid non-parametric clustering approach that amalgamates the two methods to identify general-shaped clusters and that can be applied to larger datasets. Specifically, we first partition the dataset into spherical groups using K -means. We next merge these groups using hierarchical methods with a data-driven distance measure as a stopping criterion. Our proposal has the potential to reveal groups with general shapes and structure in a dataset. We demonstrate good performance on several simulated and real datasets.

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 Clinical Data Identifies Fibromyalgia Subgroups

PubMed Central

Docampo, Elisa; Collado, Antonio; Escaramís, Geòrgia; Carbonell, Jordi; Rivera, Javier; Vidal, Javier; Alegre, José

2013-01-01

Introduction Fibromyalgia (FM) is mainly characterized by widespread pain and multiple accompanying symptoms, which hinder FM assessment and management. In order to reduce FM heterogeneity we classified clinical data into simplified dimensions that were used to define FM subgroups. Material and Methods 48 variables were evaluated in 1,446 Spanish FM cases fulfilling 1990 ACR FM criteria. A partitioning analysis was performed to find groups of variables similar to each other. Similarities between variables were identified and the variables were grouped into dimensions. This was performed in a subset of 559 patients, and cross-validated in the remaining 887 patients. For each sample and dimension, a composite index was obtained based on the weights of the variables included in the dimension. Finally, a clustering procedure was applied to the indexes, resulting in FM subgroups. Results Variables clustered into three independent dimensions: “symptomatology”, “comorbidities” and “clinical scales”. Only the two first dimensions were considered for the construction of FM subgroups. Resulting scores classified FM samples into three subgroups: low symptomatology and comorbidities (Cluster 1), high symptomatology and comorbidities (Cluster 2), and high symptomatology but low comorbidities (Cluster 3), showing differences in measures of disease severity. Conclusions We have identified three subgroups of FM samples in a large cohort of FM by clustering clinical data. Our analysis stresses the importance of family and personal history of FM comorbidities. Also, the resulting patient clusters could indicate different forms of the disease, relevant to future research, and might have an impact on clinical assessment. PMID:24098674

Archetypal TRMM Radar Profiles Identified Through Cluster Analysis

NASA Technical Reports Server (NTRS)

Boccippio, Dennis J.

2003-01-01

It is widely held that identifiable 'convective regimes' exist in nature, although precise definitions of these are elusive. Examples include land / Ocean distinctions, break / monsoon beahvior, seasonal differences in the Amazon (SON vs DJF), etc. These regimes are often described by differences in the realized local convective spectra, and measured by various metrics of convective intensity, depth, areal coverage and rainfall amount. Objective regime identification may be valuable in several ways: regimes may serve as natural 'branch points' in satellite retrieval algorithms or data assimilation efforts; one example might be objective identification of regions that 'should' share a similar 2-R relationship. Similarly, objectively defined regimes may provide guidance on optimal siting of ground validation efforts. Objectively defined regimes could also serve as natural (rather than arbitrary geographic) domain 'controls' in studies of convective response to environmental forcing. Quantification of convective vertical structure has traditionally involved parametric study of prescribed quantities thought to be important to convective dynamics: maximum radar reflectivity, cloud top height, 30-35 dBZ echo top height, rain rate, etc. Individually, these parameters are somewhat deficient as their interpretation is often nonunique (the same metric value may signify different physics in different storm realizations). Individual metrics also fail to capture the coherence and interrelationships between vertical levels available in full 3-D radar datasets. An alternative approach is discovery of natural partitions of vertical structure in a globally representative dataset, or 'archetypal' reflectivity profiles. In this study, this is accomplished through cluster analysis of a very large sample (0[107) of TRMM-PR reflectivity columns. Once achieved, the rainconditional and unconditional 'mix' of archetypal profile types in a given location and/or season provides a description

Identifying clusters of active transportation using spatial scan statistics.

PubMed

Huang, Lan; Stinchcomb, David G; Pickle, Linda W; Dill, Jennifer; Berrigan, David

2009-08-01

There is an intense interest in the possibility that neighborhood characteristics influence active transportation such as walking or biking. The purpose of this paper is to illustrate how a spatial cluster identification method can evaluate the geographic variation of active transportation and identify neighborhoods with unusually high/low levels of active transportation. Self-reported walking/biking prevalence, demographic characteristics, street connectivity variables, and neighborhood socioeconomic data were collected from respondents to the 2001 California Health Interview Survey (CHIS; N=10,688) in Los Angeles County (LAC) and San Diego County (SDC). Spatial scan statistics were used to identify clusters of high or low prevalence (with and without age-adjustment) and the quantity of time spent walking and biking. The data, a subset from the 2001 CHIS, were analyzed in 2007-2008. Geographic clusters of significantly high or low prevalence of walking and biking were detected in LAC and SDC. Structural variables such as street connectivity and shorter block lengths are consistently associated with higher levels of active transportation, but associations between active transportation and socioeconomic variables at the individual and neighborhood levels are mixed. Only one cluster with less time spent walking and biking among walkers/bikers was detected in LAC, and this was of borderline significance. Age-adjustment affects the clustering pattern of walking/biking prevalence in LAC, but not in SDC. The use of spatial scan statistics to identify significant clustering of health behaviors such as active transportation adds to the more traditional regression analysis that examines associations between behavior and environmental factors by identifying specific geographic areas with unusual levels of the behavior independent of predefined administrative units.

Identifying Clusters of Active Transportation Using Spatial Scan Statistics

PubMed Central

Huang, Lan; Stinchcomb, David G.; Pickle, Linda W.; Dill, Jennifer; Berrigan, David

2009-01-01

Background There is an intense interest in the possibility that neighborhood characteristics influence active transportation such as walking or biking. The purpose of this paper is to illustrate how a spatial cluster identification method can evaluate the geographic variation of active transportation and identify neighborhoods with unusually high/low levels of active transportation. Methods Self-reported walking/biking prevalence, demographic characteristics, street connectivity variables, and neighborhood socioeconomic data were collected from respondents to the 2001 California Health Interview Survey (CHIS; N=10,688) in Los Angeles County (LAC) and San Diego County (SDC). Spatial scan statistics were used to identify clusters of high or low prevalence (with and without age-adjustment) and the quantity of time spent walking and biking. The data, a subset from the 2001 CHIS, were analyzed in 2007–2008. Results Geographic clusters of significantly high or low prevalence of walking and biking were detected in LAC and SDC. Structural variables such as street connectivity and shorter block lengths are consistently associated with higher levels of active transportation, but associations between active transportation and socioeconomic variables at the individual and neighborhood levels are mixed. Only one cluster with less time spent walking and biking among walkers/bikers was detected in LAC, and this was of borderline significance. Age-adjustment affects the clustering pattern of walking/biking prevalence in LAC, but not in SDC. Conclusions The use of spatial scan statistics to identify significant clustering of health behaviors such as active transportation adds to the more traditional regression analysis that examines associations between behavior and environmental factors by identifying specific geographic areas with unusual levels of the behavior independent of predefined administrative units. PMID:19589451

CHEMICAL TAGGING OF THREE DISTINCT POPULATIONS OF RED GIANTS IN THE GLOBULAR CLUSTER NGC 6752

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

Carretta, E.; Bragaglia, A.; Gratton, R. G.

2012-05-01

We present aluminum, magnesium, and silicon abundances in the metal-poor globular cluster NGC 6752 for a sample of more than 130 red giants with homogeneous oxygen and sodium abundances. We find that [Al/Fe] shows a spread of about 1.4 dex among giants in NGC 6752 and is anticorrelated with [Mg/Fe] and [O/Fe] and correlated with [Na/Fe] and [Si/Fe]. These relations are not continuous in nature, but the distribution of stars is clearly clustered around three distinct Al values, low, intermediate, and high. These three groups nicely correspond to the three distinct sequences previously detected using Stroemgren photometry along the redmore » giant branch. These two independent findings strongly indicate the existence of three distinct stellar populations in NGC 6752. Comparing the abundances of O and Mg, we find that the population with intermediate chemical abundances cannot originate from material with the same composition of the most O- and Mg-poor population, diluted by material with that of the most O- and Mg-rich one. This calls for different polluters.« less

Early and long-standing rheumatoid arthritis: distinct molecular signatures identified by gene-expression profiling in synovia

PubMed Central

Lequerré, Thierry; Bansard, Carine; Vittecoq, Olivier; Derambure, Céline; Hiron, Martine; Daveau, Maryvonne; Tron, François; Ayral, Xavier; Biga, Norman; Auquit-Auckbur, Isabelle; Chiocchia, Gilles; Le Loët, Xavier; Salier, Jean-Philippe

2009-01-01

Introduction Rheumatoid arthritis (RA) is a heterogeneous disease and its underlying molecular mechanisms are still poorly understood. Because previous microarray studies have only focused on long-standing (LS) RA compared to osteoarthritis, we aimed to compare the molecular profiles of early and LS RA versus control synovia. Methods Synovial biopsies were obtained by arthroscopy from 15 patients (4 early untreated RA, 4 treated LS RA and 7 controls, who had traumatic or mechanical lesions). Extracted mRNAs were used for large-scale gene-expression profiling. The different gene-expression combinations identified by comparison of profiles of early, LS RA and healthy synovia were linked to the biological processes involved in each situation. Results Three combinations of 719, 116 and 52 transcripts discriminated, respectively, early from LS RA, and early or LS RA from healthy synovia. We identified several gene clusters and distinct molecular signatures specifically expressed during early or LS RA, thereby suggesting the involvement of different pathophysiological mechanisms during the course of RA. Conclusions Early and LS RA have distinct molecular signatures with different biological processes participating at different times during the course of the disease. These results suggest that better knowledge of the main biological processes involved at a given RA stage might help to choose the most appropriate treatment. PMID:19563633

Identifying knowledge activism in worker health and safety representation: A cluster analysis.

PubMed

Hall, Alan; Oudyk, John; King, Andrew; Naqvi, Syed; Lewchuk, Wayne

2016-01-01

Although worker representation in OHS has been widely recognized as contributing to health and safety improvements at work, few studies have examined the role that worker representatives play in this process. Using a large quantitative sample, this paper seeks to confirm findings from an earlier exploratory qualitative study that worker representatives can be differentiated by the knowledge intensive tactics and strategies that they use to achieve changes in their workplace. Just under 900 worker health and safety representatives in Ontario completed surveys which asked them to report on the amount of time they devoted to different types of representation activities (i.e., technical activities such as inspections and report writing vs. political activities such as mobilizing workers to build support), the kinds of conditions or hazards they tried to address through their representation (e.g., housekeeping vs. modifications in ventilation systems), and their reported success in making positive improvements. A cluster analysis was used to determine whether the worker representatives could be distinguished in terms of the relative time devoted to different activities and the clusters were then compared with reference to types of intervention efforts and outcomes. The cluster analysis identified three distinct groupings of representatives with significant differences in reported types of interventions and in their level of reported impact. Two of the clusters were consistent with the findings in the exploratory study, identified as knowledge activism for greater emphasis on knowledge based political activity and technical-legal representation for greater emphasis on formalized technical oriented procedures and legal regulations. Knowledge activists were more likely to take on challenging interventions and they reported more impact across the full range of interventions. This paper provides further support for the concepts of knowledge activism and technical

Identifying seizure clusters in patients with psychogenic nonepileptic seizures.

PubMed

Baird, Grayson L; Harlow, Lisa L; Machan, Jason T; Thomas, Dave; LaFrance, W C

2017-08-01

The present study explored how seizure clusters may be defined for those with psychogenic nonepileptic seizures (PNES), a topic for which there is a paucity of literature. The sample was drawn from a multisite randomized clinical trial for PNES; seizure data are from participants' seizure diaries. Three possible cluster definitions were examined: 1) common clinical definition, where ≥3 seizures in a day is considered a cluster, along with two novel statistical definitions, where ≥3 seizures in a day are considered a cluster if the observed number of seizures statistically exceeds what would be expected relative to a patient's: 1) average seizure rate prior to the trial, 2) observed seizure rate for the previous seven days. Prevalence of clusters was 62-68% depending on cluster definition used, and occurrence rate of clusters was 6-19% depending on cluster definition. Based on these data, clusters seem to be common in patients with PNES, and more research is needed to identify if clusters are related to triggers and outcomes. Copyright © 2017 Elsevier Inc. All rights reserved.

Clustering approaches to identifying gene expression patterns from DNA microarray data.

PubMed

Do, Jin Hwan; Choi, Dong-Kug

2008-04-30

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

Cluster Analysis Identifies 3 Phenotypes within Allergic Asthma.

PubMed

Sendín-Hernández, María Paz; Ávila-Zarza, Carmelo; Sanz, Catalina; García-Sánchez, Asunción; Marcos-Vadillo, Elena; Muñoz-Bellido, Francisco J; Laffond, Elena; Domingo, Christian; Isidoro-García, María; Dávila, Ignacio

Asthma is a heterogeneous chronic disease with different clinical expressions and responses to treatment. In recent years, several unbiased approaches based on clinical, physiological, and molecular features have described several phenotypes of asthma. Some phenotypes are allergic, but little is known about whether these phenotypes can be further subdivided. We aimed to phenotype patients with allergic asthma using an unbiased approach based on multivariate classification techniques (unsupervised hierarchical cluster analysis). From a total of 54 variables of 225 patients with well-characterized allergic asthma diagnosed following American Thoracic Society (ATS) recommendation, positive skin prick test to aeroallergens, and concordant symptoms, we finally selected 19 variables by multiple correspondence analyses. Then a cluster analysis was performed. Three groups were identified. Cluster 1 was constituted by patients with intermittent or mild persistent asthma, without family antecedents of atopy, asthma, or rhinitis. This group showed the lowest total IgE levels. Cluster 2 was constituted by patients with mild asthma with a family history of atopy, asthma, or rhinitis. Total IgE levels were intermediate. Cluster 3 included patients with moderate or severe persistent asthma that needed treatment with corticosteroids and long-acting β-agonists. This group showed the highest total IgE levels. We identified 3 phenotypes of allergic asthma in our population. Furthermore, we described 2 phenotypes of mild atopic asthma mainly differentiated by a family history of allergy. Copyright © 2017 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Neuro- and social-cognitive clustering highlights distinct profiles in adults with anorexia nervosa.

PubMed

Renwick, Beth; Musiat, Peter; Lose, Anna; DeJong, Hannah; Broadbent, Hannah; Kenyon, Martha; Loomes, Rachel; Watson, Charlotte; Ghelani, Shreena; Serpell, Lucy; Richards, Lorna; Johnson-Sabine, Eric; Boughton, Nicky; Treasure, Janet; Schmidt, Ulrike

2015-01-01

This study aimed to explore the neuro- and social-cognitive profile of a consecutive series of adult outpatients with anorexia nervosa (AN) when compared with widely available age and gender matched historical control data. The relationship between performance profiles, clinical characteristics, service utilization, and treatment adherence was also investigated. Consecutively recruited outpatients with a broad diagnosis of AN (restricting subtype AN-R: n = 44, binge-purge subtype AN-BP: n = 33 or Eating Disorder Not Otherwise Specified-AN subtype EDNOS-AN: n = 23) completed a comprehensive set of neurocognitive (set-shifting, central coherence) and social-cognitive measures (Emotional Theory of Mind). Data were subjected to hierarchical cluster analysis and a discriminant function analysis. Three separate, meaningful clusters emerged. Cluster 1 (n = 45) showed overall average to high average neuro- and social- cognitive performance, Cluster 2 (n = 38) showed mixed performance characterized by distinct strengths and weaknesses, and Cluster 3 (n = 17) showed poor overall performance (Autism Spectrum disorder (ASD) like cluster). The three clusters did not differ in terms of eating disorder symptoms, comorbid features or service utilization and treatment adherence. A discriminant function analysis confirmed that the clusters were best characterized by performance in perseveration and set-shifting measures. The findings suggest that considerable neuro- and social-cognitive heterogeneity exists in patients with AN, with a subset showing ASD-like features. The value of this method of profiling in predicting longer term patient outcomes and in guiding development of etiologically targeted treatments remains to be seen. © 2014 Wiley Periodicals, Inc.

The High and Low Molecular Weight Forms of Hyaluronan Have Distinct Effects on CD44 Clustering*

PubMed Central

Yang, Cuixia; Cao, Manlin; Liu, Hua; He, Yiqing; Xu, Jing; Du, Yan; Liu, Yiwen; Wang, Wenjuan; Cui, Lian; Hu, Jiajie; Gao, Feng

2012-01-01

CD44 is a major cell surface receptor for the glycosaminoglycan hyaluronan (HA). Native high molecular weight hyaluronan (nHA) and oligosaccharides of hyaluronan (oHA) provoke distinct biological effects upon binding to CD44. Despite the importance of such interactions, however, the feature of binding with CD44 at the cell surface and the molecular basis for functional distinction between different sizes of HA is still unclear. In this study we investigated the effects of high and low molecular weight hyaluronan on CD44 clustering. For the first time, we provided direct evidence for a strong relationship between HA size and CD44 clustering in vivo. In CD44-transfected COS-7 cells, we showed that exogenous nHA stimulated CD44 clustering, which was disrupted by oHA. Moreover, naturally expressed CD44 was distributed into clusters due to abundantly expressed nHA in HK-2 cells (human renal proximal tubule cells) and BT549 cells (human breast cancer cell line) without exogenous stimulation. Our results suggest that native HA binding to CD44 selectively induces CD44 clustering, which could be inhibited by oHA. Finally, we demonstrated that HA regulates cell adhesion in a manner specifically dependent on its size. oHA promoted cell adhesion while nHA showed no effects. Our results might elucidate a molecular- and/or cellular-based mechanism for the diverse biological activities of nHA and oHA. PMID:23118219

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

Two distinct sequences of blue straggler stars in the globular cluster M 30.

PubMed

Ferraro, F R; Beccari, G; Dalessandro, E; Lanzoni, B; Sills, A; Rood, R T; Pecci, F Fusi; Karakas, A I; Miocchi, P; Bovinelli, S

2009-12-24

Stars in globular clusters are generally believed to have all formed at the same time, early in the Galaxy's history. 'Blue stragglers' are stars massive enough that they should have evolved into white dwarfs long ago. Two possible mechanisms have been proposed for their formation: mass transfer between binary companions and stellar mergers resulting from direct collisions between two stars. Recently the binary explanation was claimed to be dominant. Here we report that there are two distinct parallel sequences of blue stragglers in M 30. This globular cluster is thought to have undergone 'core collapse', during which both the collision rate and the mass transfer activity in binary systems would have been enhanced. We suggest that the two observed sequences are a consequence of cluster core collapse, with the bluer population arising from direct stellar collisions and the redder one arising from the evolution of close binaries that are probably still experiencing an active phase of mass transfer.

MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma

PubMed Central

Sass, Steffen; Pitea, Adriana; Unger, Kristian; Hess, Julia; Mueller, Nikola S.; Theis, Fabian J.

2015-01-01

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method “miRlastic”, which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of mi

MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma.

PubMed

Sass, Steffen; Pitea, Adriana; Unger, Kristian; Hess, Julia; Mueller, Nikola S; Theis, Fabian J

2015-12-18

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method "miRlastic", which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of miRNAs that

Two distinct phenotypes of asthma in elite athletes identified by latent class analysis.

PubMed

Couto, Mariana; Stang, Julie; Horta, Luís; Stensrud, Trine; Severo, Milton; Mowinckel, Petter; Silva, Diana; Delgado, Luís; Moreira, André; Carlsen, Kai-Håkon

2015-01-01

Clusters of asthma in athletes have been insufficiently studied. Therefore, the present study aimed to characterize asthma phenotypes in elite athletes using latent class analysis (LCA) and to evaluate its association with the type of sport practiced. In the present cross-sectional study, an analysis of athletes' records was carried out in databases of the Portuguese National Anti-Doping Committee and the Norwegian School of Sport Sciences. Athletes with asthma, diagnosed according to criteria given by the International Olympic Committee, were included for LCA. Sports practiced were categorized into water, winter and other sports. Of 324 files screened, 150 files belonged to asthmatic athletes (91 Portuguese; 59 Norwegian). LCA retrieved two clusters: "atopic asthma" defined by allergic sensitization, rhinitis and allergic co-morbidities and increased exhaled nitric oxide levels; and "sports asthma", defined by exercise-induced respiratory symptoms and airway hyperesponsiveness without allergic features. The risk of developing the phenotype "sports asthma" was significantly increased in athletes practicing water (OR = 2.87; 95% CI [1.82-4.51]) and winter (OR = 8.65; 95% CI [2.67-28.03]) sports, when compared with other athletes. Two asthma phenotypes were identified in elite athletes: "atopic asthma" and "sports asthma". The type of sport practiced was associated with different phenotypes: water and winter sport athletes had three- and ninefold increased risk of "sports asthma". Recognizing different phenotypes is clinically relevant as it would lead to distinct targeted treatments.

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.

Interpersonal Subtypes Within Social Anxiety: The Identification of Distinct Social Features.

PubMed

Cooper, Danielle; Anderson, Timothy

2017-10-05

Although social anxiety disorder is defined by anxiety-related symptoms, little research has focused on the interpersonal features of social anxiety. Prior studies (Cain, Pincus, & Grosse Holtforth, 2010; Kachin, Newman, & Pincus, 2001) identified distinct subgroups of socially anxious individuals' interpersonal circumplex problems that were blends of agency and communion, and yet inconsistencies remain. We predicted 2 distinct interpersonal subtypes would exist for individuals with high social anxiety, and that these social anxiety subtypes would differ on empathetic concern, paranoia, received peer victimization, perspective taking, and emotional suppression. From a sample of 175 undergraduate participants, 51 participants with high social anxiety were selected as above a clinical cutoff on the social phobia scale. Cluster analyses identified 2 interpersonal subtypes of socially anxious individuals: low hostility-high submissiveness (Cluster 1) and high hostility-high submissiveness (Cluster 2). Cluster 1 reported higher levels of empathetic concern, lower paranoia, less peer victimization, and lower emotional suppression compared to Cluster 2. There were no differences between subtypes on perspective taking or cognitive reappraisal. Findings are consistent with an interpersonal conceptualization of social anxiety, and provide evidence of distinct social features between these subtypes. Findings have implications for the etiology, classification, and treatment of social anxiety.

Identifying a gene expression signature of cluster headache in blood

PubMed Central

Eising, Else; Pelzer, Nadine; Vijfhuizen, Lisanne S.; Vries, Boukje de; Ferrari, Michel D.; ‘t Hoen, Peter A. C.; Terwindt, Gisela M.; van den Maagdenberg, Arn M. J. M.

2017-01-01

Cluster headache is a relatively rare headache disorder, typically characterized by multiple daily, short-lasting attacks of excruciating, unilateral (peri-)orbital or temporal pain associated with autonomic symptoms and restlessness. To better understand the pathophysiology of cluster headache, we used RNA sequencing to identify differentially expressed genes and pathways in whole blood of patients with episodic (n = 19) or chronic (n = 20) cluster headache in comparison with headache-free controls (n = 20). Gene expression data were analysed by gene and by module of co-expressed genes with particular attention to previously implicated disease pathways including hypocretin dysregulation. Only moderate gene expression differences were identified and no associations were found with previously reported pathogenic mechanisms. At the level of functional gene sets, associations were observed for genes involved in several brain-related mechanisms such as GABA receptor function and voltage-gated channels. In addition, genes and modules of co-expressed genes showed a role for intracellular signalling cascades, mitochondria and inflammation. Although larger study samples may be required to identify the full range of involved pathways, these results indicate a role for mitochondria, intracellular signalling and inflammation in cluster headache. PMID:28074859

Using cluster ensemble and validation to identify subtypes of pervasive developmental disorders.

PubMed

Shen, Jess J; Lee, Phil-Hyoun; Holden, Jeanette J A; Shatkay, Hagit

2007-10-11

Pervasive Developmental Disorders (PDD) are neurodevelopmental disorders characterized by impairments in social interaction, communication and behavior. Given the diversity and varying severity of PDD, diagnostic tools attempt to identify homogeneous subtypes within PDD. Identifying subtypes can lead to targeted etiology studies and to effective type-specific intervention. Cluster analysis can suggest coherent subsets in data; however, different methods and assumptions lead to different results. Several previous studies applied clustering to PDD data, varying in number and characteristics of the produced subtypes. Most studies used a relatively small dataset (fewer than 150 subjects), and all applied only a single clustering method. Here we study a relatively large dataset (358 PDD patients), using an ensemble of three clustering methods. The results are evaluated using several validation methods, and consolidated through an integration step. Four clusters are identified, analyzed and compared to subtypes previously defined by the widely used diagnostic tool DSM-IV.

Identifying the heterogeneity of young adult rhinitis through cluster analysis in the Isle of Wight birth cohort.

PubMed

Kurukulaaratchy, Ramesh J; Zhang, Hongmei; Patil, Veeresh; Raza, Abid; Karmaus, Wilfried; Ewart, Susan; Arshad, S Hasan

2015-01-01

Rhinitis affects many young adults and often shows comorbidity with asthma. We hypothesized that young adult rhinitis, like asthma, exhibits clinical heterogeneity identifiable by means of cluster analysis. Participants in the Isle of Wight birth cohort (n = 1456) were assessed at 1, 2, 4, 10, and 18 years of age. Cluster analysis was performed on those with rhinitis at age 18 years (n = 468) by using 13 variables defining clinical characteristics. Four clusters were identified. Patients in cluster 1 (n = 128 [27.4%]; ie, moderate childhood-onset rhinitis) had high atopy and eczema prevalence and high total IgE levels but low asthma prevalence. They showed the best lung function at 18 years of age, with normal fraction of exhaled nitric oxide (Feno), low bronchial hyperresponsiveness (BHR), and low bronchodilator reversibility (BDR) but high rhinitis symptoms and treatment. Patients in cluster 2 (n = 199 [42.5%]; ie, mild-adolescence-onset female rhinitis) had the lowest prevalence of comorbid atopy, asthma, and eczema. They had normal lung function and low BHR, BDR, Feno values, and total IgE levels plus low rhinitis symptoms, severity, and treatment. Patients in cluster 3 (n = 59 [12.6%]; ie, severe earliest-onset rhinitis with asthma) had the youngest rhinitis onset plus the highest comorbid asthma (of simultaneous onset) and atopy. They showed the most obstructed lung function with high BHR, BDR, and Feno values plus high rhinitis symptoms, severity, and treatment. Patient 4 in cluster 4 (n = 82 [17.5%]; ie, moderate childhood-onset male rhinitis with asthma) had high atopy, intermediate asthma, and low eczema. They had impaired lung function with high Feno values and total IgE levels but intermediate BHR and BDR. They had moderate rhinitis symptoms. Clinically distinctive adolescent rhinitis clusters are apparent with varying sex and asthma associations plus differing rhinitis severity and treatment needs. Copyright © 2014 American Academy of Allergy, Asthma

Sun Protection Belief Clusters: Analysis of Amazon Mechanical Turk Data.

PubMed

Santiago-Rivas, Marimer; Schnur, Julie B; Jandorf, Lina

2016-12-01

This study aimed (i) to determine whether people could be differentiated on the basis of their sun protection belief profiles and individual characteristics and (ii) explore the use of a crowdsourcing web service for the assessment of sun protection beliefs. A sample of 500 adults completed an online survey of sun protection belief items using Amazon Mechanical Turk. A two-phased cluster analysis (i.e., hierarchical and non-hierarchical K-means) was utilized to determine clusters of sun protection barriers and facilitators. Results yielded three distinct clusters of sun protection barriers and three distinct clusters of sun protection facilitators. Significant associations between gender, age, sun sensitivity, and cluster membership were identified. Results also showed an association between barrier and facilitator cluster membership. The results of this study provided a potential alternative approach to developing future sun protection promotion initiatives in the population. Findings add to our knowledge regarding individuals who support, oppose, or are ambivalent toward sun protection and inform intervention research by identifying distinct subtypes that may best benefit from (or have a higher need for) skin cancer prevention efforts.

Using Cluster Ensemble and Validation to Identify Subtypes of Pervasive Developmental Disorders

PubMed Central

Shen, Jess J.; Lee, Phil Hyoun; Holden, Jeanette J.A.; Shatkay, Hagit

2007-01-01

Pervasive Developmental Disorders (PDD) are neurodevelopmental disorders characterized by impairments in social interaction, communication and behavior.1 Given the diversity and varying severity of PDD, diagnostic tools attempt to identify homogeneous subtypes within PDD. Identifying subtypes can lead to targeted etiology studies and to effective type-specific intervention. Cluster analysis can suggest coherent subsets in data; however, different methods and assumptions lead to different results. Several previous studies applied clustering to PDD data, varying in number and characteristics of the produced subtypes19. Most studies used a relatively small dataset (fewer than 150 subjects), and all applied only a single clustering method. Here we study a relatively large dataset (358 PDD patients), using an ensemble of three clustering methods. The results are evaluated using several validation methods, and consolidated through an integration step. Four clusters are identified, analyzed and compared to subtypes previously defined by the widely used diagnostic tool DSM-IV.2 PMID:18693920

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

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.

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.

A cross-species bi-clustering approach to identifying conserved co-regulated genes.

PubMed

Sun, Jiangwen; Jiang, Zongliang; Tian, Xiuchun; Bi, Jinbo

2016-06-15

A growing number of studies have explored the process of pre-implantation embryonic development of multiple mammalian species. However, the conservation and variation among different species in their developmental programming are poorly defined due to the lack of effective computational methods for detecting co-regularized genes that are conserved across species. The most sophisticated method to date for identifying conserved co-regulated genes is a two-step approach. This approach first identifies gene clusters for each species by a cluster analysis of gene expression data, and subsequently computes the overlaps of clusters identified from different species to reveal common subgroups. This approach is ineffective to deal with the noise in the expression data introduced by the complicated procedures in quantifying gene expression. Furthermore, due to the sequential nature of the approach, the gene clusters identified in the first step may have little overlap among different species in the second step, thus difficult to detect conserved co-regulated genes. We propose a cross-species bi-clustering approach which first denoises the gene expression data of each species into a data matrix. The rows of the data matrices of different species represent the same set of genes that are characterized by their expression patterns over the developmental stages of each species as columns. A novel bi-clustering method is then developed to cluster genes into subgroups by a joint sparse rank-one factorization of all the data matrices. This method decomposes a data matrix into a product of a column vector and a row vector where the column vector is a consistent indicator across the matrices (species) to identify the same gene cluster and the row vector specifies for each species the developmental stages that the clustered genes co-regulate. Efficient optimization algorithm has been developed with convergence analysis. This approach was first validated on synthetic data and compared

Clustering analysis of water distribution systems: identifying critical components and community impacts.

PubMed

Diao, K; Farmani, R; Fu, G; Astaraie-Imani, M; Ward, S; Butler, D

2014-01-01

Large water distribution systems (WDSs) are networks with both topological and behavioural complexity. Thereby, it is usually difficult to identify the key features of the properties of the system, and subsequently all the critical components within the system for a given purpose of design or control. One way is, however, to more explicitly visualize the network structure and interactions between components by dividing a WDS into a number of clusters (subsystems). Accordingly, this paper introduces a clustering strategy that decomposes WDSs into clusters with stronger internal connections than external connections. The detected cluster layout is very similar to the community structure of the served urban area. As WDSs may expand along with urban development in a community-by-community manner, the correspondingly formed distribution clusters may reveal some crucial configurations of WDSs. For verification, the method is applied to identify all the critical links during firefighting for the vulnerability analysis of a real-world WDS. Moreover, both the most critical pipes and clusters are addressed, given the consequences of pipe failure. Compared with the enumeration method, the method used in this study identifies the same group of the most critical components, and provides similar criticality prioritizations of them in a more computationally efficient time.

Using earthquake clusters to identify fracture zones at Puna geothermal field, Hawaii

NASA Astrophysics Data System (ADS)

Lucas, A.; Shalev, E.; Malin, P.; Kenedi, C. L.

2010-12-01

The actively producing Puna geothermal system (PGS) is located on the Kilauea East Rift Zone (ERZ), which extends out from the active Kilauea volcano on Hawaii. In the Puna area the rift trend is identified as NE-SW from surface expressions of normal faulting with a corresponding strike; at PGS the surface expression offsets in a left step, but no rift perpendicular faulting is observed. An eight station borehole seismic network has been installed in the area of the geothermal system. Since June 2006, a total of 6162 earthquakes have been located close to or inside the geothermal system. The spread of earthquake locations follows the rift trend, but down rift to the NE of PGS almost no earthquakes are observed. Most earthquakes located within the PGS range between 2-3 km depth. Up rift to the SW of PGS the number of events decreases and the depth range increases to 3-4 km. All initial locations used Hypoinverse71 and showed no trends other than the dominant rift parallel. Double difference relocation of all earthquakes, using both catalog and cross-correlation, identified one large cluster but could not conclusively identify trends within the cluster. A large number of earthquake waveforms showed identifiable shear wave splitting. For five stations out of the six where shear wave splitting was observed, the dominant polarization direction was rift parallel. Two of the five stations also showed a smaller rift perpendicular signal. The sixth station (located close to the area of the rift offset) displayed a N-S polarization, approximately halfway between rift parallel and perpendicular. The shear wave splitting time delays indicate that fracture density is higher at the PGS compared to the surrounding ERZ. Correlation co-efficient clustering with independent P and S wave windows was used to identify clusters based on similar earthquake waveforms. In total, 40 localized clusters containing ten or more events were identified. The largest cluster was located in the

Clustering Teachers' Motivations for Teaching

ERIC Educational Resources Information Center

Visser-Wijnveen, Gerda J.; Stes, Ann; Van Petegem, Peter

2014-01-01

The motivation to teach is a powerful, yet neglected, force in teaching at institutes of higher education. A better understanding of academics' motivations for teaching is necessary. The aim of this mixed-method study was to identify groups with distinctively different motivations for teaching. Six clusters were identified: expertise, duty,…

Density-based clustering analyses to identify heterogeneous cellular sub-populations

NASA Astrophysics Data System (ADS)

Heaster, Tiffany M.; Walsh, Alex J.; Landman, Bennett A.; Skala, Melissa C.

2017-02-01

Autofluorescence microscopy of NAD(P)H and FAD provides functional metabolic measurements at the single-cell level. Here, density-based clustering algorithms were applied to metabolic autofluorescence measurements to identify cell-level heterogeneity in tumor cell cultures. The performance of the density-based clustering algorithm, DENCLUE, was tested in samples with known heterogeneity (co-cultures of breast carcinoma lines). DENCLUE was found to better represent the distribution of cell clusters compared to Gaussian mixture modeling. Overall, DENCLUE is a promising approach to quantify cell-level heterogeneity, and could be used to understand single cell population dynamics in cancer progression and treatment.

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…

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.

Iron loading site on the Fe-S cluster assembly scaffold protein is distinct from the active site.

PubMed

Rodrigues, Andria V; Kandegedara, Ashoka; Rotondo, John A; Dancis, Andrew; Stemmler, Timothy L

2015-06-01

Iron-sulfur (Fe-S) cluster containing proteins are utilized in almost every biochemical pathway. The unique redox and coordination chemistry associated with the cofactor allows these proteins to participate in a diverse set of reactions, including electron transfer, enzyme catalysis, DNA synthesis and signaling within several pathways. Due to the high reactivity of the metal, it is not surprising that biological Fe-S cluster assembly is tightly regulated within cells. In yeast, the major assembly pathway for Fe-S clusters is the mitochondrial ISC pathway. Yeast Fe-S cluster assembly is accomplished using the scaffold protein (Isu1) as the molecular foundation, with assistance from the cysteine desulfurase (Nfs1) to provide sulfur, the accessory protein (Isd11) to regulate Nfs1 activity, the yeast frataxin homologue (Yfh1) to regulate Nfs1 activity and participate in Isu1 Fe loading possibly as a chaperone, and the ferredoxin (Yah1) to provide reducing equivalents for assembly. In this report, we utilize calorimetric and spectroscopic methods to provide molecular insight into how wt-Isu1 from S. cerevisiae becomes loaded with iron. Isothermal titration calorimetry and an iron competition binding assay were developed to characterize the energetics of protein Fe(II) binding. Differential scanning calorimetry was used to identify thermodynamic characteristics of the protein in the apo state or under iron loaded conditions. Finally, X-ray absorption spectroscopy was used to characterize the electronic and structural properties of Fe(II) bound to Isu1. Current data are compared to our previous characterization of the D37A Isu1 mutant, and these suggest that when Isu1 binds Fe(II) in a manner not perturbed by the D37A substitution, and that metal binding occurs at a site distinct from the cysteine rich active site in the protein.

Identifying influential individuals on intensive care units: using cluster analysis to explore culture.

PubMed

Fong, Allan; Clark, Lindsey; Cheng, Tianyi; Franklin, Ella; Fernandez, Nicole; Ratwani, Raj; Parker, Sarah Henrickson

2017-07-01

The objective of this paper is to identify attribute patterns of influential individuals in intensive care units using unsupervised cluster analysis. Despite the acknowledgement that culture of an organisation is critical to improving patient safety, specific methods to shift culture have not been explicitly identified. A social network analysis survey was conducted and an unsupervised cluster analysis was used. A total of 100 surveys were gathered. Unsupervised cluster analysis was used to group individuals with similar dimensions highlighting three general genres of influencers: well-rounded, knowledge and relational. Culture is created locally by individual influencers. Cluster analysis is an effective way to identify common characteristics among members of an intensive care unit team that are noted as highly influential by their peers. To change culture, identifying and then integrating the influencers in intervention development and dissemination may create more sustainable and effective culture change. Additional studies are ongoing to test the effectiveness of utilising these influencers to disseminate patient safety interventions. This study offers an approach that can be helpful in both identifying and understanding influential team members and may be an important aspect of developing methods to change organisational culture. © 2017 John Wiley & Sons Ltd.

Cataloging the Praesepe Cluster: Identifying Interlopers and Binary Systems

NASA Astrophysics Data System (ADS)

Lucey, Madeline R.; Gosnell, Natalie M.; Mann, Andrew; Douglas, Stephanie

2018-01-01

We present radial velocity measurements from an ongoing survey of the Praesepe open cluster using the WIYN 3.5m Telescope. Our target stars include 229 early-K to mid-M dwarfs with proper motion memberships that have been observed by the repurposed Kepler mission, K2. With this survey, we will provide a well-constrained membership list of the cluster. By removing interloping stars and determining the cluster binary frequency we can avoid systematic errors in our analysis of the K2 findings and more accurately determine exoplanet properties in the Praesepe cluster. Obtaining accurate exoplanet parameters in open clusters allows us to study the temporal dimension of exoplanet parameter space. We find Praesepe to have a mean radial velocity of 34.09 km/s and a velocity dispersion of 1.13 km/s, which is consistent with previous studies. We derive radial velocity membership probabilities for stars with ≥3 radial velocity measurements and compare against published membership probabilities. We also identify radial velocity variables and potential double-lined spectroscopic binaries. We plan to obtain more observations to determine the radial velocity membership of all the stars in our sample, as well as follow up on radial velocity variables to determine binary orbital solutions.

Identifying drugs that cause acute thrombocytopenia: an analysis using 3 distinct methods

PubMed Central

Reese, Jessica A.; Li, Xiaoning; Hauben, Manfred; Aster, Richard H.; Bougie, Daniel W.; Curtis, Brian R.; George, James N.

2010-01-01

Drug-induced immune thrombocytopenia (DITP) is often suspected in patients with acute thrombocytopenia unexplained by other causes, but documenting that a drug is the cause of thrombocytopenia can be challenging. To provide a resource for diagnosis of DITP and for drug safety surveillance, we analyzed 3 distinct methods for identifying drugs that may cause thrombocytopenia. (1) Published case reports of DITP have described 253 drugs suspected of causing thrombocytopenia; using defined clinical criteria, 87 (34%) were identified with evidence that the drug caused thrombocytopenia. (2) Serum samples from patients with suspected DITP were tested for 202 drugs; drug-dependent, platelet-reactive antibodies were identified for 67 drugs (33%). (3) The Food and Drug Administration's Adverse Event Reporting System database was searched for drugs associated with thrombocytopenia by use of data mining algorithms; 1444 drugs had at least 1 report associated with thrombocytopenia, and 573 (40%) drugs demonstrated a statistically distinctive reporting association with thrombocytopenia. Among 1468 drugs suspected of causing thrombocytopenia, 102 were evaluated by all 3 methods, and 23 of these 102 drugs had evidence for an association with thrombocytopenia by all 3 methods. Multiple methods, each with a distinct perspective, can contribute to the identification of drugs that can cause thrombocytopenia. PMID:20530792

Identifying the ideal profile of French yogurts for different clusters of consumers.

PubMed

Masson, M; Saint-Eve, A; Delarue, J; Blumenthal, D

2016-05-01

Identifying the sensory properties that affect consumer preferences for food products is an important feature of product development. Different methods, such as external preference mapping or partial least squares regression, are used to establish relationships between sensory data and consumer preferences and to identify sensory attributes that drive consumer preferences, by highlighting optimum products. Plain French yogurts were evaluated by a sensory profiling method performed by 12 trained judges. In parallel, 180 consumers were asked to score their overall liking and complete a cognitive restraint questionnaire. After hierarchical cluster analysis on the liking scores, preference mapping using a quadratic regression model was performed. Five clusters of consumers were identified as a function of different preference patterns. Contrary to our expectations, fat levels were not discriminating. For each cluster, the results of preference mapping enabled the identification of optimum products. A comparison of the 5 sensory profiles revealed numerous differences between key sensory attributes. For example, one consumer cluster had a strong preference for products perceived as very thick, grainy, but with a less flowing texture, less sticky, whey presence and color, in contrast to other clusters. In addition, each segment of consumers was characterized according to the results of the cognitive restraint questionnaire. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Severe or life-threatening asthma exacerbation: patient heterogeneity identified by cluster analysis.

PubMed

Sekiya, K; Nakatani, E; Fukutomi, Y; Kaneda, H; Iikura, M; Yoshida, M; Takahashi, K; Tomii, K; Nishikawa, M; Kaneko, N; Sugino, Y; Shinkai, M; Ueda, T; Tanikawa, Y; Shirai, T; Hirabayashi, M; Aoki, T; Kato, T; Iizuka, K; Homma, S; Taniguchi, M; Tanaka, H

2016-08-01

Severe or life-threatening asthma exacerbation is one of the worst outcomes of asthma because of the risk of death. To date, few studies have explored the potential heterogeneity of this condition. To examine the clinical characteristics and heterogeneity of patients with severe or life-threatening asthma exacerbation. This was a multicentre, prospective study of patients with severe or life-threatening asthma exacerbation and pulse oxygen saturation < 90% who were admitted to 17 institutions across Japan. Cluster analysis was performed using variables from patient- and physician-orientated structured questionnaires. Analysis of data from 175 patients with severe or life-threatening asthma exacerbation revealed five distinct clusters. Cluster 1 (n = 27) was younger-onset asthma with severe symptoms at baseline, including limitation of activities, a higher frequency of treatment with oral corticosteroids and short-acting beta-agonists, and a higher frequency of asthma hospitalizations in the past year. Cluster 2 (n = 35) was predominantly composed of elderly females, with the highest frequency of comorbid, chronic hyperplastic rhinosinusitis/nasal polyposis, and a long disease duration. Cluster 3 (n = 40) was allergic asthma without inhaled corticosteroid use at baseline. Patients in this cluster had a higher frequency of atopy, including allergic rhinitis and furred pet hypersensitivity, and a better prognosis during hospitalization compared with the other clusters. Cluster 4 (n = 34) was characterized by elderly males with concomitant chronic obstructive pulmonary disease (COPD). Although cluster 5 (n = 39) had very mild symptoms at baseline according to the patient questionnaires, 41% had previously been hospitalized for asthma. This study demonstrated that significant heterogeneity exists among patients with severe or life-threatening asthma exacerbation. Differences were observed in the severity of asthma symptoms and use of inhaled corticosteroids at baseline

Distinct functional domains within the acidic cluster of tegument protein pp28 required for trafficking and cytoplasmic envelopment of human cytomegalovirus.

PubMed

Seo, Jun-Young; Jeon, Hyejin; Hong, Sookyung; Britt, William J

2016-10-01

Human cytomegalovirus UL99-encoded tegument protein pp28 contains a 16 aa acidic cluster that is required for pp28 trafficking to the assembly compartment (AC) and the virus assembly. However, functional signals within the acidic cluster of pp28 remain undefined. Here, we demonstrated that an acidic cluster rather than specific sorting signals was required for trafficking to the AC. Recombinant viruses with chimeric pp28 proteins expressing non-native acidic clusters exhibited delayed viral growth kinetics and decreased production of infectious virus, indicating that the native acidic cluster of pp28 was essential for wild-type virus assembly. These results suggested that the acidic cluster of pp28 has distinct functional domains required for trafficking and for efficient virus assembly. The first half (aa 44-50) of the acidic cluster was sufficient for pp28 trafficking, whereas the native acidic cluster consisting of aa 51-59 was required for the assembly of wild-type levels of infectious virus.

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.

Suicide in the oldest old: an observational study and cluster analysis.

PubMed

Sinyor, Mark; Tan, Lynnette Pei Lin; Schaffer, Ayal; Gallagher, Damien; Shulman, Kenneth

2016-01-01

The older population are at a high risk for suicide. This study sought to learn more about the characteristics of suicide in the oldest-old and to use a cluster analysis to determine if oldest-old suicide victims assort into clinically meaningful subgroups. Data were collected from a coroner's chart review of suicide victims in Toronto from 1998 to 2011. We compared two age groups (65-79 year olds, n = 335, and 80+ year olds, n = 191) and then conducted a hierarchical agglomerative cluster analysis using Ward's method to identify distinct clusters in the 80+ group. The younger and older age groups differed according to marital status, living circumstances and pattern of stressors. The cluster analysis identified three distinct clusters in the 80+ group. Cluster 1 was the largest (n = 124) and included people who were either married or widowed who had significantly more depression and somewhat more medical health stressors. In contrast, cluster 2 (n = 50) comprised people who were almost all single and living alone with significantly less identified depression and slightly fewer medical health stressors. All members of cluster 3 (n = 17) lived in a retirement residence or nursing home, and this group had the highest rates of depression, dementia, other mental illness and past suicide attempts. This is the first study to use the cluster analysis technique to identify meaningful subgroups among suicide victims in the oldest-old. The results reveal different patterns of suicide in the older population that may be relevant for clinical care. Copyright © 2015 John Wiley & Sons, Ltd.

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.

Symptom clusters and quality of life among patients with advanced heart failure

PubMed Central

Yu, Doris SF; Chan, Helen YL; Leung, Doris YP; Hui, Elsie; Sit, Janet WH

2016-01-01

Objectives To identify symptom clusters among patients with advanced heart failure (HF) and the independent relationships with their quality of life (QoL). Methods This is the secondary data analysis of a cross-sectional study which interviewed 119 patients with advanced HF in the geriatric unit of a regional hospital in Hong Kong. The symptom profile and QoL were assessed by using the Edmonton Symptom Assessment Scale (ESAS) and the McGill QoL Questionnaire. Exploratory factor analysis was used to identify the symptom clusters. Hierarchical regression analysis was used to examine the independent relationships with their QoL, after adjusting the effects of age, gender, and comorbidities. Results The patients were at an advanced age (82.9 ± 6.5 years). Three distinct symptom clusters were identified: they were the distress cluster (including shortness of breath, anxiety, and depression), the decondition cluster (fatigue, drowsiness, nausea, and reduced appetite), and the discomfort cluster (pain, and sense of generalized discomfort). These three symptom clusters accounted for 63.25% of variance of the patients' symptom experience. The small to moderate correlations between these symptom clusters indicated that they were rather independent of one another. After adjusting the age, gender and comorbidities, the distress (β = −0.635, P < 0.001), the decondition (β = −0.148, P = 0.01), and the discomfort (β = −0.258, P < 0.001) symptom clusters independently predicted their QoL. Conclusions This study identified the distinctive symptom clusters among patients with advanced HF. The results shed light on the need to develop palliative care interventions for optimizing the symptom control for this life-limiting disease. PMID:27403150

Analyzing the Role of Community and Individual Factors in Food Insecurity: Identifying Diverse Barriers Across Clustered Community Members.

PubMed

Jablonski, Becca B R; McFadden, Dawn Thilmany; Colpaart, Ashley

2016-10-01

This paper uses the results from a community food security assessment survey of 684 residents and three focus groups in Pueblo County, Colorado to examine the question: what community and individual factors contribute to or alleviate food insecurity, and are these factors consistent throughout a sub-county population. Importantly, we use a technique called cluster analysis to endogenously determine the key factors pertinent to food access and fruit and vegetable consumption. Our results show significant heterogeneity among sub-population clusters in terms of the community and individual factors that would make it easier to get access to fruits and vegetables. We find two distinct clusters of food insecure populations: the first was significantly less likely to identify increased access to fruits and vegetables proximate to where they live or work as a way to improve their household's healthy food consumption despite being significantly less likely to utilize a personal vehicle to get to the store; the second group did not report significant challenges with access, rather with affordability. We conclude that though interventions focused on improving the local food retail environment may be important for some subsamples of the food insecure population, it is unclear that proximity to a store with healthy food will support enhanced food security for all. We recommend that future research recognizes that determinants of food insecurity may vary within county or zip code level regions, and that multiple interventions that target sub-population clusters may elicit better improvements in access to and consumption of fruits and vegetables.

Identification of Clusters of Foot Pain Location in a Community Sample.

PubMed

Gill, Tiffany K; Menz, Hylton B; Landorf, Karl B; Arnold, John B; Taylor, Anne W; Hill, Catherine L

2017-12-01

To identify foot pain clusters according to pain location in a community-based sample of the general population. This study analyzed data from the North West Adelaide Health Study. Data were obtained between 2004 and 2006, using computer-assisted telephone interviewing, clinical assessment, and self-completed questionnaire. The location of foot pain was assessed using a diagram during the clinical assessment. Hierarchical cluster analysis was undertaken to identify foot pain location clusters, which were then compared in relation to demographics, comorbidities, and podiatry services utilization. There were 558 participants with foot pain (mean age 54.4 years, 57.5% female). Five clusters were identified: 1 with predominantly arch and ball pain (26.8%), 1 with rearfoot pain (20.9%), 1 with heel pain (13.3%), and 2 with predominantly forefoot, toe, and nail pain (28.3% and 10.7%). Each cluster was distinct in age, sex, and comorbidity profile. Of the two clusters with predominantly forefoot, toe, and nail pain, one of them had a higher proportion of men and those classified as obese, had diabetes mellitus, and used podiatry services (30%), while the other was comprised of a higher proportion of women who were overweight and reported less use of podiatry services (17.5%). Five clusters of foot pain according to pain location were identified, all with distinct age, sex, and comorbidity profiles. These findings may assist in the identification of individuals at risk for developing foot pain and in the development of targeted preventive strategies and treatments. © 2017, American College of Rheumatology.

Identifying Subgroups of Tinnitus Using Novel Resting State fMRI Biomarkers and Cluster Analysis

DTIC Science & Technology

2016-10-01

AWARD NUMBER: W81XWH-15-2-0032 TITLE: Identifying Subgroups of Tinnitus Using Novel Resting State fMRI Biomarkers and Cluster Analysis PRINCIPAL...4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Identifying Subgroups of Tinnitus Using Novel Resting State fMRI Biomarkers and Cluster Analysis 5b...Public Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT The subject of the project is FY14 PRMRP Topic Area – Tinnitus . The broad

Cardiorespiratory instability in monitored step-down unit patients: using cluster analysis to identify patterns of change

PubMed Central

Clermont, Gilles; Chen, Lujie; Dubrawski, Artur W.; Ren, Dianxu; Hoffman, Leslie A.; Pinsky, Michael R.; Hravnak, Marilyn

2018-01-01

Cardiorespiratory instability (CRI) in monitored step-down unit (SDU) patients has a variety of etiologies, and likely manifests in patterns of vital signs (VS) changes. We explored use of clustering techniques to identify patterns in the initial CRI epoch (CRI1; first exceedances of VS beyond stability thresholds after SDU admission) of unstable patients, and inter-cluster differences in admission characteristics and outcomes. Continuous noninvasive monitoring of heart rate (HR), respiratory rate (RR), and pulse oximetry (SpO2) were sampled at 1/20 Hz. We identified CRI1 in 165 patients, employed hierarchical and k-means clustering, tested several clustering solutions, used 10-fold cross validation to establish the best solution and assessed inter-cluster differences in admission characteristics and outcomes. Three clusters (C) were derived: C1) normal/high HR and RR, normal SpO2 (n = 30); C2) normal HR and RR, low SpO2 (n = 103); and C3) low/normal HR, low RR and normal SpO2 (n = 32). Clusters were significantly different based on age (p < 0.001; older patients in C2), number of comorbidities (p = 0.008; more C2 patients had ≥ 2) and hospital length of stay (p = 0.006; C1 patients stayed longer). There were no between-cluster differences in SDU length of stay, or mortality. Three different clusters of VS presentations for CRI1 were identified. Clusters varied on age, number of comorbidities and hospital length of stay. Future study is needed to determine if there are common physiologic underpinnings of VS clusters which might inform clinical decision-making when CRI first manifests. PMID:28229353

Epidemiological analysis of Salmonella clusters identified by whole genome sequencing, England and Wales 2014.

PubMed

Waldram, Alison; Dolan, Gayle; Ashton, Philip M; Jenkins, Claire; Dallman, Timothy J

2018-05-01

The unprecedented level of bacterial strain discrimination provided by whole genome sequencing (WGS) presents new challenges with respect to the utility and interpretation of the data. Whole genome sequences from 1445 isolates of Salmonella belonging to the most commonly identified serotypes in England and Wales isolated between April and August 2014 were analysed. Single linkage single nucleotide polymorphism thresholds at the 10, 5 and 0 level were explored for evidence of epidemiological links between clustered cases. Analysis of the WGS data organised 566 of the 1445 isolates into 32 clusters of five or more. A statistically significant epidemiological link was identified for 17 clusters. The clusters were associated with foreign travel (n = 8), consumption of Chinese takeaways (n = 4), chicken eaten at home (n = 2), and one each of the following; eating out, contact with another case in the home and contact with reptiles. In the same time frame, one cluster was detected using traditional outbreak detection methods. WGS can be used for the highly specific and highly sensitive detection of biologically related isolates when epidemiological links are obscured. Improvements in the collection of detailed, standardised exposure information would enhance cluster investigations. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Automatic script identification from images using cluster-based templates

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

Hochberg, J.; Kerns, L.; Kelly, P.

We have developed a technique for automatically identifying the script used to generate a document that is stored electronically in bit image form. Our approach differs from previous work in that the distinctions among scripts are discovered by an automatic learning procedure, without any handson analysis. We first develop a set of representative symbols (templates) for each script in our database (Cyrillic, Roman, etc.). We do this by identifying all textual symbols in a set of training documents, scaling each symbol to a fixed size, clustering similar symbols, pruning minor clusters, and finding each cluster`s centroid. To identify a newmore » document`s script, we identify and scale a subset of symbols from the document and compare them to the templates for each script. We choose the script whose templates provide the best match. Our current system distinguishes among the Armenian, Burmese, Chinese, Cyrillic, Ethiopic, Greek, Hebrew, Japanese, Korean, Roman, and Thai scripts with over 90% accuracy.« less

Distinct Host Tropism Protein Signatures to Identify Possible Zoonotic Influenza A Viruses.

PubMed

Eng, Christine L P; Tong, Joo Chuan; Tan, Tin Wee

2016-01-01

Zoonotic influenza A viruses constantly pose a health threat to humans as novel strains occasionally emerge from the avian population to cause human infections. Many past epidemic as well as pandemic strains have originated from avian species. While most viruses are restricted to their primary hosts, zoonotic strains can sometimes arise from mutations or reassortment, leading them to acquire the capability to escape host species barrier and successfully infect a new host. Phylogenetic analyses and genetic markers are useful in tracing the origins of zoonotic infections, but there are still no effective means to identify high risk strains prior to an outbreak. Here we show that distinct host tropism protein signatures can be used to identify possible zoonotic strains in avian species which have the potential to cause human infections. We have discovered that influenza A viruses can now be classified into avian, human, or zoonotic strains based on their host tropism protein signatures. Analysis of all influenza A viruses with complete proteome using the host tropism prediction system, based on machine learning classifications of avian and human viral proteins has uncovered distinct signatures of zoonotic strains as mosaics of avian and human viral proteins. This is in contrast with typical avian or human strains where they show mostly avian or human viral proteins in their signatures respectively. Moreover, we have found that zoonotic strains from the same influenza outbreaks carry similar host tropism protein signatures characteristic of a common ancestry. Our results demonstrate that the distinct host tropism protein signature in zoonotic strains may prove useful in influenza surveillance to rapidly identify potential high risk strains circulating in avian species, which may grant us the foresight in anticipating an impending influenza outbreak.

Integrated genetic and epigenetic analysis identifies three different subclasses of colon cancer

PubMed Central

Shen, Lanlan; Toyota, Minoru; Kondo, Yutaka; Lin, E; Zhang, Li; Guo, Yi; Hernandez, Natalie Supunpong; Chen, Xinli; Ahmed, Saira; Konishi, Kazuo; Hamilton, Stanley R.; Issa, Jean-Pierre J.

2007-01-01

Colon cancer has been viewed as the result of progressive accumulation of genetic and epigenetic abnormalities. However, this view does not fully reflect the molecular heterogeneity of the disease. We have analyzed both genetic (mutations of BRAF, KRAS, and p53 and microsatellite instability) and epigenetic alterations (DNA methylation of 27 CpG island promoter regions) in 97 primary colorectal cancer patients. Two clustering analyses on the basis of either epigenetic profiling or a combination of genetic and epigenetic profiling were performed to identify subclasses with distinct molecular signatures. Unsupervised hierarchical clustering of the DNA methylation data identified three distinct groups of colon cancers named CpG island methylator phenotype (CIMP) 1, CIMP2, and CIMP negative. Genetically, these three groups correspond to very distinct profiles. CIMP1 are characterized by MSI (80%) and BRAF mutations (53%) and rare KRAS and p53 mutations (16% and 11%, respectively). CIMP2 is associated with 92% KRAS mutations and rare MSI, BRAF, or p53 mutations (0, 4, and 31% respectively). CIMP-negative cases have a high rate of p53 mutations (71%) and lower rates of MSI (12%) or mutations of BRAF (2%) or KRAS (33%). Clustering based on both genetic and epigenetic parameters also identifies three distinct (and homogeneous) groups that largely overlap with the previous classification. The three groups are independent of age, gender, or stage, but CIMP1 and 2 are more common in proximal tumors. Together, our integrated genetic and epigenetic analysis reveals that colon cancers correspond to three molecularly distinct subclasses of disease. PMID:18003927

Cluster Analysis in Sociometric Research: A Pattern-Oriented Approach to Identifying Temporally Stable Peer Status Groups of Girls

ERIC Educational Resources Information Center

Zettergren, Peter

2007-01-01

A modern clustering technique was applied to age-10 and age-13 sociometric data with the purpose of identifying longitudinally stable peer status clusters. The study included 445 girls from a Swedish longitudinal study. The identified temporally stable clusters of rejected, popular, and average girls were essentially larger than corresponding…

What is your patient’s cognitive profile? Three distinct subgroups of cognitive function in persons with heart failure

PubMed Central

Hawkins, Misty A.W.; Schaefer, Julie T.; Gunstad, John; Dolansky, Mary A.; Redle, Joseph D.; Josephson, Richard; Moore, Shirley M.; Hughes, Joel W.

2014-01-01

Purpose To determine whether patients with heart failure (HF) have distinct profiles of cognitive impairment. Background Cognitive impairment is common in HF. Recent work found three cognitive profiles in HF patients— (1) intact, (2) impaired, and (3) memory-impaired. We examined the reproducibility of these profiles and clarified mechanisms. Methods HF patients (68.6±9.7years; N=329) completed neuropsychological testing. Composite scores were created for cognitive domains and used to identify clusters via agglomerative-hierarchical cluster analysis. Results A 3-cluster solution emerged. Cluster 1 (n=109) had intact cognition. Cluster 2 (n=123) was impaired across all domains. Cluster 3 (n=97) had impaired memory only. Clusters differed in age, race, education, SES, IQ, BMI, and diabetes (ps ≤.026) but not in mood, anxiety, cardiovascular, or pulmonary disease (ps≥.118). Conclusions We replicated three distinct patterns of cognitive function in persons with HF. These profiles may help providers offer tailored care to patients with different cognitive and clinical needs. PMID:25510559

Whole-Genome and Epigenomic Landscapes of Etiologically Distinct Subtypes of Cholangiocarcinoma.

PubMed

Jusakul, Apinya; Cutcutache, Ioana; Yong, Chern Han; Lim, Jing Quan; Huang, Mi Ni; Padmanabhan, Nisha; Nellore, Vishwa; Kongpetch, Sarinya; Ng, Alvin Wei Tian; Ng, Ley Moy; Choo, Su Pin; Myint, Swe Swe; Thanan, Raynoo; Nagarajan, Sanjanaa; Lim, Weng Khong; Ng, Cedric Chuan Young; Boot, Arnoud; Liu, Mo; Ong, Choon Kiat; Rajasegaran, Vikneswari; Lie, Stefanus; Lim, Alvin Soon Tiong; Lim, Tse Hui; Tan, Jing; Loh, Jia Liang; McPherson, John R; Khuntikeo, Narong; Bhudhisawasdi, Vajaraphongsa; Yongvanit, Puangrat; Wongkham, Sopit; Totoki, Yasushi; Nakamura, Hiromi; Arai, Yasuhito; Yamasaki, Satoshi; Chow, Pierce Kah-Hoe; Chung, Alexander Yaw Fui; Ooi, London Lucien Peng Jin; Lim, Kiat Hon; Dima, Simona; Duda, Dan G; Popescu, Irinel; Broet, Philippe; Hsieh, Sen-Yung; Yu, Ming-Chin; Scarpa, Aldo; Lai, Jiaming; Luo, Di-Xian; Carvalho, André Lopes; Vettore, André Luiz; Rhee, Hyungjin; Park, Young Nyun; Alexandrov, Ludmil B; Gordân, Raluca; Rozen, Steven G; Shibata, Tatsuhiro; Pairojkul, Chawalit; Teh, Bin Tean; Tan, Patrick

2017-10-01

Cholangiocarcinoma (CCA) is a hepatobiliary malignancy exhibiting high incidence in countries with endemic liver-fluke infection. We analyzed 489 CCAs from 10 countries, combining whole-genome (71 cases), targeted/exome, copy-number, gene expression, and DNA methylation information. Integrative clustering defined 4 CCA clusters-fluke-positive CCAs (clusters 1/2) are enriched in ERBB2 amplifications and TP53 mutations; conversely, fluke-negative CCAs (clusters 3/4) exhibit high copy-number alterations and PD-1 / PD-L2 expression, or epigenetic mutations ( IDH1/2, BAP1 ) and FGFR / PRKA -related gene rearrangements. Whole-genome analysis highlighted FGFR2 3' untranslated region deletion as a mechanism of FGFR2 upregulation. Integration of noncoding promoter mutations with protein-DNA binding profiles demonstrates pervasive modulation of H3K27me3-associated sites in CCA. Clusters 1 and 4 exhibit distinct DNA hypermethylation patterns targeting either CpG islands or shores-mutation signature and subclonality analysis suggests that these reflect different mutational pathways. Our results exemplify how genetics, epigenetics, and environmental carcinogens can interplay across different geographies to generate distinct molecular subtypes of cancer. Significance: Integrated whole-genome and epigenomic analysis of CCA on an international scale identifies new CCA driver genes, noncoding promoter mutations, and structural variants. CCA molecular landscapes differ radically by etiology, underscoring how distinct cancer subtypes in the same organ may arise through different extrinsic and intrinsic carcinogenic processes. Cancer Discov; 7(10); 1116-35. ©2017 AACR. This article is highlighted in the In This Issue feature, p. 1047 . ©2017 American Association for Cancer Research.

[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

Distinct sources of injections in the polar cusp observed by Cluster

NASA Astrophysics Data System (ADS)

Escoubet, C. Philippe; Reme, Henri; Dunlop, Malcolm; Daly, Patrick; Laakso, Harri; Berchem, Jean; Richard, Robert; Taylor, Matthew; Trattner, Karlheinz; Grison, Benjamin; Dandouras, Iannis; Fazakerley, Andrew; Pitout, Frederic; Masson, Arnaud

The main process that injects solar wind plasma into the polar cusp is now generally accepted to be magnetic reconnection. Depending on the IMF direction, this process takes place equatorward (for IMF southward), poleward (for IMF northward) or on the dusk or dawn sides (for IMF azimuthal) of the cusp. We report a Cluster crossing on 5 January 2002 near the exterior cusp on the southern dusk side. The IMF was mainly azimuthal (IMF-By around -5 nT), the solar wind speed lower than usual around 280 km/s and the density around 5 cm-3. The four Cluster spacecraft had an elongated configuration near the magnetopause. C4 was the first spacecraft to enter the cusp around 19:52:04 UT, followed by C2 at 19:52:35 UT, C1 at 19:54:24 UT and C3 at 20:13:15 UT. C4 and C1 observed two ion energy dispersions at 20:10 UT and 20:40 UT and C3 at 20:35 UT and 21:15 UT. Using the time of flight technique on the upgoing and downgoing ions in the dispersions, we obtain an altitude of the sources of these ions between 14 and 20 RE. Using Tsyganenko model, these sources are located on the dusk flank, past the terminator. The first injection by C3 is seen at approximately the same time as the 2nd injection on C1 but their sources at the magnetopause were separated by more than 10 RE. This would imply that two distinct sources were active at the same time on the dusk flank of the magnetosphere.

Cluster redshifts in five suspected superclusters

NASA Technical Reports Server (NTRS)

Ciardullo, R.; Ford, H.; Harms, R.

1985-01-01

Redshift surveys for rich superclusters were carried out in five regions of the sky containing surface-density enhancements of Abell clusters. While several superclusters are identified, projection effects dominate each field, and no system contains more than five rich clusters. Two systems are found to be especially interesting. The first, field 0136 10, is shown to contain a superposition of at least four distinct superclusters, with the richest system possessing a small velocity dispersion. The second system, 2206 - 22, though a region of exceedingly high Abell cluster surface density, appears to be a remarkable superposition of 23 rich clusters almost uniformly distributed in redshift space between 0.08 and 0.24. The new redshifts significantly increase the three-dimensional information available for the distance class 5 and 6 Abell clusters and allow the spatial correlation function around rich superclusters to be estimated.

Construct validation of the hybrid model of posttraumatic stress disorder: Distinctiveness of the new symptom clusters.

PubMed

Silverstein, Madison W; Dieujuste, Nathalie; Kramer, Lindsay B; Lee, Daniel J; Weathers, Frank W

2018-03-01

Despite the factor analytic support for the seven-factor hybrid model (Armour et al., 2015) of posttraumatic stress disorder (PTSD), little research has examined the degree to which newly established symptom clusters (i.e., negative affect, anhedonia, dysphoric arousal, anxious arousal, externalizing behavior) functionally and meaningfully differ in their associations with other clinical phenomena. The aim of the current study was to examine the degree to which newly established PTSD symptom clusters differentially relate to co-occurring psychopathology and related clinical phenomena through Wald testing using latent variable modeling. Participants were 535 trauma-exposed undergraduates who completed the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5; Weathers et al., 2013) and Personality Assessment Inventory (PAI; Morey, 1991). As expected and in line with results from previous studies, significant heterogeneity emerged for dysphoric arousal, anxious arousal, and externalizing behavior. However, there was less evidence for the distinctiveness of negative affect and anhedonia. Results indicate that only some of the newly established symptom clusters significantly differ in their associations with related clinical phenomena and that the hybrid model might not provide a meaningful framework for understanding which PTSD symptoms relate to associated features. Limitations include a non-clinical sample and reliance on retrospective self-report assessment measures. Copyright © 2017 Elsevier Ltd. All rights reserved.

Progeny Clustering: A Method to Identify Biological Phenotypes

PubMed Central

Hu, Chenyue W.; Kornblau, Steven M.; Slater, John H.; Qutub, Amina A.

2015-01-01

Estimating the optimal number of clusters is a major challenge in applying cluster analysis to any type of dataset, especially to biomedical datasets, which are high-dimensional and complex. Here, we introduce an improved method, Progeny Clustering, which is stability-based and exceptionally efficient in computing, to find the ideal number of clusters. The algorithm employs a novel Progeny Sampling method to reconstruct cluster identity, a co-occurrence probability matrix to assess the clustering stability, and a set of reference datasets to overcome inherent biases in the algorithm and data space. Our method was shown successful and robust when applied to two synthetic datasets (datasets of two-dimensions and ten-dimensions containing eight dimensions of pure noise), two standard biological datasets (the Iris dataset and Rat CNS dataset) and two biological datasets (a cell phenotype dataset and an acute myeloid leukemia (AML) reverse phase protein array (RPPA) dataset). Progeny Clustering outperformed some popular clustering evaluation methods in the ten-dimensional synthetic dataset as well as in the cell phenotype dataset, and it was the only method that successfully discovered clinically meaningful patient groupings in the AML RPPA dataset. PMID:26267476

A Cluster Analytic Study of Clinical Orientations among Chemical Dependency Counselors.

ERIC Educational Resources Information Center

Thombs, Dennis L.; Osborn, Cynthia J.

2001-01-01

Three distinct clinical orientations were identified in a sample of chemical dependency counselors (N=406). Based on cluster analysis, the largest group, identified and labeled as "uniform counselors," endorsed a simple, moral-disease model with little interest in psychosocial interventions. (Contains 50 references and 4 tables.) (GCP)

Tobacco, Marijuana, and Alcohol Use in University Students: A Cluster Analysis

PubMed Central

Primack, Brian A.; Kim, Kevin H.; Shensa, Ariel; Sidani, Jaime E.; Barnett, Tracey E.; Switzer, Galen E.

2012-01-01

Objective Segmentation of populations may facilitate development of targeted substance abuse prevention programs. We aimed to partition a national sample of university students according to profiles based on substance use. Participants We used 2008–2009 data from the National College Health Assessment from the American College Health Association. Our sample consisted of 111,245 individuals from 158 institutions. Method We partitioned the sample using cluster analysis according to current substance use behaviors. We examined the association of cluster membership with individual and institutional characteristics. Results Cluster analysis yielded six distinct clusters. Three individual factors—gender, year in school, and fraternity/sorority membership—were the most strongly associated with cluster membership. Conclusions In a large sample of university students, we were able to identify six distinct patterns of substance abuse. It may be valuable to target specific populations of college-aged substance users based on individual factors. However, comprehensive intervention will require a multifaceted approach. PMID:22686360

A DNA-Encapsulated and Fluorescent Ag 10 6+ Cluster with a Distinct Metal-Like Core

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

Petty, Jeffrey T.; Ganguly, Mainak; Rankine, Ian J.

Silver cluster–DNA complexes are optical chromophores, and pairs of these conjugates can be toggled from fluorescently dim to bright states using DNA hybridization. This paper highlights spectral and structural differences for a specific cluster pair. We have previously characterized a cluster with low emission and violet absorption that forms a compact structure with single-stranded oligonucleotides. We now consider its counterpart with blue absorption and strong green emission. This cluster develops with a single-stranded/duplex DNA construct and is favored by low silver concentrations with ≲8 Ag+:DNA, an oxygen atmosphere, and neutral pH. The resulting cluster displays key signatures of a molecularmore » metal with well-defined absorption/emission bands at 490/550 nm, and with a fluorescence quantum yield of 15% and lifetime of 2.4 ns. The molecular cluster conjugates with the larger DNA host because it chromatographically elutes with the DNA and it exhibits circular dichroism. The silver cluster is identified as Ag106+ using two modes of mass spectrometry and elemental analysis. Our key finding is that it adopts a low-dimensional shape, as determined from a Ag K-edge extended X-ray absorption fine structure analysis. The Ag0 in this oxidized cluster segregates from the Ag+ via a sparse number of metal-like bonds and a denser network of silver–DNA bonds. This structure contrasts with the compact, octahedral-like shape of the violet counterpart to the blue cluster, which is also a Ag106+ species. We consider that the blue- and violet-absorbing clusters may be isomers with shapes that are controlled by the secondary structures of their DNA templates.« less

Anticancer Properties of Distinct Antimalarial Drug Classes

PubMed Central

Hooft van Huijsduijnen, Rob; Guy, R. Kiplin; Chibale, Kelly; Haynes, Richard K.; Peitz, Ingmar; Kelter, Gerhard; Phillips, Margaret A.; Vennerstrom, Jonathan L.; Yuthavong, Yongyuth; Wells, Timothy N. C.

2013-01-01

We have tested five distinct classes of established and experimental antimalarial drugs for their anticancer potential, using a panel of 91 human cancer lines. Three classes of drugs: artemisinins, synthetic peroxides and DHFR (dihydrofolate reductase) inhibitors effected potent inhibition of proliferation with IC50s in the nM- low µM range, whereas a DHODH (dihydroorotate dehydrogenase) and a putative kinase inhibitor displayed no activity. Furthermore, significant synergies were identified with erlotinib, imatinib, cisplatin, dasatinib and vincristine. Cluster analysis of the antimalarials based on their differential inhibition of the various cancer lines clearly segregated the synthetic peroxides OZ277 and OZ439 from the artemisinin cluster that included artesunate, dihydroartemisinin and artemisone, and from the DHFR inhibitors pyrimethamine and P218 (a parasite DHFR inhibitor), emphasizing their shared mode of action. In order to further understand the basis of the selectivity of these compounds against different cancers, microarray-based gene expression data for 85 of the used cell lines were generated. For each compound, distinct sets of genes were identified whose expression significantly correlated with compound sensitivity. Several of the antimalarials tested in this study have well-established and excellent safety profiles with a plasma exposure, when conservatively used in malaria, that is well above the IC50s that we identified in this study. Given their unique mode of action and potential for unique synergies with established anticancer drugs, our results provide a strong basis to further explore the potential application of these compounds in cancer in pre-clinical or and clinical settings. PMID:24391728

Linking Strengths: Identifying and Exploring Protective Factor Clusters in Academically Resilient Low-Socioeconomic Urban Students of Color

ERIC Educational Resources Information Center

Morales, Erik E.

2010-01-01

Based on data from qualitative interviews with 50 high-achieving low-socioeconomic students of color, two "clusters" of important and symbiotic protective factors are identified and explored. Each cluster consists of a series of interrelated protective factors identified by the participants as crucial to their statistically exceptional academic…

Identifying nearby field T dwarfs in the UKIDSS Galactic Clusters Survey

NASA Astrophysics Data System (ADS)

Lodieu, N.; Burningham, B.; Hambly, N. C.; Pinfield, D. J.

2009-07-01

We present the discovery of two new late-T dwarfs identified in the UKIRT Infrared Deep Sky Survey (UKIDSS) Galactic Clusters Survey (GCS) Data Release 2 (DR2). These T dwarfs are nearby old T dwarfs along the line of sight to star-forming regions and open clusters targeted by the UKIDSS GCS. They are found towards the αPer cluster and Orion complex, respectively, from a search in 54deg2 surveyed in five filters. Photometric candidates were picked up in two-colour diagrams, in a very similar manner to candidates extracted from the UKIDSS Large Area Survey (LAS) but taking advantage of the Z filter employed by the GCS. Both candidates exhibit near-infrared J-band spectra with strong methane and water absorption bands characteristic of late-T dwarfs. We derive spectral types of T6.5 +/- 0.5 and T7 +/- 1 and estimate photometric distances less than 50 pc for UGCS J030013.86+490142.5 and UGCS J053022.52-052447.4, respectively. The space density of T dwarfs found in the GCS seems consistent with discoveries in the larger areal coverage of the UKIDSS LAS, indicating one T dwarf in 6-11deg2. The final area surveyed by the GCS, 1000deg2 in five passbands, will allow expansion of the LAS search area by 25 per cent, increase the probability of finding ultracool brown dwarfs, and provide optimal estimates of contamination by old field brown dwarfs in deep surveys to identify such objects in open clusters and star-forming regions. Based on observations made with the United Kingdom Infrared Telescope, operated by the Joint Astronomy Centre on behalf of the U.K. Science Technology and Facility Council. E-mail: nlodieu@iac.es

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.

TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.

PubMed

Jung, Inuk; Jo, Kyuri; Kang, Hyejin; Ahn, Hongryul; Yu, Youngjae; Kim, Sun

2017-12-01

Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.e. gene-time-condition. Computational complexity for analyzing such data is very high, compared to the already difficult NP-hard two dimensional biclustering algorithms. Because of this challenge, traditional time series clustering algorithms are designed to capture co-expressed genes with similar expression pattern in two sample conditions. We present a triclustering algorithm, TimesVector, specifically designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. TimesVector identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters. Using four sets of time series gene expression data, generated by both microarray and high throughput sequencing platforms, we demonstrated that TimesVector successfully detected biologically meaningful clusters of high quality. TimesVector improved the clustering quality compared to existing triclustering tools and only TimesVector detected clusters with differential expression patterns across conditions successfully. The TimesVector software is available at http://biohealth.snu.ac.kr/software/TimesVector/. sunkim.bioinfo@snu.ac.kr. Supplementary data are available at

Profiling of ARDS pulmonary edema fluid identifies a metabolically distinct subset

PubMed Central

Contrepois, Kévin; Wu, Manhong; Zheng, Ming; Peltz, Gary; Ware, Lorraine B.; Matthay, Michael A.

2017-01-01

There is considerable biological and physiological heterogeneity among patients who meet standard clinical criteria for acute respiratory distress syndrome (ARDS). In this study, we tested the hypothesis that there exists a subgroup of ARDS patients who exhibit a metabolically distinct profile. We examined undiluted pulmonary edema fluid obtained at the time of endotracheal intubation from 16 clinically phenotyped ARDS patients and 13 control patients with hydrostatic pulmonary edema. Nontargeted metabolic profiling was carried out on the undiluted edema fluid. Univariate and multivariate statistical analyses including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were conducted to find discriminant metabolites. Seven-hundred and sixty unique metabolites were identified in the pulmonary edema fluid of these 29 patients. We found that a subset of ARDS patients (6/16, 38%) presented a distinct metabolic profile with the overrepresentation of 235 metabolites compared with edema fluid from the other 10 ARDS patients, whose edema fluid metabolic profile was indistinguishable from those of the 13 control patients with hydrostatic edema. This “high metabolite” endotype was characterized by higher concentrations of metabolites belonging to all of the main metabolic classes including lipids, amino acids, and carbohydrates. This distinct group with high metabolite levels in the edema fluid was also associated with a higher mortality rate. Thus metabolic profiling of the edema fluid of ARDS patients supports the hypothesis that there is considerable biological heterogeneity among ARDS patients who meet standard clinical and physiological criteria for ARDS. PMID:28258106

Model-based recursive partitioning to identify risk clusters for metabolic syndrome and its components: findings from the International Mobility in Aging Study

PubMed Central

Pirkle, Catherine M; Wu, Yan Yan; Zunzunegui, Maria-Victoria; Gómez, José Fernando

2018-01-01

Objective Conceptual models underpinning much epidemiological research on ageing acknowledge that environmental, social and biological systems interact to influence health outcomes. Recursive partitioning is a data-driven approach that allows for concurrent exploration of distinct mixtures, or clusters, of individuals that have a particular outcome. Our aim is to use recursive partitioning to examine risk clusters for metabolic syndrome (MetS) and its components, in order to identify vulnerable populations. Study design Cross-sectional analysis of baseline data from a prospective longitudinal cohort called the International Mobility in Aging Study (IMIAS). Setting IMIAS includes sites from three middle-income countries—Tirana (Albania), Natal (Brazil) and Manizales (Colombia)—and two from Canada—Kingston (Ontario) and Saint-Hyacinthe (Quebec). Participants Community-dwelling male and female adults, aged 64–75 years (n=2002). Primary and secondary outcome measures We apply recursive partitioning to investigate social and behavioural risk factors for MetS and its components. Model-based recursive partitioning (MOB) was used to cluster participants into age-adjusted risk groups based on variabilities in: study site, sex, education, living arrangements, childhood adversities, adult occupation, current employment status, income, perceived income sufficiency, smoking status and weekly minutes of physical activity. Results 43% of participants had MetS. Using MOB, the primary partitioning variable was participant sex. Among women from middle-incomes sites, the predicted proportion with MetS ranged from 58% to 68%. Canadian women with limited physical activity had elevated predicted proportions of MetS (49%, 95% CI 39% to 58%). Among men, MetS ranged from 26% to 41% depending on childhood social adversity and education. Clustering for MetS components differed from the syndrome and across components. Study site was a primary partitioning variable for all components

Cluster analysis identifies three urodynamic patterns in patients with orthotopic neobladder reconstruction.

PubMed

Kim, Kwang Hyun; Yoon, Hyun Suk; Song, Wan; Choo, Hee Jung; Yoon, Hana; Chung, Woo Sik; Sim, Bong Suk; Lee, Dong Hyeon

2017-01-01

To classify patients with orthotopic neobladder based on urodynamic parameters using cluster analysis and to characterize the voiding function of each group. From January 2012 to November 2015, 142 patients with bladder cancer underwent radical cystectomy and Studer neobladder reconstruction at our institute. Of the 142 patients, 103 with complete urodynamic data and information on urinary functional outcomes were included in this study. K-means clustering was performed with urodynamic parameters which included maximal cystometric capacity, residual volume, maximal flow rate, compliance, and detrusor pressure at maximum flow rate. Three groups emerged by cluster analysis. Urodynamic parameters and urinary function outcomes were compared between three groups. Group 1 (n = 44) had ideal urodynamic parameters with a mean maximal bladder capacity of 513.3 ml and mean residual urine volume of 33.1 ml. Group 2 (n = 42) was characterized by small bladder capacity with low compliance. Patients in group 2 had higher rates of daytime incontinence and nighttime incontinence than patients in group 1. Group 3 (n = 17) was characterized by large residual urine volume with high compliance. When we examined gender differences in urodynamics and functional outcomes, residual urine volume and the rate of daytime incontinence were only marginally significant. However, females were significantly more likely to belong to group 2 or 3 (P = 0.003). In multivariate analysis to identify factors associated with group 1 which has the most ideal urodynamic pattern, age (OR 0.95, P = 0.017) and male gender (OR 7.57, P = 0.003) were identified as significant factors. While patients with ileal neobladder present with various voiding symptoms, three urodynamic patterns were identified by cluster analysis. Approximately half of patients had ideal urodynamic parameters. The other two groups were characterized by large residual urine and small capacity bladder with low compliance. Young age and male

NASA Telescopes Help Identify Most Distant Galaxy Cluster

NASA Astrophysics Data System (ADS)

2011-01-01

WASHINGTON -- Astronomers have uncovered a burgeoning galactic metropolis, the most distant known in the early universe. This ancient collection of galaxies presumably grew into a modern galaxy cluster similar to the massive ones seen today. The developing cluster, named COSMOS-AzTEC3, was discovered and characterized by multi-wavelength telescopes, including NASA's Spitzer, Chandra and Hubble space telescopes, and the ground-based W.M. Keck Observatory and Japan's Subaru Telescope. "This exciting discovery showcases the exceptional science made possible through collaboration among NASA projects and our international partners," said Jon Morse, NASA's Astrophysics Division director at NASA Headquarters in Washington. Scientists refer to this growing lump of galaxies as a proto-cluster. COSMOS-AzTEC3 is the most distant massive proto-cluster known, and also one of the youngest, because it is being seen when the universe itself was young. The cluster is roughly 12.6 billion light-years away from Earth. Our universe is estimated to be 13.7 billion years old. Previously, more mature versions of these clusters had been spotted at 10 billion light-years away. The astronomers also found that this cluster is buzzing with extreme bursts of star formation and one enormous feeding black hole. "We think the starbursts and black holes are the seeds of the cluster," said Peter Capak of NASA's Spitzer Science Center at the California Institute of Technology in Pasadena. "These seeds will eventually grow into a giant, central galaxy that will dominate the cluster -- a trait found in modern-day galaxy clusters." Capak is first author of a paper appearing in the Jan. 13 issue of the journal Nature. Most galaxies in our universe are bound together into clusters that dot the cosmic landscape like urban sprawls, usually centered around one old, monstrous galaxy containing a massive black hole. Astronomers thought that primitive versions of these clusters, still forming and clumping

Identifying Few-Molecule Water Clusters with High Precision on Au(111) Surface.

PubMed

Dong, Anning; Yan, Lei; Sun, Lihuan; Yan, Shichao; Shan, Xinyan; Guo, Yang; Meng, Sheng; Lu, Xinghua

2018-06-01

Revealing the nature of a hydrogen-bond network in water structures is one of the imperative objectives of science. With the use of a low-temperature scanning tunneling microscope, water clusters on a Au(111) surface were directly imaged with molecular resolution by a functionalized tip. The internal structures of the water clusters as well as the geometry variations with the increase of size were identified. In contrast to a buckled water hexamer predicted by previous theoretical calculations, our results present deterministic evidence for a flat configuration of water hexamers on Au(111), corroborated by density functional theory calculations with properly implemented van der Waals corrections. The consistency between the experimental observations and improved theoretical calculations not only renders the internal structures of absorbed water clusters unambiguously, but also directly manifests the crucial role of van der Waals interactions in constructing water-solid interfaces.

Single Molecule Cluster Analysis Identifies Signature Dynamic Conformations along the Splicing Pathway

PubMed Central

Blanco, Mario R.; Martin, Joshua S.; Kahlscheuer, Matthew L.; Krishnan, Ramya; Abelson, John; Laederach, Alain; Walter, Nils G.

2016-01-01

The spliceosome is the dynamic RNA-protein machine responsible for faithfully splicing introns from precursor messenger RNAs (pre-mRNAs). Many of the dynamic processes required for the proper assembly, catalytic activation, and disassembly of the spliceosome as it acts on its pre-mRNA substrate remain poorly understood, a challenge that persists for many biomolecular machines. Here, we developed a fluorescence-based Single Molecule Cluster Analysis (SiMCAn) tool to dissect the manifold conformational dynamics of a pre-mRNA through the splicing cycle. By clustering common dynamic behaviors derived from selectively blocked splicing reactions, SiMCAn was able to identify signature conformations and dynamic behaviors of multiple ATP-dependent intermediates. In addition, it identified a conformation adopted late in splicing by a 3′ splice site mutant, invoking a mechanism for substrate proofreading. SiMCAn presents a novel framework for interpreting complex single molecule behaviors that should prove widely useful for the comprehensive analysis of a plethora of dynamic cellular machines. PMID:26414013

Specialized 'dauciform' roots of Cyperaceae are structurally distinct, but functionally analogous with 'cluster' roots.

PubMed

Shane, Michael W; Cawthray, Gregory R; Cramer, Michael D; Kuo, John; Lambers, Hans

2006-10-01

When grown in nutrient solutions of extremely low [P] (cluster (proteoid) roots developed by some dicotyledonous species, but without evidence to substantiate this claim. To elucidate the ecophysiological role of dauciform roots, we assessed carboxylate exudation, internal carboxylate and P concentrations and O(2) uptake rates during dauciform root development. We showed that O(2) consumption was fastest [9 nmol O(2) g(-1) fresh mass (FM) s(-1)] and root [P] greatest (0.4 mg P g(-1) FM) when dauciform roots were young and rapidly developing. Citrate was the most abundant carboxylate in root tissues at all developmental stages, and was most concentrated (22.2 micromol citrate g(-1) FM) in young dauciform roots, decreasing by more than half in mature dauciform roots. Peak citrate-exudation rates (1.7 nmol citrate g(-1) FM s(-1)) occurred from mature dauciform roots, and were approximately an order of magnitude faster than those from roots of species without root clusters, and similar to those of mature proteoid (cluster) roots of Proteaceae. Both developing and mature dauciform roots had the capacity to acidify (but not alkalinize) the rhizosphere. Anatomical studies showed that epidermal cells in dauciform roots were greatly elongated in the transverse plane; epidermal cells of parent roots were unmodified. Although structurally distinct, the physiology of dauciform roots in sedges appears to be analogous to that of proteoid roots of Proteaceae and Fabaceae, and hence, dauciform roots would facilitate access to sorbed P and micronutrients from soils of low fertility.

Automated classification of mouse pup isolation syllables: from cluster analysis to an Excel-based "mouse pup syllable classification calculator".

PubMed

Grimsley, Jasmine M S; Gadziola, Marie A; Wenstrup, Jeffrey J

2012-01-01

Mouse pups vocalize at high rates when they are cold or isolated from the nest. The proportions of each syllable type produced carry information about disease state and are being used as behavioral markers for the internal state of animals. Manual classifications of these vocalizations identified 10 syllable types based on their spectro-temporal features. However, manual classification of mouse syllables is time consuming and vulnerable to experimenter bias. This study uses an automated cluster analysis to identify acoustically distinct syllable types produced by CBA/CaJ mouse pups, and then compares the results to prior manual classification methods. The cluster analysis identified two syllable types, based on their frequency bands, that have continuous frequency-time structure, and two syllable types featuring abrupt frequency transitions. Although cluster analysis computed fewer syllable types than manual classification, the clusters represented well the probability distributions of the acoustic features within syllables. These probability distributions indicate that some of the manually classified syllable types are not statistically distinct. The characteristics of the four classified clusters were used to generate a Microsoft Excel-based mouse syllable classifier that rapidly categorizes syllables, with over a 90% match, into the syllable types determined by cluster analysis.

Cognitive Clusters in Specific Learning Disorder.

PubMed

Poletti, Michele; Carretta, Elisa; Bonvicini, Laura; Giorgi-Rossi, Paolo

The heterogeneity among children with learning disabilities still represents a barrier and a challenge in their conceptualization. Although a dimensional approach has been gaining support, the categorical approach is still the most adopted, as in the recent fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. The introduction of the single overarching diagnostic category of specific learning disorder (SLD) could underemphasize interindividual clinical differences regarding intracategory cognitive functioning and learning proficiency, according to current models of multiple cognitive deficits at the basis of neurodevelopmental disorders. The characterization of specific cognitive profiles associated with an already manifest SLD could help identify possible early cognitive markers of SLD risk and distinct trajectories of atypical cognitive development leading to SLD. In this perspective, we applied a cluster analysis to identify groups of children with a Diagnostic and Statistical Manual-based diagnosis of SLD with similar cognitive profiles and to describe the association between clusters and SLD subtypes. A sample of 205 children with a diagnosis of SLD were enrolled. Cluster analyses (agglomerative hierarchical and nonhierarchical iterative clustering technique) were used successively on 10 core subtests of the Wechsler Intelligence Scale for Children-Fourth Edition. The 4-cluster solution was adopted, and external validation found differences in terms of SLD subtype frequencies and learning proficiency among clusters. Clinical implications of these findings are discussed, tracing directions for further studies.

Distinct subtypes of behavioral-variant frontotemporal dementia based on patterns of network degeneration

PubMed Central

Ranasinghe, Kamalini G; Rankin, Katherine P; Pressman, Peter S; Perry, David C; Lobach, Iryna V; Seeley, William W; Coppola, Giovanni; Karydas, Anna M; Grinberg, Lea T; Shany-Ur, Tal; Lee, Suzee E; Rabinovici, Gil D; Rosen, Howard J; Gorno-Tempini, Maria Luisa; Boxer, Adam L; Miller, Zachary A; Chiong, Winston; DeMay, Mary; Kramer, Joel H; Possin, Katherine L; Sturm, Virginia E; Bettcher, Brianne M; Neylan, Michael; Zackey, Diana D; Nguyen, Lauren A; Ketelle, Robin; Block, Nikolas; Wu, Teresa Q; Dallich, Alison; Russek, Natanya; Caplan, Alyssa; Geschwind, Daniel H; Vossel, Keith A; Miller, Bruce L

2016-01-01

Importance Clearer delineation of the phenotypic heterogeneity within behavioral variant frontotemporal dementia (bvFTD) will help uncover underlying biological mechanisms, and will improve clinicians’ ability to predict disease course and design targeted management strategies. Objective To identify subtypes of bvFTD syndrome based on distinctive patterns of atrophy defined by selective vulnerability of specific functional networks targeted in bvFTD, using statistical classification approaches. Design, Setting and Participants In this retrospective observational study, 104 patients meeting the Frontotemporal Dementia Consortium consensus criteria for bvFTD were evaluated at the Memory and Aging Center of Department of Neurology at University of California, San Francisco. Patients underwent a multidisciplinary clinical evaluation, including clinical demographics, genetic testing, symptom evaluation, neurological exam, neuropsychological bedside testing, and socioemotional assessments. Ninety patients underwent structural Magnetic Resonance Imaging at their earliest evaluation at the memory clinic. From each patients’ structural imaging, the mean volumes of 18 regions of interest (ROI) comprising the functional networks specifically vulnerable in bvFTD, including the ‘salience network’ (SN), with key nodes in the frontoinsula and pregenual anterior cingulate, and the ‘semantic appraisal network’ (SAN) anchored in the anterior temporal lobe and subgenual cingulate, were estimated. Principal component and cluster analyses of ROI volumes were used to identify patient clusters with anatomically distinct atrophy patterns. Main Outcome Measures We evaluated brain morphology and other clinical features including presenting symptoms, neurologic exam signs, neuropsychological performance, rate of dementia progression, and socioemotional function in each patient cluster. Results We identified four subgroups of bvFTD patients with distinct anatomic patterns of

Distinct types of primary cutaneous large B-cell lymphoma identified by gene expression profiling.

PubMed

Hoefnagel, Juliette J; Dijkman, Remco; Basso, Katia; Jansen, Patty M; Hallermann, Christian; Willemze, Rein; Tensen, Cornelis P; Vermeer, Maarten H

2005-05-01

In the European Organization for Research and Treatment of Cancer (EORTC) classification 2 types of primary cutaneous large B-cell lymphoma (PCLBCL) are distinguished: primary cutaneous follicle center cell lymphomas (PCFCCL) and PCLBCL of the leg (PCLBCL-leg). Distinction between both groups is considered important because of differences in prognosis (5-year survival > 95% and 52%, respectively) and the first choice of treatment (radiotherapy or systemic chemotherapy, respectively), but is not generally accepted. To establish a molecular basis for this subdivision in the EORTC classification, we investigated the gene expression profiles of 21 PCLBCLs by oligonucleotide microarray analysis. Hierarchical clustering based on a B-cell signature (7450 genes) classified PCLBCL into 2 distinct subgroups consisting of, respectively, 8 PCFCCLs and 13 PCLBCLsleg. PCLBCLs-leg showed increased expression of genes associated with cell proliferation; the proto-oncogenes Pim-1, Pim-2, and c-Myc; and the transcription factors Mum1/IRF4 and Oct-2. In the group of PCFCCL high expression of SPINK2 was observed. Further analysis suggested that PCFCCLs and PCLBCLs-leg have expression profiles similar to that of germinal center B-cell-like and activated B-cell-like diffuse large B-cell lymphoma, respectively. The results of this study suggest that different pathogenetic mechanisms are involved in the development of PCFCCLs and PCLBCLs-leg and provide molecular support for the subdivision used in the EORTC classification.

Identifying conserved gene clusters in the presence of homology families.

PubMed

He, Xin; Goldwasser, Michael H

2005-01-01

The study of conserved gene clusters is important for understanding the forces behind genome organization and evolution, as well as the function of individual genes or gene groups. In this paper, we present a new model and algorithm for identifying conserved gene clusters from pairwise genome comparison. This generalizes a recent model called "gene teams." A gene team is a set of genes that appear homologously in two or more species, possibly in a different order yet with the distance of adjacent genes in the team for each chromosome always no more than a certain threshold. We remove the constraint in the original model that each gene must have a unique occurrence in each chromosome and thus allow the analysis on complex prokaryotic or eukaryotic genomes with extensive paralogs. Our algorithm analyzes a pair of chromosomes in O(mn) time and uses O(m+n) space, where m and n are the number of genes in the respective chromosomes. We demonstrate the utility of our methods by studying two bacterial genomes, E. coli K-12 and B. subtilis. Many of the teams identified by our algorithm correlate with documented E. coli operons, while several others match predicted operons, previously suggested by computational techniques. Our implementation and data are publicly available at euler.slu.edu/ approximately goldwasser/homologyteams/.

Profiling of ARDS pulmonary edema fluid identifies a metabolically distinct subset.

PubMed

Rogers, Angela J; Contrepois, Kévin; Wu, Manhong; Zheng, Ming; Peltz, Gary; Ware, Lorraine B; Matthay, Michael A

2017-05-01

There is considerable biological and physiological heterogeneity among patients who meet standard clinical criteria for acute respiratory distress syndrome (ARDS). In this study, we tested the hypothesis that there exists a subgroup of ARDS patients who exhibit a metabolically distinct profile. We examined undiluted pulmonary edema fluid obtained at the time of endotracheal intubation from 16 clinically phenotyped ARDS patients and 13 control patients with hydrostatic pulmonary edema. Nontargeted metabolic profiling was carried out on the undiluted edema fluid. Univariate and multivariate statistical analyses including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were conducted to find discriminant metabolites. Seven-hundred and sixty unique metabolites were identified in the pulmonary edema fluid of these 29 patients. We found that a subset of ARDS patients (6/16, 38%) presented a distinct metabolic profile with the overrepresentation of 235 metabolites compared with edema fluid from the other 10 ARDS patients, whose edema fluid metabolic profile was indistinguishable from those of the 13 control patients with hydrostatic edema. This "high metabolite" endotype was characterized by higher concentrations of metabolites belonging to all of the main metabolic classes including lipids, amino acids, and carbohydrates. This distinct group with high metabolite levels in the edema fluid was also associated with a higher mortality rate. Thus metabolic profiling of the edema fluid of ARDS patients supports the hypothesis that there is considerable biological heterogeneity among ARDS patients who meet standard clinical and physiological criteria for ARDS. Copyright © 2017 the American Physiological Society.

Breast cancer and symptom clusters during radiotherapy.

PubMed

Matthews, Ellyn E; Schmiege, Sarah J; Cook, Paul F; Sousa, Karen H

2012-01-01

Symptom clusters assessment shifts the clinical focus from a specific symptom to the patient's experience as a whole. Few studies have examined breast cancer symptom clusters during treatment, and fewer studies have addressed symptom clusters during radiation therapy (RT). The theoretical underpinning of this study is the Symptoms Experience Model. Research is needed to identify antecedents and consequences of cancer-related symptom clusters. The present study was intended to determine the clustering of symptoms during RT in women with breast cancer and significant correlations among the symptoms, individual characteristics, and mood. A secondary data analysis from a descriptive correlational study of 93 women at weeks 3 to 7 of RT from centers in the mid-Atlantic region of the United States, Symptom Distress Scale, the subscales of the Positive and Negative Affect Scale, Life Orientation Test, and Self-transcendence Scale were completed. Confirmatory factor analysis revealed symptoms grouped into 3 distinct clusters: pain-insomnia-fatigue, cognitive disturbance-outlook, and gastrointestinal. The pain-insomnia-fatigue and cognitive disturbance-outlook clusters were associated with individual characteristics, optimism, self-transcendence, and positive and negative mood. The gastrointestinal cluster correlated significantly only with positive mood. This study provides insight into symptoms that group together and the relationship of symptom clusters to antecedents and mood. These findings underscore the need to define and standardize the measurement of symptom clusters and understand variability in concurrent symptoms. Attention to symptom clusters shifts the clinical focus from a specific symptom to the patient's experience as a whole and helps identify the most effective interventions.

Methylation profiling of choroid plexus tumors reveals 3 clinically distinct subgroups.

PubMed

Thomas, Christian; Sill, Martin; Ruland, Vincent; Witten, Anika; Hartung, Stefan; Kordes, Uwe; Jeibmann, Astrid; Beschorner, Rudi; Keyvani, Kathy; Bergmann, Markus; Mittelbronn, Michel; Pietsch, Torsten; Felsberg, Jörg; Monoranu, Camelia M; Varlet, Pascale; Hauser, Peter; Olar, Adriana; Grundy, Richard G; Wolff, Johannes E; Korshunov, Andrey; Jones, David T; Bewerunge-Hudler, Melanie; Hovestadt, Volker; von Deimling, Andreas; Pfister, Stefan M; Paulus, Werner; Capper, David; Hasselblatt, Martin

2016-06-01

Choroid plexus tumors are intraventricular neoplasms derived from the choroid plexus epithelium. A better knowledge of molecular factors involved in choroid plexus tumor biology may aid in identifying patients at risk for recurrence. Methylation profiles were examined in 29 choroid plexus papillomas (CPPs, WHO grade I), 32 atypical choroid plexus papillomas (aCPPs, WHO grade II), and 31 choroid plexus carcinomas (CPCs, WHO grade III) by Illumina Infinium HumanMethylation450 Bead Chip Array. Unsupervised hierarchical clustering identified 3 subgroups: methylation cluster 1 (pediatric CPP and aCPP of mainly supratentorial location), methylation cluster 2 (adult CPP and aCPP of mainly infratentorial location), and methylation cluster 3 (pediatric CPP, aCPP, and CPC of supratentorial location). In methylation cluster 3, progression-free survival (PFS) accounted for a mean of 72 months (CI, 55-89 mo), whereas only 1 of 42 tumors of methylation clusters 1 and 2 progressed (P< .001). On stratification of outcome data according to WHO grade, all CPCs clustered within cluster 3 and were associated with shorter overall survival (mean, 105 mo [CI, 81-128 mo]) and PFS (mean, 55 mo [CI, 36-73 mo]). The aCPP of methylation cluster 3 also progressed frequently (mean, 69 mo [CI, 44-93 mo]), whereas no tumor progression was observed in aCPP of methylation clusters 1 and 2 (P< .05). Only 1 of 29 CPPs recurred. Methylation profiling of choroid plexus tumors reveals 3 distinct subgroups (ie, pediatric low-risk choroid plexus tumors [cluster 1], adult low-risk choroid plexus tumors [cluster 2], and pediatric high-risk choroid plexus tumors [cluster 3]) and may provide useful prognostic information in addition to histopathology. Published by Oxford University Press on behalf of the Society for Neuro-Oncology 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Machine-learned cluster identification in high-dimensional data.

PubMed

Ultsch, Alfred; Lötsch, Jörn

2017-02-01

High-dimensional biomedical data are frequently clustered to identify subgroup structures pointing at distinct disease subtypes. It is crucial that the used cluster algorithm works correctly. However, by imposing a predefined shape on the clusters, classical algorithms occasionally suggest a cluster structure in homogenously distributed data or assign data points to incorrect clusters. We analyzed whether this can be avoided by using emergent self-organizing feature maps (ESOM). Data sets with different degrees of complexity were submitted to ESOM analysis with large numbers of neurons, using an interactive R-based bioinformatics tool. On top of the trained ESOM the distance structure in the high dimensional feature space was visualized in the form of a so-called U-matrix. Clustering results were compared with those provided by classical common cluster algorithms including single linkage, Ward and k-means. Ward clustering imposed cluster structures on cluster-less "golf ball", "cuboid" and "S-shaped" data sets that contained no structure at all (random data). Ward clustering also imposed structures on permuted real world data sets. By contrast, the ESOM/U-matrix approach correctly found that these data contain no cluster structure. However, ESOM/U-matrix was correct in identifying clusters in biomedical data truly containing subgroups. It was always correct in cluster structure identification in further canonical artificial data. Using intentionally simple data sets, it is shown that popular clustering algorithms typically used for biomedical data sets may fail to cluster data correctly, suggesting that they are also likely to perform erroneously on high dimensional biomedical data. The present analyses emphasized that generally established classical hierarchical clustering algorithms carry a considerable tendency to produce erroneous results. By contrast, unsupervised machine-learned analysis of cluster structures, applied using the ESOM/U-matrix method, is a

An Atlas of Peroxiredoxins Created Using an Active Site Profile-Based Approach to Functionally Relevant Clustering of Proteins.

PubMed

Harper, Angela F; Leuthaeuser, Janelle B; Babbitt, Patricia C; Morris, John H; Ferrin, Thomas E; Poole, Leslie B; Fetrow, Jacquelyn S

2017-02-01

Peroxiredoxins (Prxs or Prdxs) are a large protein superfamily of antioxidant enzymes that rapidly detoxify damaging peroxides and/or affect signal transduction and, thus, have roles in proliferation, differentiation, and apoptosis. Prx superfamily members are widespread across phylogeny and multiple methods have been developed to classify them. Here we present an updated atlas of the Prx superfamily identified using a novel method called MISST (Multi-level Iterative Sequence Searching Technique). MISST is an iterative search process developed to be both agglomerative, to add sequences containing similar functional site features, and divisive, to split groups when functional site features suggest distinct functionally-relevant clusters. Superfamily members need not be identified initially-MISST begins with a minimal representative set of known structures and searches GenBank iteratively. Further, the method's novelty lies in the manner in which isofunctional groups are selected; rather than use a single or shifting threshold to identify clusters, the groups are deemed isofunctional when they pass a self-identification criterion, such that the group identifies itself and nothing else in a search of GenBank. The method was preliminarily validated on the Prxs, as the Prxs presented challenges of both agglomeration and division. For example, previous sequence analysis clustered the Prx functional families Prx1 and Prx6 into one group. Subsequent expert analysis clearly identified Prx6 as a distinct functionally relevant group. The MISST process distinguishes these two closely related, though functionally distinct, families. Through MISST search iterations, over 38,000 Prx sequences were identified, which the method divided into six isofunctional clusters, consistent with previous expert analysis. The results represent the most complete computational functional analysis of proteins comprising the Prx superfamily. The feasibility of this novel method is demonstrated by

An Atlas of Peroxiredoxins Created Using an Active Site Profile-Based Approach to Functionally Relevant Clustering of Proteins

PubMed Central

Babbitt, Patricia C.; Ferrin, Thomas E.

2017-01-01

Peroxiredoxins (Prxs or Prdxs) are a large protein superfamily of antioxidant enzymes that rapidly detoxify damaging peroxides and/or affect signal transduction and, thus, have roles in proliferation, differentiation, and apoptosis. Prx superfamily members are widespread across phylogeny and multiple methods have been developed to classify them. Here we present an updated atlas of the Prx superfamily identified using a novel method called MISST (Multi-level Iterative Sequence Searching Technique). MISST is an iterative search process developed to be both agglomerative, to add sequences containing similar functional site features, and divisive, to split groups when functional site features suggest distinct functionally-relevant clusters. Superfamily members need not be identified initially—MISST begins with a minimal representative set of known structures and searches GenBank iteratively. Further, the method’s novelty lies in the manner in which isofunctional groups are selected; rather than use a single or shifting threshold to identify clusters, the groups are deemed isofunctional when they pass a self-identification criterion, such that the group identifies itself and nothing else in a search of GenBank. The method was preliminarily validated on the Prxs, as the Prxs presented challenges of both agglomeration and division. For example, previous sequence analysis clustered the Prx functional families Prx1 and Prx6 into one group. Subsequent expert analysis clearly identified Prx6 as a distinct functionally relevant group. The MISST process distinguishes these two closely related, though functionally distinct, families. Through MISST search iterations, over 38,000 Prx sequences were identified, which the method divided into six isofunctional clusters, consistent with previous expert analysis. The results represent the most complete computational functional analysis of proteins comprising the Prx superfamily. The feasibility of this novel method is demonstrated

Cluster Analysis of Vulnerable Groups in Acute Traumatic Brain Injury Rehabilitation.

PubMed

Kucukboyaci, N Erkut; Long, Coralynn; Smith, Michelle; Rath, Joseph F; Bushnik, Tamara

2018-01-06

To analyze the complex relation between various social indicators that contribute to socioeconomic status and health care barriers. Cluster analysis of historical patient data obtained from inpatient visits. Inpatient rehabilitation unit in a large urban university hospital. Adult patients (N=148) receiving acute inpatient care, predominantly for closed head injury. Not applicable. We examined the membership of patients with traumatic brain injury in various "vulnerable group" clusters (eg, homeless, unemployed, racial/ethnic minority) and characterized the rehabilitation outcomes of patients (eg, duration of stay, changes in FIM scores between admission to inpatient stay and discharge). The cluster analysis revealed 4 major clusters (ie, clusters A-D) separated by vulnerable group memberships, with distinct durations of stay and FIM gains during their stay. Cluster B, the largest cluster and also consisting of mostly racial/ethnic minorities, had the shortest duration of hospital stay and one of the lowest FIM improvements among the 4 clusters despite higher FIM scores at admission. In cluster C, also consisting of mostly ethnic minorities with multiple socioeconomic status vulnerabilities, patients were characterized by low cognitive FIM scores at admission and the longest duration of stay, and they showed good improvement in FIM scores. Application of clustering techniques to inpatient data identified distinct clusters of patients who may experience differences in their rehabilitation outcome due to their membership in various "at-risk" groups. The results identified patients (ie, cluster B, with minority patients; and cluster D, with elderly patients) who attain below-average gains in brain injury rehabilitation. The results also suggested that systemic (eg, duration of stay) or clinical service improvements (eg, staff's language skills, ability to offer substance abuse therapy, provide appropriate referrals, liaise with intensive social work services, or plan

Typology of patients with fibromyalgia: cluster analysis of duloxetine study patients.

PubMed

Lipkovich, Ilya A; Choy, Ernest H; Van Wambeke, Peter; Deberdt, Walter; Sagman, Doron

2014-12-23

To identify distinct groups of patients with fibromyalgia (FM) with respect to multiple outcome measures. Data from 631 duloxetine-treated women in 4 randomized, placebo-controlled trials were included in a cluster analysis based on outcomes after up to 12 weeks of treatment. Corresponding classification rules were constructed using a classification tree method. Probabilities for transitioning from baseline to Week 12 category were estimated for placebo and duloxetine patients (Ntotal = 1188) using logistic regression. Five clusters were identified, from "worst" (high pain levels and severe mental/physical impairment) to "best" (low pain levels and nearly normal mental/physical function). For patients with moderate overall severity, mental and physical symptoms were less correlated, resulting in 2 distinct clusters based on these 2 symptom domains. Three key variables with threshold values were identified for classification of patients: Brief Pain Inventory (BPI) pain interference overall scores of <3.29 and <7.14, respectively, a Fibromyalgia Impact Questionnaire (FIQ) interference with work score of <2, and an FIQ depression score of ≥5. Patient characteristics and frequencies per baseline category were similar between treatments; >80% of patients were in the 3 worst categories. Duloxetine patients were significantly more likely to improve after 12 weeks than placebo patients. A sustained effect was seen with continued duloxetine treatment. FM patients are heterogeneous and can be classified into distinct subgroups by simple descriptive rules derived from only 3 variables, which may guide individual patient management. Duloxetine showed higher improvement rates than placebo and had a sustained effect beyond 12 weeks.

Abraham's children in the genome era: major Jewish diaspora populations comprise distinct genetic clusters with shared Middle Eastern Ancestry.

PubMed

Atzmon, Gil; Hao, Li; Pe'er, Itsik; Velez, Christopher; Pearlman, Alexander; Palamara, Pier Francesco; Morrow, Bernice; Friedman, Eitan; Oddoux, Carole; Burns, Edward; Ostrer, Harry

2010-06-11

For more than a century, Jews and non-Jews alike have tried to define the relatedness of contemporary Jewish people. Previous genetic studies of blood group and serum markers suggested that Jewish groups had Middle Eastern origin with greater genetic similarity between paired Jewish populations. However, these and successor studies of monoallelic Y chromosomal and mitochondrial genetic markers did not resolve the issues of within and between-group Jewish genetic identity. Here, genome-wide analysis of seven Jewish groups (Iranian, Iraqi, Syrian, Italian, Turkish, Greek, and Ashkenazi) and comparison with non-Jewish groups demonstrated distinctive Jewish population clusters, each with shared Middle Eastern ancestry, proximity to contemporary Middle Eastern populations, and variable degrees of European and North African admixture. Two major groups were identified by principal component, phylogenetic, and identity by descent (IBD) analysis: Middle Eastern Jews and European/Syrian Jews. The IBD segment sharing and the proximity of European Jews to each other and to southern European populations suggested similar origins for European Jewry and refuted large-scale genetic contributions of Central and Eastern European and Slavic populations to the formation of Ashkenazi Jewry. Rapid decay of IBD in Ashkenazi Jewish genomes was consistent with a severe bottleneck followed by large expansion, such as occurred with the so-called demographic miracle of population expansion from 50,000 people at the beginning of the 15th century to 5,000,000 people at the beginning of the 19th century. Thus, this study demonstrates that European/Syrian and Middle Eastern Jews represent a series of geographical isolates or clusters woven together by shared IBD genetic threads.

Abraham's Children in the Genome Era: Major Jewish Diaspora Populations Comprise Distinct Genetic Clusters with Shared Middle Eastern Ancestry

PubMed Central

Atzmon, Gil; Hao, Li; Pe'er, Itsik; Velez, Christopher; Pearlman, Alexander; Palamara, Pier Francesco; Morrow, Bernice; Friedman, Eitan; Oddoux, Carole; Burns, Edward; Ostrer, Harry

2010-01-01

For more than a century, Jews and non-Jews alike have tried to define the relatedness of contemporary Jewish people. Previous genetic studies of blood group and serum markers suggested that Jewish groups had Middle Eastern origin with greater genetic similarity between paired Jewish populations. However, these and successor studies of monoallelic Y chromosomal and mitochondrial genetic markers did not resolve the issues of within and between-group Jewish genetic identity. Here, genome-wide analysis of seven Jewish groups (Iranian, Iraqi, Syrian, Italian, Turkish, Greek, and Ashkenazi) and comparison with non-Jewish groups demonstrated distinctive Jewish population clusters, each with shared Middle Eastern ancestry, proximity to contemporary Middle Eastern populations, and variable degrees of European and North African admixture. Two major groups were identified by principal component, phylogenetic, and identity by descent (IBD) analysis: Middle Eastern Jews and European/Syrian Jews. The IBD segment sharing and the proximity of European Jews to each other and to southern European populations suggested similar origins for European Jewry and refuted large-scale genetic contributions of Central and Eastern European and Slavic populations to the formation of Ashkenazi Jewry. Rapid decay of IBD in Ashkenazi Jewish genomes was consistent with a severe bottleneck followed by large expansion, such as occurred with the so-called demographic miracle of population expansion from 50,000 people at the beginning of the 15th century to 5,000,000 people at the beginning of the 19th century. Thus, this study demonstrates that European/Syrian and Middle Eastern Jews represent a series of geographical isolates or clusters woven together by shared IBD genetic threads. PMID:20560205

On Identifying Clusters Within the C-type Asteroids of the Sloan Digital Sky Survey

NASA Astrophysics Data System (ADS)

Poole, Renae; Ziffer, J.; Harvell, T.

2012-10-01

We applied AutoClass, a data mining technique based upon Bayesian Classification, to C-group asteroid colors in the Sloan Digital Sky Survey (SDSS). Previous taxonomic studies relied mostly on Principal Component Analysis (PCA) to differentiate asteroids within the C-group (e.g. B, G, F, Ch, Cg and Cb). AutoClass's advantage is that it calculates the most probable classification for us, removing the human factor from this part of the analysis. In our results, AutoClass divided the C-groups into two large classes and six smaller classes. The two large classes (n=4974 and 2033, respectively) display distinct regions with some overlap in color-vs-color plots. Each cluster's average spectrum is compared to 'typical' spectra of the C-group subtypes as defined by Tholen (1989) and each cluster's members are evaluated for consistency with previous taxonomies. Of the 117 asteroids classified as B-type in previous taxonomies, only 12 were found with SDSS colors that matched our criteria of having less than 0.1 magnitude error in u and 0.05 magnitude error in g, r, i, and z colors. Although this is a relatively small group, 11 of the 12 B-types were placed by AutoClass in the same cluster. By determining the C-group sub-classifications in the large SDSS database, this research furthers our understanding of the stratigraphy and composition of the main-belt.

Stable isotope phenotyping via cluster analysis of NanoSIMS data as a method for characterizing distinct microbial ecophysiologies and sulfur-cycling in the environment

NASA Astrophysics Data System (ADS)

Dawson, K.; Scheller, S.; Dillon, J. G.; Orphan, V. J.

2016-12-01

Stable isotope probing (SIP) is a valuable tool for gaining insights into ecophysiology and biogeochemical cycling of environmental microbial communities by tracking isotopically labeled compounds into cellular macromolecules as well as into byproducts of respiration. SIP, in conjunction with nanoscale secondary ion mass spectrometry (NanoSIMS), allows for the visualization of isotope incorporation at the single cell level. In this manner, both active cells within a diverse population as well as heterogeneity in metabolism within a homogeneous population can be observed. The ecophysiological implications of these single cell stable isotope measurements are often limited to the taxonomic resolution of paired fluorescence in situ hybridization (FISH) microscopy. Here we introduce a taxonomy-independent method using multi-isotope SIP and NanoSIMS for identifying and grouping phenotypically similar microbial cells by their chemical and isotopic fingerprint. This method was applied to SIP experiments in a sulfur-cycling biofilm collected from sulfidic intertidal vents amended with 13C-acetate, 15N-ammonium, and 33S-sulfate. Using a cluster analysis technique based on fuzzy c-means to group cells according to their isotope (13C/12C, 15N/14N, and 33S/32S) and elemental ratio (C/CN and S/CN) profiles, our analysis partitioned 2200 cellular regions of interest (ROIs) into 5 distinct groups. These isotope phenotype groupings are reflective of the variation in labeled substrate uptake by cells in a multispecies metabolic network dominated by Gamma- and Deltaproteobacteria. Populations independently grouped by isotope phenotype were subsequently compared with paired FISH data, demonstrating a single coherent deltaproteobacterial cluster and multiple gammaproteobacterial groups, highlighting the distinct ecophysiologies of spatially-associated microbes within the sulfur-cycling biofilm from White Point Beach, CA.

Stable Isotope Phenotyping via Cluster Analysis of NanoSIMS Data As a Method for Characterizing Distinct Microbial Ecophysiologies and Sulfur-Cycling in the Environment

PubMed Central

Dawson, Katherine S.; Scheller, Silvan; Dillon, Jesse G.; Orphan, Victoria J.

2016-01-01

Stable isotope probing (SIP) is a valuable tool for gaining insights into ecophysiology and biogeochemical cycling of environmental microbial communities by tracking isotopically labeled compounds into cellular macromolecules as well as into byproducts of respiration. SIP, in conjunction with nanoscale secondary ion mass spectrometry (NanoSIMS), allows for the visualization of isotope incorporation at the single cell level. In this manner, both active cells within a diverse population as well as heterogeneity in metabolism within a homogeneous population can be observed. The ecophysiological implications of these single cell stable isotope measurements are often limited to the taxonomic resolution of paired fluorescence in situ hybridization (FISH) microscopy. Here we introduce a taxonomy-independent method using multi-isotope SIP and NanoSIMS for identifying and grouping phenotypically similar microbial cells by their chemical and isotopic fingerprint. This method was applied to SIP experiments in a sulfur-cycling biofilm collected from sulfidic intertidal vents amended with 13C-acetate, 15N-ammonium, and 33S-sulfate. Using a cluster analysis technique based on fuzzy c-means to group cells according to their isotope (13C/12C, 15N/14N, and 33S/32S) and elemental ratio (C/CN and S/CN) profiles, our analysis partitioned ~2200 cellular regions of interest (ROIs) into five distinct groups. These isotope phenotype groupings are reflective of the variation in labeled substrate uptake by cells in a multispecies metabolic network dominated by Gamma- and Deltaproteobacteria. Populations independently grouped by isotope phenotype were subsequently compared with paired FISH data, demonstrating a single coherent deltaproteobacterial cluster and multiple gammaproteobacterial groups, highlighting the distinct ecophysiologies of spatially-associated microbes within the sulfur-cycling biofilm from White Point Beach, CA. PMID:27303371

Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis

PubMed Central

Grillet, Yves; Richard, Philippe; Stach, Bruno; Vivodtzev, Isabelle; Timsit, Jean-Francois; Lévy, Patrick; Tamisier, Renaud; Pépin, Jean-Louis

2016-01-01

Background The classification of obstructive sleep apnea is on the basis of sleep study criteria that may not adequately capture disease heterogeneity. Improved phenotyping may improve prognosis prediction and help select therapeutic strategies. Objectives: This study used cluster analysis to investigate the clinical clusters of obstructive sleep apnea. Methods An ascending hierarchical cluster analysis was performed on baseline symptoms, physical examination, risk factor exposure and co-morbidities from 18,263 participants in the OSFP (French national registry of sleep apnea). The probability for criteria to be associated with a given cluster was assessed using odds ratios, determined by univariate logistic regression. Results: Six clusters were identified, in which patients varied considerably in age, sex, symptoms, obesity, co-morbidities and environmental risk factors. The main significant differences between clusters were minimally symptomatic versus sleepy obstructive sleep apnea patients, lean versus obese, and among obese patients different combinations of co-morbidities and environmental risk factors. Conclusions Our cluster analysis identified six distinct clusters of obstructive sleep apnea. Our findings underscore the high degree of heterogeneity that exists within obstructive sleep apnea patients regarding clinical presentation, risk factors and consequences. This may help in both research and clinical practice for validating new prevention programs, in diagnosis and in decisions regarding therapeutic strategies. PMID:27314230

Galaxy Cluster Mass Reconstruction Project – III. The impact of dynamical substructure on cluster mass estimates

DOE PAGES

Old, L.; Wojtak, R.; Pearce, F. R.; ...

2017-12-20

With the advent of wide-field cosmological surveys, we are approaching samples of hundreds of thousands of galaxy clusters. While such large numbers will help reduce statistical uncertainties, the control of systematics in cluster masses is crucial. Here we examine the effects of an important source of systematic uncertainty in galaxy-based cluster mass estimation techniques: the presence of significant dynamical substructure. Dynamical substructure manifests as dynamically distinct subgroups in phase-space, indicating an ‘unrelaxed’ state. This issue affects around a quarter of clusters in a generally selected sample. We employ a set of mock clusters whose masses have been measured homogeneously withmore » commonly used galaxy-based mass estimation techniques (kinematic, richness, caustic, radial methods). We use these to study how the relation between observationally estimated and true cluster mass depends on the presence of substructure, as identified by various popular diagnostics. We find that the scatter for an ensemble of clusters does not increase dramatically for clusters with dynamical substructure. However, we find a systematic bias for all methods, such that clusters with significant substructure have higher measured masses than their relaxed counterparts. This bias depends on cluster mass: the most massive clusters are largely unaffected by the presence of significant substructure, but masses are significantly overestimated for lower mass clusters, by ~ 10 percent at 10 14 and ≳ 20 percent for ≲ 10 13.5. Finally, the use of cluster samples with different levels of substructure can therefore bias certain cosmological parameters up to a level comparable to the typical uncertainties in current cosmological studies.« less

Galaxy Cluster Mass Reconstruction Project – III. The impact of dynamical substructure on cluster mass estimates

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

Old, L.; Wojtak, R.; Pearce, F. R.

With the advent of wide-field cosmological surveys, we are approaching samples of hundreds of thousands of galaxy clusters. While such large numbers will help reduce statistical uncertainties, the control of systematics in cluster masses is crucial. Here we examine the effects of an important source of systematic uncertainty in galaxy-based cluster mass estimation techniques: the presence of significant dynamical substructure. Dynamical substructure manifests as dynamically distinct subgroups in phase-space, indicating an ‘unrelaxed’ state. This issue affects around a quarter of clusters in a generally selected sample. We employ a set of mock clusters whose masses have been measured homogeneously withmore » commonly used galaxy-based mass estimation techniques (kinematic, richness, caustic, radial methods). We use these to study how the relation between observationally estimated and true cluster mass depends on the presence of substructure, as identified by various popular diagnostics. We find that the scatter for an ensemble of clusters does not increase dramatically for clusters with dynamical substructure. However, we find a systematic bias for all methods, such that clusters with significant substructure have higher measured masses than their relaxed counterparts. This bias depends on cluster mass: the most massive clusters are largely unaffected by the presence of significant substructure, but masses are significantly overestimated for lower mass clusters, by ~ 10 percent at 10 14 and ≳ 20 percent for ≲ 10 13.5. Finally, the use of cluster samples with different levels of substructure can therefore bias certain cosmological parameters up to a level comparable to the typical uncertainties in current cosmological studies.« less

Distinct collective states due to trade-off between attractive and repulsive couplings

NASA Astrophysics Data System (ADS)

Sathiyadevi, K.; Chandrasekar, V. K.; Senthilkumar, D. V.; Lakshmanan, M.

2018-03-01

We investigate the effect of repulsive coupling together with an attractive coupling in a network of nonlocally coupled oscillators. To understand the complex interaction between these two couplings we introduce a control parameter in the repulsive coupling which plays a crucial role in inducing distinct complex collective patterns. In particular, we show the emergence of various cluster chimera death states through a dynamically distinct transition route, namely the oscillatory cluster state and coherent oscillation death state as a function of the repulsive coupling in the presence of the attractive coupling. In the oscillatory cluster state, the oscillators in the network are grouped into two distinct dynamical states of homogeneous and inhomogeneous oscillatory states. Further, the network of coupled oscillators follow the same transition route in the entire coupling range. Depending upon distinct coupling ranges, the system displays different number of clusters in the death state and oscillatory state. We also observe that the number of coherent domains in the oscillatory cluster state exponentially decreases with increase in coupling range and obeys a power-law decay. Additionally, we show analytical stability for observed solitary state, synchronized state, and incoherent oscillation death state.

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.

Data Clustering

NASA Astrophysics Data System (ADS)

Wagstaff, Kiri L.

2012-03-01

On obtaining a new data set, the researcher is immediately faced with the challenge of obtaining a high-level understanding from the observations. What does a typical item look like? What are the dominant trends? How many distinct groups are included in the data set, and how is each one characterized? Which observable values are common, and which rarely occur? Which items stand out as anomalies or outliers from the rest of the data? This challenge is exacerbated by the steady growth in data set size [11] as new instruments push into new frontiers of parameter space, via improvements in temporal, spatial, and spectral resolution, or by the desire to "fuse" observations from different modalities and instruments into a larger-picture understanding of the same underlying phenomenon. Data clustering algorithms provide a variety of solutions for this task. They can generate summaries, locate outliers, compress data, identify dense or sparse regions of feature space, and build data models. It is useful to note up front that "clusters" in this context refer to groups of items within some descriptive feature space, not (necessarily) to "galaxy clusters" which are dense regions in physical space. The goal of this chapter is to survey a variety of data clustering methods, with an eye toward their applicability to astronomical data analysis. In addition to improving the individual researcher’s understanding of a given data set, clustering has led directly to scientific advances, such as the discovery of new subclasses of stars [14] and gamma-ray bursts (GRBs) [38]. All clustering algorithms seek to identify groups within a data set that reflect some observed, quantifiable structure. Clustering is traditionally an unsupervised approach to data analysis, in the sense that it operates without any direct guidance about which items should be assigned to which clusters. There has been a recent trend in the clustering literature toward supporting semisupervised or constrained

Genome-wide association study identifies the SERPINB gene cluster as a susceptibility locus for food allergy.

PubMed

Marenholz, Ingo; Grosche, Sarah; Kalb, Birgit; Rüschendorf, Franz; Blümchen, Katharina; Schlags, Rupert; Harandi, Neda; Price, Mareike; Hansen, Gesine; Seidenberg, Jürgen; Röblitz, Holger; Yürek, Songül; Tschirner, Sebastian; Hong, Xiumei; Wang, Xiaobin; Homuth, Georg; Schmidt, Carsten O; Nöthen, Markus M; Hübner, Norbert; Niggemann, Bodo; Beyer, Kirsten; Lee, Young-Ae

2017-10-20

Genetic factors and mechanisms underlying food allergy are largely unknown. Due to heterogeneity of symptoms a reliable diagnosis is often difficult to make. Here, we report a genome-wide association study on food allergy diagnosed by oral food challenge in 497 cases and 2387 controls. We identify five loci at genome-wide significance, the clade B serpin (SERPINB) gene cluster at 18q21.3, the cytokine gene cluster at 5q31.1, the filaggrin gene, the C11orf30/LRRC32 locus, and the human leukocyte antigen (HLA) region. Stratifying the results for the causative food demonstrates that association of the HLA locus is peanut allergy-specific whereas the other four loci increase the risk for any food allergy. Variants in the SERPINB gene cluster are associated with SERPINB10 expression in leukocytes. Moreover, SERPINB genes are highly expressed in the esophagus. All identified loci are involved in immunological regulation or epithelial barrier function, emphasizing the role of both mechanisms in food allergy.

The efficiency of average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling in identifying homogeneous precipitation catchments

NASA Astrophysics Data System (ADS)

Chuan, Zun Liang; Ismail, Noriszura; Shinyie, Wendy Ling; Lit Ken, Tan; Fam, Soo-Fen; Senawi, Azlyna; Yusoff, Wan Nur Syahidah Wan

2018-04-01

Due to the limited of historical precipitation records, agglomerative hierarchical clustering algorithms widely used to extrapolate information from gauged to ungauged precipitation catchments in yielding a more reliable projection of extreme hydro-meteorological events such as extreme precipitation events. However, identifying the optimum number of homogeneous precipitation catchments accurately based on the dendrogram resulted using agglomerative hierarchical algorithms are very subjective. The main objective of this study is to propose an efficient regionalized algorithm to identify the homogeneous precipitation catchments for non-stationary precipitation time series. The homogeneous precipitation catchments are identified using average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling, while uncentered correlation coefficient as the similarity measure. The regionalized homogeneous precipitation is consolidated using K-sample Anderson Darling non-parametric test. The analysis result shows the proposed regionalized algorithm performed more better compared to the proposed agglomerative hierarchical clustering algorithm in previous studies.

Application of cluster analysis to geochemical compositional data for identifying ore-related geochemical anomalies

NASA Astrophysics Data System (ADS)

Zhou, Shuguang; Zhou, Kefa; Wang, Jinlin; Yang, Genfang; Wang, Shanshan

2017-12-01

Cluster analysis is a well-known technique that is used to analyze various types of data. In this study, cluster analysis is applied to geochemical data that describe 1444 stream sediment samples collected in northwestern Xinjiang with a sample spacing of approximately 2 km. Three algorithms (the hierarchical, k-means, and fuzzy c-means algorithms) and six data transformation methods (the z-score standardization, ZST; the logarithmic transformation, LT; the additive log-ratio transformation, ALT; the centered log-ratio transformation, CLT; the isometric log-ratio transformation, ILT; and no transformation, NT) are compared in terms of their effects on the cluster analysis of the geochemical compositional data. The study shows that, on the one hand, the ZST does not affect the results of column- or variable-based (R-type) cluster analysis, whereas the other methods, including the LT, the ALT, and the CLT, have substantial effects on the results. On the other hand, the results of the row- or observation-based (Q-type) cluster analysis obtained from the geochemical data after applying NT and the ZST are relatively poor. However, we derive some improved results from the geochemical data after applying the CLT, the ILT, the LT, and the ALT. Moreover, the k-means and fuzzy c-means clustering algorithms are more reliable than the hierarchical algorithm when they are used to cluster the geochemical data. We apply cluster analysis to the geochemical data to explore for Au deposits within the study area, and we obtain a good correlation between the results retrieved by combining the CLT or the ILT with the k-means or fuzzy c-means algorithms and the potential zones of Au mineralization. Therefore, we suggest that the combination of the CLT or the ILT with the k-means or fuzzy c-means algorithms is an effective tool to identify potential zones of mineralization from geochemical data.

Globular Cluster Contributions to the Galactic Halo

NASA Astrophysics Data System (ADS)

Martell, Sarah; Grebel, Eva; Lai, David

2010-08-01

The goal of this project is to confirm chemically that globular clusters are the source of as much as half the population of the Galactic halo. Using moderate-resolution spectroscopy from the SEGUE survey, we have identified a previously unknown population of halo field giants with distinctly strong CN features. CN variations are typically only observed in globular clusters, so these stars are interpreted as immigrants to the halo that originally formed in globular clusters. In one night of Keck/HIRES time, we will obtain high-quality, high- resolution spectra for five such stars, and determine abundances of O, Na, Mg, Al, alpha, iron-peak and neutron-capture elements. With this information we can state clearly whether these unusual CN-strong halo stars carry the full abundance pattern seen in CN-strong globular cluster stars, with depleted C, O, and Mg and enhanced N, Na, and Al. This type of coarse ``chemical tagging'' will allow a clearer division of the Galactic halo into contributions from globular clusters and from dwarf galaxies, and will place constraints on theoretical models of globular cluster formation and evolution.

Utilizing Hierarchical Clustering to improve Efficiency of Self-Organizing Feature Map to Identify Hydrological Homogeneous Regions

NASA Astrophysics Data System (ADS)

Farsadnia, Farhad; Ghahreman, Bijan

2016-04-01

Hydrologic homogeneous group identification is considered both fundamental and applied research in hydrology. Clustering methods are among conventional methods to assess the hydrological homogeneous regions. Recently, Self-Organizing feature Map (SOM) method has been applied in some studies. However, the main problem of this method is the interpretation on the output map of this approach. Therefore, SOM is used as input to other clustering algorithms. The aim of this study is to apply a two-level Self-Organizing feature map and Ward hierarchical clustering method to determine the hydrologic homogenous regions in North and Razavi Khorasan provinces. At first by principal component analysis, we reduced SOM input matrix dimension, then the SOM was used to form a two-dimensional features map. To determine homogeneous regions for flood frequency analysis, SOM output nodes were used as input into the Ward method. Generally, the regions identified by the clustering algorithms are not statistically homogeneous. Consequently, they have to be adjusted to improve their homogeneity. After adjustment of the homogeneity regions by L-moment tests, five hydrologic homogeneous regions were identified. Finally, adjusted regions were created by a two-level SOM and then the best regional distribution function and associated parameters were selected by the L-moment approach. The results showed that the combination of self-organizing maps and Ward hierarchical clustering by principal components as input is more effective than the hierarchical method, by principal components or standardized inputs to achieve hydrologic homogeneous regions.

Identification and validation of asthma phenotypes in Chinese population using cluster analysis.

PubMed

Wang, Lei; Liang, Rui; Zhou, Ting; Zheng, Jing; Liang, Bing Miao; Zhang, Hong Ping; Luo, Feng Ming; Gibson, Peter G; Wang, Gang

2017-10-01

Asthma is a heterogeneous airway disease, so it is crucial to clearly identify clinical phenotypes to achieve better asthma management. To identify and prospectively validate asthma clusters in a Chinese population. Two hundred eighty-four patients were consecutively recruited and 18 sociodemographic and clinical variables were collected. Hierarchical cluster analysis was performed by the Ward method followed by k-means cluster analysis. Then, a prospective 12-month cohort study was used to validate the identified clusters. Five clusters were successfully identified. Clusters 1 (n = 71) and 3 (n = 81) were mild asthma phenotypes with slight airway obstruction and low exacerbation risk, but with a sex differential. Cluster 2 (n = 65) described an "allergic" phenotype, cluster 4 (n = 33) featured a "fixed airflow limitation" phenotype with smoking, and cluster 5 (n = 34) was a "low socioeconomic status" phenotype. Patients in clusters 2, 4, and 5 had distinctly lower socioeconomic status and more psychological symptoms. Cluster 2 had a significantly increased risk of exacerbations (risk ratio [RR] 1.13, 95% confidence interval [CI] 1.03-1.25), unplanned visits for asthma (RR 1.98, 95% CI 1.07-3.66), and emergency visits for asthma (RR 7.17, 95% CI 1.26-40.80). Cluster 4 had an increased risk of unplanned visits (RR 2.22, 95% CI 1.02-4.81), and cluster 5 had increased emergency visits (RR 12.72, 95% CI 1.95-69.78). Kaplan-Meier analysis confirmed that cluster grouping was predictive of time to the first asthma exacerbation, unplanned visit, emergency visit, and hospital admission (P < .0001 for all comparisons). We identified 3 clinical clusters as "allergic asthma," "fixed airflow limitation," and "low socioeconomic status" phenotypes that are at high risk of severe asthma exacerbations and that have management implications for clinical practice in developing countries. Copyright © 2017 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc

Identifying Peer Institutions Using Cluster Analysis

ERIC Educational Resources Information Center

Boronico, Jess; Choksi, Shail S.

2012-01-01

The New York Institute of Technology's (NYIT) School of Management (SOM) wishes to develop a list of peer institutions for the purpose of benchmarking and monitoring/improving performance against other business schools. The procedure utilizes relevant criteria for the purpose of establishing this peer group by way of a cluster analysis. The…

Exploratory Cluster Analysis to Identify Patterns of Chronic Kidney Disease in the 500 Cities Project.

PubMed

Liu, Shelley H; Li, Yan; Liu, Bian

2018-05-17

Chronic kidney disease is a leading cause of death in the United States. We used cluster analysis to explore patterns of chronic kidney disease in 500 of the largest US cities. After adjusting for socio-demographic characteristics, we found that unhealthy behaviors, prevention measures, and health outcomes related to chronic kidney disease differ between cities in Utah and those in the rest of the United States. Cluster analysis can be useful for identifying geographic regions that may have important policy implications for preventing chronic kidney disease.

Identifying Subgroups of Tinnitus Using Novel Resting State fMRI Biomarkers and Cluster Analysis

DTIC Science & Technology

2017-10-13

AWARD NUMBER: W81XWH-15-2-0032 TITLE: Identifying Subgroups of Tinnitus Using Novel Resting State fMRI Biomarkers and Cluster Analysis...TITLE AND SUBTITLE 5a. CONTRACT NUMBER W81XWH-15-2-0032 5b. GRANT NUMBER Identifying Subgroups of Tinnitus Using Novel Resting State fMRI...Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT The subject of the project is FY14 PRMRP Topic Area – Tinnitus . The broad goal is

Use of a spatial scan statistic to identify clusters of births occurring outside Ghanaian health facilities for targeted intervention.

PubMed

Bosomprah, Samuel; Dotse-Gborgbortsi, Winfred; Aboagye, Patrick; Matthews, Zoe

2016-11-01

To identify and evaluate clusters of births that occurred outside health facilities in Ghana for targeted intervention. A retrospective study was conducted using a convenience sample of live births registered in Ghanaian health facilities from January 1 to December 31, 2014. Data were extracted from the district health information system. A spatial scan statistic was used to investigate clusters of home births through a discrete Poisson probability model. Scanning with a circular spatial window was conducted only for clusters with high rates of such deliveries. The district was used as the geographic unit of analysis. The likelihood P value was estimated using Monte Carlo simulations. Ten statistically significant clusters with a high rate of home birth were identified. The relative risks ranged from 1.43 ("least likely" cluster; P=0.001) to 1.95 ("most likely" cluster; P=0.001). The relative risks of the top five "most likely" clusters ranged from 1.68 to 1.95; these clusters were located in Ashanti, Brong Ahafo, and the Western, Eastern, and Greater regions of Accra. Health facility records, geospatial techniques, and geographic information systems provided locally relevant information to assist policy makers in delivering targeted interventions to small geographic areas. Copyright © 2016 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

X-Ray Detection of the Cluster Containing the Cepheid S Mus

NASA Astrophysics Data System (ADS)

Evans, Nancy Remage; Pillitteri, Ignazio; Wolk, Scott; Guinan, Edward; Engle, Scott; Bond, Howard E.; Schaefer, Gail H.; Karovska, Margarita; DePasquale, Joseph; Tingle, Evan

2014-04-01

The galactic Cepheid S Muscae has recently been added to the important list of Cepheids linked to open clusters, in this case the sparse young cluster ASCC 69. Low-mass members of a young cluster are expected to have rapid rotation and X-ray activity, making X-ray emission an excellent way to discriminate them from old field stars. We have made an XMM-Newton observation centered on S Mus and identified a population of X-ray sources whose near-IR Two Micron All Sky Survey counterparts lie at locations in the J, (J - K) color-magnitude diagram consistent with cluster membership at the distance of S Mus. Their median energy and X-ray luminosity are consistent with young cluster members as distinct from field stars. These strengthen the association of S Mus with the young cluster, making it a potential Leavitt law (period-luminosity relation) calibrator.

Genome-Wide Association Analysis Identifies Variants Associated with Nonalcoholic Fatty Liver Disease That Have Distinct Effects on Metabolic Traits

PubMed Central

Palmer, Cameron D.; Gudnason, Vilmundur; Eiriksdottir, Gudny; Garcia, Melissa E.; Launer, Lenore J.; Nalls, Michael A.; Clark, Jeanne M.; Mitchell, Braxton D.; Shuldiner, Alan R.; Butler, Johannah L.; Tomas, Marta; Hoffmann, Udo; Hwang, Shih-Jen; Massaro, Joseph M.; O'Donnell, Christopher J.; Sahani, Dushyant V.; Salomaa, Veikko; Schadt, Eric E.; Schwartz, Stephen M.; Siscovick, David S.; Voight, Benjamin F.; Carr, J. Jeffrey; Feitosa, Mary F.; Harris, Tamara B.; Fox, Caroline S.

2011-01-01

Nonalcoholic fatty liver disease (NAFLD) clusters in families, but the only known common genetic variants influencing risk are near PNPLA3. We sought to identify additional genetic variants influencing NAFLD using genome-wide association (GWA) analysis of computed tomography (CT) measured hepatic steatosis, a non-invasive measure of NAFLD, in large population based samples. Using variance components methods, we show that CT hepatic steatosis is heritable (∼26%–27%) in family-based Amish, Family Heart, and Framingham Heart Studies (n = 880 to 3,070). By carrying out a fixed-effects meta-analysis of genome-wide association (GWA) results between CT hepatic steatosis and ∼2.4 million imputed or genotyped SNPs in 7,176 individuals from the Old Order Amish, Age, Gene/Environment Susceptibility-Reykjavik study (AGES), Family Heart, and Framingham Heart Studies, we identify variants associated at genome-wide significant levels (p<5×10−8) in or near PNPLA3, NCAN, and PPP1R3B. We genotype these and 42 other top CT hepatic steatosis-associated SNPs in 592 subjects with biopsy-proven NAFLD from the NASH Clinical Research Network (NASH CRN). In comparisons with 1,405 healthy controls from the Myocardial Genetics Consortium (MIGen), we observe significant associations with histologic NAFLD at variants in or near NCAN, GCKR, LYPLAL1, and PNPLA3, but not PPP1R3B. Variants at these five loci exhibit distinct patterns of association with serum lipids, as well as glycemic and anthropometric traits. We identify common genetic variants influencing CT–assessed steatosis and risk of NAFLD. Hepatic steatosis associated variants are not uniformly associated with NASH/fibrosis or result in abnormalities in serum lipids or glycemic and anthropometric traits, suggesting genetic heterogeneity in the pathways influencing these traits. PMID:21423719

Genome-wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits.

PubMed

Speliotes, Elizabeth K; Yerges-Armstrong, Laura M; Wu, Jun; Hernaez, Ruben; Kim, Lauren J; Palmer, Cameron D; Gudnason, Vilmundur; Eiriksdottir, Gudny; Garcia, Melissa E; Launer, Lenore J; Nalls, Michael A; Clark, Jeanne M; Mitchell, Braxton D; Shuldiner, Alan R; Butler, Johannah L; Tomas, Marta; Hoffmann, Udo; Hwang, Shih-Jen; Massaro, Joseph M; O'Donnell, Christopher J; Sahani, Dushyant V; Salomaa, Veikko; Schadt, Eric E; Schwartz, Stephen M; Siscovick, David S; Voight, Benjamin F; Carr, J Jeffrey; Feitosa, Mary F; Harris, Tamara B; Fox, Caroline S; Smith, Albert V; Kao, W H Linda; Hirschhorn, Joel N; Borecki, Ingrid B

2011-03-01

Nonalcoholic fatty liver disease (NAFLD) clusters in families, but the only known common genetic variants influencing risk are near PNPLA3. We sought to identify additional genetic variants influencing NAFLD using genome-wide association (GWA) analysis of computed tomography (CT) measured hepatic steatosis, a non-invasive measure of NAFLD, in large population based samples. Using variance components methods, we show that CT hepatic steatosis is heritable (∼26%-27%) in family-based Amish, Family Heart, and Framingham Heart Studies (n = 880 to 3,070). By carrying out a fixed-effects meta-analysis of genome-wide association (GWA) results between CT hepatic steatosis and ∼2.4 million imputed or genotyped SNPs in 7,176 individuals from the Old Order Amish, Age, Gene/Environment Susceptibility-Reykjavik study (AGES), Family Heart, and Framingham Heart Studies, we identify variants associated at genome-wide significant levels (p<5×10(-8)) in or near PNPLA3, NCAN, and PPP1R3B. We genotype these and 42 other top CT hepatic steatosis-associated SNPs in 592 subjects with biopsy-proven NAFLD from the NASH Clinical Research Network (NASH CRN). In comparisons with 1,405 healthy controls from the Myocardial Genetics Consortium (MIGen), we observe significant associations with histologic NAFLD at variants in or near NCAN, GCKR, LYPLAL1, and PNPLA3, but not PPP1R3B. Variants at these five loci exhibit distinct patterns of association with serum lipids, as well as glycemic and anthropometric traits. We identify common genetic variants influencing CT-assessed steatosis and risk of NAFLD. Hepatic steatosis associated variants are not uniformly associated with NASH/fibrosis or result in abnormalities in serum lipids or glycemic and anthropometric traits, suggesting genetic heterogeneity in the pathways influencing these traits.

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.

Nurses' beliefs about nursing diagnosis: A study with cluster analysis.

PubMed

D'Agostino, Fabio; Pancani, Luca; Romero-Sánchez, José Manuel; Lumillo-Gutierrez, Iris; Paloma-Castro, Olga; Vellone, Ercole; Alvaro, Rosaria

2018-06-01

To identify clusters of nurses in relation to their beliefs about nursing diagnosis among two populations (Italian and Spanish); to investigate differences among clusters of nurses in each population considering the nurses' socio-demographic data, attitudes towards nursing diagnosis, intentions to make nursing diagnosis and actual behaviours in making nursing diagnosis. Nurses' beliefs concerning nursing diagnosis can influence its use in practice but this is still unclear. A cross-sectional design. A convenience sample of nurses in Italy and Spain was enrolled. Data were collected between 2014-2015 using tools, that is, a socio-demographic questionnaire and behavioural, normative and control beliefs, attitudes, intentions and behaviours scales. The sample included 499 nurses (272 Italians & 227 Spanish). Of these, 66.5% of the Italian and 90.7% of the Spanish sample were female. The mean age was 36.5 and 45.2 years old in the Italian and Spanish sample respectively. Six clusters of nurses were identified in Spain and four in Italy. Three clusters were similar among the two populations. Similar significant associations between age, years of work, attitudes towards nursing diagnosis, intentions to make nursing diagnosis and behaviours in making nursing diagnosis and cluster membership in each population were identified. Belief profiles identified unique subsets of nurses that have distinct characteristics. Categorizing nurses by belief patterns may help administrators and educators to tailor interventions aimed at improving nursing diagnosis use in practice. © 2018 John Wiley & Sons Ltd.

SU-F-T-312: Identifying Distinct Radiation Therapy Plan Classes Through Multi-Dimensional Analysis of Plan Complexity Metrics

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

Desai, V; Labby, Z; Culberson, W

Purpose: To determine whether body site-specific treatment plans form unique “plan class” clusters in a multi-dimensional analysis of plan complexity metrics such that a single beam quality correction determined for a representative plan could be universally applied within the “plan class”, thereby increasing the dosimetric accuracy of a detector’s response within a subset of similarly modulated nonstandard deliveries. Methods: We collected 95 clinical volumetric modulated arc therapy (VMAT) plans from four body sites (brain, lung, prostate, and spine). The lung data was further subdivided into SBRT and non-SBRT data for a total of five plan classes. For each control pointmore » in each plan, a variety of aperture-based complexity metrics were calculated and stored as unique characteristics of each patient plan. A multiple comparison of means analysis was performed such that every plan class was compared to every other plan class for every complexity metric in order to determine which groups could be considered different from one another. Statistical significance was assessed after correcting for multiple hypothesis testing. Results: Six out of a possible 10 pairwise plan class comparisons were uniquely distinguished based on at least nine out of 14 of the proposed metrics (Brain/Lung, Brain/SBRT lung, Lung/Prostate, Lung/SBRT Lung, Lung/Spine, Prostate/SBRT Lung). Eight out of 14 of the complexity metrics could distinguish at least six out of the possible 10 pairwise plan class comparisons. Conclusion: Aperture-based complexity metrics could prove to be useful tools to quantitatively describe a distinct class of treatment plans. Certain plan-averaged complexity metrics could be considered unique characteristics of a particular plan. A new approach to generating plan-class specific reference (pcsr) fields could be established through a targeted preservation of select complexity metrics or a clustering algorithm that identifies plans exhibiting similar

Distinct developmental profiles in typical speech acquisition

PubMed Central

Campbell, Thomas F.; Shriberg, Lawrence D.; Green, Jordan R.; Abdi, Hervé; Rusiewicz, Heather Leavy; Venkatesh, Lakshmi; Moore, Christopher A.

2012-01-01

Three- to five-year-old children produce speech that is characterized by a high level of variability within and across individuals. This variability, which is manifest in speech movements, acoustics, and overt behaviors, can be input to subgroup discovery methods to identify cohesive subgroups of speakers or to reveal distinct developmental pathways or profiles. This investigation characterized three distinct groups of typically developing children and provided normative benchmarks for speech development. These speech development profiles, identified among 63 typically developing preschool-aged speakers (ages 36–59 mo), were derived from the children's performance on multiple measures. These profiles were obtained by submitting to a k-means cluster analysis of 72 measures that composed three levels of speech analysis: behavioral (e.g., task accuracy, percentage of consonants correct), acoustic (e.g., syllable duration, syllable stress), and kinematic (e.g., variability of movements of the upper lip, lower lip, and jaw). Two of the discovered group profiles were distinguished by measures of variability but not by phonemic accuracy; the third group of children was characterized by their relatively low phonemic accuracy but not by an increase in measures of variability. Analyses revealed that of the original 72 measures, 8 key measures were sufficient to best distinguish the 3 profile groups. PMID:22357794

Identifying typical patterns of vulnerability: A 5-step approach based on cluster analysis

NASA Astrophysics Data System (ADS)

Sietz, Diana; Lüdeke, Matthias; Kok, Marcel; Lucas, Paul; Carsten, Walther; Janssen, Peter

2013-04-01

Specific processes that shape the vulnerability of socio-ecological systems to climate, market and other stresses derive from diverse background conditions. Within the multitude of vulnerability-creating mechanisms, distinct processes recur in various regions inspiring research on typical patterns of vulnerability. The vulnerability patterns display typical combinations of the natural and socio-economic properties that shape a systems' vulnerability to particular stresses. Based on the identification of a limited number of vulnerability patterns, pattern analysis provides an efficient approach to improving our understanding of vulnerability and decision-making for vulnerability reduction. However, current pattern analyses often miss explicit descriptions of their methods and pay insufficient attention to the validity of their groupings. Therefore, the question arises as to how do we identify typical vulnerability patterns in order to enhance our understanding of a systems' vulnerability to stresses? A cluster-based pattern recognition applied at global and local levels is scrutinised with a focus on an applicable methodology and practicable insights. Taking the example of drylands, this presentation demonstrates the conditions necessary to identify typical vulnerability patterns. They are summarised in five methodological steps comprising the elicitation of relevant cause-effect hypotheses and the quantitative indication of mechanisms as well as an evaluation of robustness, a validation and a ranking of the identified patterns. Reflecting scale-dependent opportunities, a global study is able to support decision-making with insights into the up-scaling of interventions when available funds are limited. In contrast, local investigations encourage an outcome-based validation. This constitutes a crucial step in establishing the credibility of the patterns and hence their suitability for informing extension services and individual decisions. In this respect, working at

Deconstructing Bipolar Disorder and Schizophrenia: A cross-diagnostic cluster analysis of cognitive phenotypes.

PubMed

Lee, Junghee; Rizzo, Shemra; Altshuler, Lori; Glahn, David C; Miklowitz, David J; Sugar, Catherine A; Wynn, Jonathan K; Green, Michael F

2017-02-01

Bipolar disorder (BD) and schizophrenia (SZ) show substantial overlap. It has been suggested that a subgroup of patients might contribute to these overlapping features. This study employed a cross-diagnostic cluster analysis to identify subgroups of individuals with shared cognitive phenotypes. 143 participants (68 BD patients, 39 SZ patients and 36 healthy controls) completed a battery of EEG and performance assessments on perception, nonsocial cognition and social cognition. A K-means cluster analysis was conducted with all participants across diagnostic groups. Clinical symptoms, functional capacity, and functional outcome were assessed in patients. A two-cluster solution across 3 groups was the most stable. One cluster including 44 BD patients, 31 controls and 5 SZ patients showed better cognition (High cluster) than the other cluster with 24 BD patients, 35 SZ patients and 5 controls (Low cluster). BD patients in the High cluster performed better than BD patients in the Low cluster across cognitive domains. Within each cluster, participants with different clinical diagnoses showed different profiles across cognitive domains. All patients are in the chronic phase and out of mood episode at the time of assessment and most of the assessment were behavioral measures. This study identified two clusters with shared cognitive phenotype profiles that were not proxies for clinical diagnoses. The finding of better social cognitive performance of BD patients than SZ patients in the Lowe cluster suggest that relatively preserved social cognition may be important to identify disease process distinct to each disorder. Copyright © 2016 Elsevier B.V. All rights reserved.

Chemodynamical Clustering Applied to APOGEE Data: Rediscovering Globular Clusters

NASA Astrophysics Data System (ADS)

Chen, Boquan; D’Onghia, Elena; Pardy, Stephen A.; Pasquali, Anna; Bertelli Motta, Clio; Hanlon, Bret; Grebel, Eva K.

2018-06-01

We have developed a novel technique based on a clustering algorithm that searches for kinematically and chemically clustered stars in the APOGEE DR12 Cannon data. As compared to classical chemical tagging, the kinematic information included in our methodology allows us to identify stars that are members of known globular clusters with greater confidence. We apply our algorithm to the entire APOGEE catalog of 150,615 stars whose chemical abundances are derived by the Cannon. Our methodology found anticorrelations between the elements Al and Mg, Na and O, and C and N previously identified in the optical spectra in globular clusters, even though we omit these elements in our algorithm. Our algorithm identifies globular clusters without a priori knowledge of their locations in the sky. Thus, not only does this technique promise to discover new globular clusters, but it also allows us to identify candidate streams of kinematically and chemically clustered stars in the Milky Way.

Psychosocial Clusters and their Associations with Well-Being and Health: An Empirical Strategy for Identifying Psychosocial Predictors Most Relevant to Racially/Ethnically Diverse Women’s Health

PubMed Central

Jabson, Jennifer M.; Bowen, Deborah; Weinberg, Janice; Kroenke, Candyce; Luo, Juhua; Messina, Catherine; Shumaker, Sally; Tindle, Hilary A.

2016-01-01

BACKGROUND Strategies for identifying the most relevant psychosocial predictors in studies of racial/ethnic minority women’s health are limited because they largely exclude cultural influences and they assume that psychosocial predictors are independent. This paper proposes and tests an empirical solution. METHODS Hierarchical cluster analysis, conducted with data from 140,652 Women’s Health Initiative participants, identified clusters among individual psychosocial predictors. Multivariable analyses tested associations between clusters and health outcomes. RESULTS A Social Cluster and a Stress Cluster were identified. The Social Cluster was positively associated with well-being and inversely associated with chronic disease index, and the Stress Cluster was inversely associated with well-being and positively associated with chronic disease index. As hypothesized, the magnitude of association between clusters and outcomes differed by race/ethnicity. CONCLUSIONS By identifying psychosocial clusters and their associations with health, we have taken an important step toward understanding how individual psychosocial predictors interrelate and how empirically formed Stress and Social clusters relate to health outcomes. This study has also demonstrated important insight about differences in associations between these psychosocial clusters and health among racial/ethnic minorities. These differences could signal the best pathways for intervention modification and tailoring. PMID:27279761

Comparative genomics identifies distinct lineages of S. Enteritidis from Queensland, Australia.

PubMed

Graham, Rikki M A; Hiley, Lester; Rathnayake, Irani U; Jennison, Amy V

2018-01-01

Salmonella enterica is a major cause of gastroenteritis and foodborne illness in Australia where notification rates in the state of Queensland are the highest in the country. S. Enteritidis is among the five most common serotypes reported in Queensland and it is a priority for epidemiological surveillance due to concerns regarding its emergence in Australia. Using whole genome sequencing, we have analysed the genomic epidemiology of 217 S. Enteritidis isolates from Queensland, and observed that they fall into three distinct clades, which we have differentiated as Clades A, B and C. Phage types and MLST sequence types differed between the clades and comparative genomic analysis has shown that each has a unique profile of prophage and genomic islands. Several of the phage regions present in the S. Enteritidis reference strain P125109 were absent in Clades A and C, and these clades also had difference in the presence of pathogenicity islands, containing complete SPI-6 and SPI-19 regions, while P125109 does not. Antimicrobial resistance markers were found in 39 isolates, all but one of which belonged to Clade B. Phylogenetic analysis of the Queensland isolates in the context of 170 international strains showed that Queensland Clade B isolates group together with the previously identified global clade, while the other two clades are distinct and appear largely restricted to Australia. Locally sourced environmental isolates included in this analysis all belonged to Clades A and C, which is consistent with the theory that these clades are a source of locally acquired infection, while Clade B isolates are mostly travel related.

Geographic Clusters of Basal Cell Carcinoma in a Northern California Health Plan Population.

PubMed

Ray, G Thomas; Kulldorff, Martin; Asgari, Maryam M

2016-11-01

Rates of skin cancer, including basal cell carcinoma (BCC), the most common cancer, have been increasing over the past 3 decades. A better understanding of geographic clustering of BCCs can help target screening and prevention efforts. Present a methodology to identify spatial clusters of BCC and identify such clusters in a northern California population. This retrospective study used a BCC registry to determine rates of BCC by census block group, and used spatial scan statistics to identify statistically significant geographic clusters of BCCs, adjusting for age, sex, and socioeconomic status. The study population consisted of white, non-Hispanic members of Kaiser Permanente Northern California during years 2011 and 2012. Statistically significant geographic clusters of BCC as determined by spatial scan statistics. Spatial analysis of 28 408 individuals who received a diagnosis of at least 1 BCC in 2011 or 2012 revealed distinct geographic areas with elevated BCC rates. Among the 14 counties studied, BCC incidence ranged from 661 to 1598 per 100 000 person-years. After adjustment for age, sex, and neighborhood socioeconomic status, a pattern of 5 discrete geographic clusters emerged, with a relative risk ranging from 1.12 (95% CI, 1.03-1.21; P = .006) for a cluster in eastern Sonoma and northern Napa Counties to 1.40 (95% CI, 1.15-1.71; P < .001) for a cluster in east Contra Costa and west San Joaquin Counties, compared with persons residing outside that cluster. In this study of a northern California population, we identified several geographic clusters with modestly elevated incidence of BCC. Knowledge of geographic clusters can help inform future research on the underlying etiology of the clustering including factors related to the environment, health care access, or other characteristics of the resident population, and can help target screening efforts to areas of highest yield.

Use of molecular testing to identify a cluster of patients with polycythemia vera in eastern Pennsylvania.

PubMed

Seaman, Vincent; Jumaan, Aisha; Yanni, Emad; Lewis, Brian; Neyer, Jonathan; Roda, Paul; Xu, Mingjiang; Hoffman, Ronald

2009-02-01

The role of the environment in the origin of polycythemia vera has not been well documented. Recently, molecular diagnostic tools have been developed to facilitate the diagnosis of polycythemia vera. A cluster of patients with polycythemia vera was suspected in three countries in eastern Pennsylvania where there have long been a concern about environment hazards. Rigorous clinical criteria and JAK2 617V>F testing were used to confirm the diagnosis of polycythemia vera in patients in this area. Participants included cases of polycythemia vera from the 2001 to 2005 state cancer registry as well as self- and physician-referred cases. A diagnosis of polycythemia vera was confirmed in 53% of 62 participants using WHO criteria, which includes JAK2 617V>F testing. A statistically significant cluster of cases (P < 0.001) was identified where the incidence of polycythemia vera was 4.3 times that of the rest of the study area. The area of the cluster contained numerous sources of hazardous material including waste-coal power plants and U.S. Environmental Protection Agency Superfund sites. The diagnosis of polycythemia vera based solely on clinical criteria is frequently erroneous, suggesting that our prior knowledge of the epidemiology of this disease might be inaccurate. The JAK2 617V>F mutational analysis provides diagnostic clarity and permitted the confirmation of a cluster of polycythemia vera cases not identified by traditional clinical and pathologic diagnostic criteria. The close proximity of this cluster to known areas of hazardous material exposure raises concern that such environmental factors might play a role in the origin of polycythemia vera.

Chaperone expression profiles correlate with distinct physiological states of Plasmodium falciparum in malaria patients

PubMed Central

2010-01-01

Background Molecular chaperones have been shown to be important in the growth of the malaria parasite Plasmodium falciparum and inhibition of chaperone function by pharmacological agents has been shown to abrogate parasite growth. A recent study has demonstrated that clinical isolates of the parasite have distinct physiological states, one of which resembles environmental stress response showing up-regulation of specific molecular chaperones. Methods Chaperone networks operational in the distinct physiological clusters in clinical malaria parasites were constructed using cytoscape by utilizing their clinical expression profiles. Results Molecular chaperones show distinct profiles in the previously defined physiologically distinct states. Further, expression profiles of the chaperones from different cellular compartments correlate with specific patient clusters. While cluster 1 parasites, representing a starvation response, show up-regulation of organellar chaperones, cluster 2 parasites, which resemble active growth based on glycolysis, show up-regulation of cytoplasmic chaperones. Interestingly, cytoplasmic Hsp90 and its co-chaperones, previously implicated as drug targets in malaria, cluster in the same group. Detailed analysis of chaperone expression in the patient cluster 2 reveals up-regulation of the entire Hsp90-dependent pro-survival circuitries. In addition, cluster 2 also shows up-regulation of Plasmodium export element (PEXEL)-containing Hsp40s thought to have regulatory and host remodeling roles in the infected erythrocyte. Conclusion In all, this study demonstrates an intimate involvement of parasite-encoded chaperones, PfHsp90 in particular, in defining pathogenesis of malaria. PMID:20719001

Mapping cellular Fe-S cluster uptake and exchange reactions - divergent pathways for iron-sulfur cluster delivery to human ferredoxins.

PubMed

Fidai, Insiya; Wachnowsky, Christine; Cowan, J A

2016-12-07

Ferredoxins are protein mediators of biological electron-transfer reactions and typically contain either [2Fe-2S] or [4Fe-4S] clusters. Two ferredoxin homologues have been identified in the human genome, Fdx1 and Fdx2, that share 43% identity and 69% similarity in protein sequence and both bind [2Fe-2S] clusters. Despite the high similarity, the two ferredoxins play very specific roles in distinct physiological pathways and cannot replace each other in function. Both eukaryotic and prokaryotic ferredoxins and homologues have been reported to receive their Fe-S cluster from scaffold/delivery proteins such as IscU, Isa, glutaredoxins, and Nfu. However, the preferred and physiologically relevant pathway for receiving the [2Fe-2S] cluster by ferredoxins is subject to speculation and is not clearly identified. In this work, we report on in vitro UV-visible (UV-vis) circular dichroism studies of [2Fe-2S] cluster transfer to the ferredoxins from a variety of partners. The results reveal rapid and quantitative transfer to both ferredoxins from several donor proteins (IscU, Isa1, Grx2, and Grx3). Transfer from Isa1 to Fdx2 was also observed to be faster than that of IscU to Fdx2, suggesting that Fdx2 could receive its cluster from Isa1 instead of IscU. Several other transfer combinations were also investigated and the results suggest a complex, but kinetically detailed map for cellular cluster trafficking. This is the first step toward building a network map for all of the possible iron-sulfur cluster transfer pathways in the mitochondria and cytosol, providing insights on the most likely cellular pathways and possible redundancies in these pathways.

Identifying driving gene clusters in complex diseases through critical transition theory

NASA Astrophysics Data System (ADS)

Wolanyk, Nathaniel; Wang, Xujing; Hessner, Martin; Gao, Shouguo; Chen, Ye; Jia, Shuang

A novel approach of looking at the human body using critical transition theory has yielded positive results: clusters of genes that act in tandem to drive complex disease progression. This cluster of genes can be thought of as the first part of a large genetic force that pushes the body from a curable, but sick, point to an incurable diseased point through a catastrophic bifurcation. The data analyzed is time course microarray blood assay data of 7 high risk individuals for Type 1 Diabetes who progressed into a clinical onset, with an additional larger study requested to be presented at the conference. The normalized data is 25,000 genes strong, which were narrowed down based on statistical metrics, and finally a machine learning algorithm using critical transition metrics found the driving network. This approach was created to be repeatable across multiple complex diseases with only progression time course data needed so that it would be applicable to identifying when an individual is at risk of developing a complex disease. Thusly, preventative measures can be enacted, and in the longer term, offers a possible solution to prevent all Type 1 Diabetes.

Distinct ADHD Symptom Clusters Differentially Associated with Personality Traits

ERIC Educational Resources Information Center

McKinney, Ashley A.; Canu, Will H.; Schneider, H. G.

2013-01-01

Objective: ADHD has been linked to various constructs, yet there is a lack of focus on how its symptom clusters differentially associate with personality, which this study addresses. Method: The current study examines the relationship between impulsive and inattentive ADHD traits and personality, indexed by the Revised NEO Personality Inventory…

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.

A catalogue of clusters of galaxies identified from all sky surveys of 2MASS, WISE, and SuperCOSMOS

NASA Astrophysics Data System (ADS)

Wen, Z. L.; Han, J. L.; Yang, F.

2018-03-01

We identify 47 600 clusters of galaxies from photometric data of Two Micron All Sky Survey (2MASS), Wide-field Infrared Survey Explorer (WISE), and SuperCOSMOS, among which 26 125 clusters are recognized for the first time and mostly in the sky outside the Sloan Digital Sky Survey (SDSS) area. About 90 per cent of massive clusters of M500 > 3 × 1014 M⊙ in the redshift range of 0.025 < z < 0.3 have been detected from such survey data, and the detection rate drops down to 50 per cent for clusters with a mass of M500 ˜ 1 × 1014 M⊙. Monte Carlo simulations show that the false detection rate for the whole cluster sample is less than 5 per cent. By cross-matching with ROSAT and XMM-Newton sources, we get 779 new X-ray cluster candidates which have X-ray counterparts within a projected offset of 0.2 Mpc.

Using exploratory data analysis to identify and predict patterns of human Lyme disease case clustering within a multistate region, 2010-2014.

PubMed

Hendricks, Brian; Mark-Carew, Miguella

2017-02-01

Lyme disease is the most commonly reported vectorborne disease in the United States. The objective of our study was to identify patterns of Lyme disease reporting after multistate inclusion to mitigate potential border effects. County-level human Lyme disease surveillance data were obtained from Kentucky, Maryland, Ohio, Pennsylvania, Virginia, and West Virginia state health departments. Rate smoothing and Local Moran's I was performed to identify clusters of reporting activity and identify spatial outliers. A logistic generalized estimating equation was performed to identify significant associations in disease clustering over time. Resulting analyses identified statistically significant (P=0.05) clusters of high reporting activity and trends over time. High reporting activity aggregated near border counties in high incidence states, while low reporting aggregated near shared county borders in non-high incidence states. Findings highlight the need for exploratory surveillance approaches to describe the extent to which state level reporting affects accurate estimation of Lyme disease progression. Copyright © 2017 Elsevier Ltd. All rights reserved.

Siriusly, a newly identified intermediate-age Milky Way stellar cluster: a spectroscopic study of Gaia 1

NASA Astrophysics Data System (ADS)

Simpson, J. D.; De Silva, G. M.; Martell, S. L.; Zucker, D. B.; Ferguson, A. M. N.; Bernard, E. J.; Irwin, M.; Penarrubia, J.; Tolstoy, E.

2017-11-01

We confirm the reality of the recently discovered Milky Way stellar cluster Gaia 1 using spectra acquired with the HERMES and AAOmega spectrographs of the Anglo-Australian Telescope. This cluster had been previously undiscovered due to its close angular proximity to Sirius, the brightest star in the sky at visual wavelengths. Our observations identified 41 cluster members, and yielded an overall metallicity of [{Fe}/{H}]=-0.13± 0.13 and barycentric radial velocity of vr = 58.30 ± 0.22 km s-1. These kinematics provide a dynamical mass estimate of 12.9^{+4.6}_{-3.9}× 10^3 M_{⊙}. Isochrone fits to Gaia, 2MASS, and Pan-STARRS1 photometry indicate that Gaia 1 is an intermediate age (˜3 Gyr) stellar cluster. Combining the spatial and kinematic data we calculate Gaia 1 has a circular orbit with a radius of about 12 kpc, but with a large out of plane motion: z_{max}=1.1^{+0.4}_{-0.3} kpc. Clusters with such orbits are unlikely to survive long due to the number of plane passages they would experience.

Coping profiles, perceived stress and health-related behaviors: a cluster analysis approach.

PubMed

Doron, Julie; Trouillet, Raphael; Maneveau, Anaïs; Ninot, Grégory; Neveu, Dorine

2015-03-01

Using cluster analytical procedure, this study aimed (i) to determine whether people could be differentiated on the basis of coping profiles (or unique combinations of coping strategies); and (ii) to examine the relationships between these profiles and perceived stress and health-related behaviors. A sample of 578 French students (345 females, 233 males; M(age)= 21.78, SD(age)= 2.21) completed the Perceived Stress Scale-14 ( Bruchon-Schweitzer, 2002), the Brief COPE ( Muller and Spitz, 2003) and a series of items measuring health-related behaviors. A two-phased cluster analytic procedure (i.e. hierarchical and non-hierarchical-k-means) was employed to derive clusters of coping strategy profiles. The results yielded four distinctive coping profiles: High Copers, Adaptive Copers, Avoidant Copers and Low Copers. The results showed that clusters differed significantly in perceived stress and health-related behaviors. High Copers and Avoidant Copers displayed higher levels of perceived stress and engaged more in unhealthy behavior, compared with Adaptive Copers and Low Copers who reported lower levels of stress and engaged more in healthy behaviors. These findings suggested that individuals' relative reliance on some strategies and de-emphasis on others may be a more advantageous way of understanding the manner in which individuals cope with stress. Therefore, cluster analysis approach may provide an advantage over more traditional statistical techniques by identifying distinct coping profiles that might best benefit from interventions. Future research should consider coping profiles to provide a deeper understanding of the relationships between coping strategies and health outcomes and to identify risk groups. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The spatial clustering of obesity: does the built environment matter?

PubMed

Huang, R; Moudon, A V; Cook, A J; Drewnowski, A

2015-12-01

Obesity rates in the USA show distinct geographical patterns. The present study used spatial cluster detection methods and individual-level data to locate obesity clusters and to analyse them in relation to the neighbourhood built environment. The 2008-2009 Seattle Obesity Study provided data on the self-reported height, weight, and sociodemographic characteristics of 1602 King County adults. Home addresses were geocoded. Clusters of high or low body mass index were identified using Anselin's Local Moran's I and a spatial scan statistic with regression models that searched for unmeasured neighbourhood-level factors from residuals, adjusting for measured individual-level covariates. Spatially continuous values of objectively measured features of the local neighbourhood built environment (SmartMaps) were constructed for seven variables obtained from tax rolls and commercial databases. Both the Local Moran's I and a spatial scan statistic identified similar spatial concentrations of obesity. High and low obesity clusters were attenuated after adjusting for age, gender, race, education and income, and they disappeared once neighbourhood residential property values and residential density were included in the model. Using individual-level data to detect obesity clusters with two cluster detection methods, the present study showed that the spatial concentration of obesity was wholly explained by neighbourhood composition and socioeconomic characteristics. These characteristics may serve to more precisely locate obesity prevention and intervention programmes. © 2014 The British Dietetic Association Ltd.

Recursive expectation-maximization clustering: A method for identifying buffering mechanisms composed of phenomic modules

NASA Astrophysics Data System (ADS)

Guo, Jingyu; Tian, Dehua; McKinney, Brett A.; Hartman, John L.

2010-06-01

of physiological homeostasis. To develop the method, 297 gene deletion strains were selected based on gene-drug interactions with hydroxyurea, an inhibitor of ribonucleotide reductase enzyme activity, which is critical for DNA synthesis. To partition the gene functions, these 297 deletion strains were challenged with growth inhibitory drugs known to target different genes and cellular pathways. Q-HTCP-derived growth curves were used to quantify all gene interactions, and the data were used to test the performance of REMc. Fundamental advantages of REMc include objective assessment of total number of clusters and assignment to each cluster a log-likelihood value, which can be considered an indicator of statistical quality of clusters. To assess the biological quality of clusters, we developed a method called gene ontology information divergence z-score (GOid_z). GOid_z summarizes total enrichment of GO attributes within individual clusters. Using these and other criteria, we compared the performance of REMc to hierarchical and K-means clustering. The main conclusion is that REMc provides distinct efficiencies for mining Q-HTCP data. It facilitates identification of phenomic modules, which contribute to buffering mechanisms that underlie cellular homeostasis and the regulation of phenotypic expression.

A Cluster Analytic Approach to Identifying Predictors and Moderators of Psychosocial Treatment for Bipolar Depression: Results from STEP-BD

PubMed Central

Deckersbach, Thilo; Peters, Amy T.; Sylvia, Louisa G.; Gold, Alexandra K.; da Silva Magalhaes, Pedro Vieira; Henry, David B.; Frank, Ellen; Otto, Michael W.; Berk, Michael; Dougherty, Darin D.; Nierenberg, Andrew A.; Miklowitz, David J.

2016-01-01

Background We sought to address how predictors and moderators of psychotherapy for bipolar depression – identified individually in prior analyses – can inform the development of a metric for prospectively classifying treatment outcome in intensive psychotherapy (IP) versus collaborative care (CC) adjunctive to pharmacotherapy in the Systematic Treatment Enhancement Program (STEP-BD) study. Methods We conducted post-hoc analyses on 135 STEP-BD participants using cluster analysis to identify subsets of participants with similar clinical profiles and investigated this combined metric as a moderator and predictor of response to IP. We used agglomerative hierarchical cluster analyses and k-means clustering to determine the content of the clinical profiles. Logistic regression and Cox proportional hazard models were used to evaluate whether the resulting clusters predicted or moderated likelihood of recovery or time until recovery. Results The cluster analysis yielded a two-cluster solution: 1) “less-recurrent/severe” and 2) “chronic/recurrent.” Rates of recovery in IP were similar for less-recurrent/severe and chronic/recurrent participants. Less-recurrent/severe patients were more likely than chronic/recurrent patients to achieve recovery in CC (p = .040, OR = 4.56). IP yielded a faster recovery for chronic/recurrent participants, whereas CC led to recovery sooner in the less-recurrent/severe cluster (p = .034, OR = 2.62). Limitations Cluster analyses require list-wise deletion of cases with missing data so we were unable to conduct analyses on all STEP-BD participants. Conclusions A well-powered, parametric approach can distinguish patients based on illness history and provide clinicians with symptom profiles of patients that confer differential prognosis in CC vs. IP. PMID:27289316

Identifying eating behavior phenotypes and their correlates: a novel direction toward improving weight management interventions

PubMed Central

Bouhlal, Sofia; McBride, Colleen M.; Trivedi, Niraj S.; Agurs-Collins, Tanya; Persky, Susan

2017-01-01

Common reports of over-response to food cues, difficulties with calorie restriction, and difficulty adhering to dietary guidelines suggest that eating behaviors could be interrelated in ways that influence weight management efforts. The feasibility of identifying robust eating phenotypes (showing face, content, and criterion validity) was explored based on well-validated individual eating behavior assessments. Adults (n=260; mean age 34 years) completed online questionnaires with measurements of nine eating behaviors including: appetite for palatable foods, binge eating, bitter taste sensitivity, disinhibition, food neophobia, pickiness and satiety responsiveness. Discovery-based visualization procedures that have the combined strengths of heatmaps and hierarchical clustering were used to investigate: 1) how eating behaviors cluster, 2) how participants can be grouped within eating behavior clusters, and 3) whether group clustering is associated with body mass index (BMI) and dietary self-efficacy levels. Two distinct eating behavior clusters and participant groups that aligned within these clusters were identified: one with higher drive to eat and another with food avoidance behaviors. Participants’ BMI (p=.0002) and dietary self-efficacy (p<.0001) were associated with cluster membership. Eating behavior clusters showed content and criterion validity based on their association with BMI (associated, but not entirely overlapping) and dietary self-efficacy. Identifying eating behavior phenotypes appears viable. These efforts could be expanded and ultimately inform tailored weight management interventions. PMID:28043857

Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles.

PubMed

Farshidfar, Farshad; Zheng, Siyuan; Gingras, Marie-Claude; Newton, Yulia; Shih, Juliann; Robertson, A Gordon; Hinoue, Toshinori; Hoadley, Katherine A; Gibb, Ewan A; Roszik, Jason; Covington, Kyle R; Wu, Chia-Chin; Shinbrot, Eve; Stransky, Nicolas; Hegde, Apurva; Yang, Ju Dong; Reznik, Ed; Sadeghi, Sara; Pedamallu, Chandra Sekhar; Ojesina, Akinyemi I; Hess, Julian M; Auman, J Todd; Rhie, Suhn K; Bowlby, Reanne; Borad, Mitesh J; Zhu, Andrew X; Stuart, Josh M; Sander, Chris; Akbani, Rehan; Cherniack, Andrew D; Deshpande, Vikram; Mounajjed, Taofic; Foo, Wai Chin; Torbenson, Michael S; Kleiner, David E; Laird, Peter W; Wheeler, David A; McRee, Autumn J; Bathe, Oliver F; Andersen, Jesper B; Bardeesy, Nabeel; Roberts, Lewis R; Kwong, Lawrence N

2017-03-14

Cholangiocarcinoma (CCA) is an aggressive malignancy of the bile ducts, with poor prognosis and limited treatment options. Here, we describe the integrated analysis of somatic mutations, RNA expression, copy number, and DNA methylation by The Cancer Genome Atlas of a set of predominantly intrahepatic CCA cases and propose a molecular classification scheme. We identified an IDH mutant-enriched subtype with distinct molecular features including low expression of chromatin modifiers, elevated expression of mitochondrial genes, and increased mitochondrial DNA copy number. Leveraging the multi-platform data, we observed that ARID1A exhibited DNA hypermethylation and decreased expression in the IDH mutant subtype. More broadly, we found that IDH mutations are associated with an expanded histological spectrum of liver tumors with molecular features that stratify with CCA. Our studies reveal insights into the molecular pathogenesis and heterogeneity of cholangiocarcinoma and provide classification information of potential therapeutic significance. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

The Distinctive Difficulties of Disagreeable Youth

ERIC Educational Resources Information Center

Laursen, Brett; Hafen, Christopher A.; Rubin, Kenneth H.; Booth-LaForce, Cathryn; Rose-Krasnor, Linda

2010-01-01

This study examines whether disagreeable youth are distinct from aggressive youth, victimized youth, and withdrawn youth. Young adolescents (120 girls and 104 boys, M = 13.59 years old) completed personality and adjustment inventories. Aggression, withdrawal, and victimization scores were derived from peer nominations (N = 807). Cluster analyses…

Strand swapping regulates the iron-sulfur cluster in the diabetes drug target mitoNEET

PubMed Central

Baxter, Elizabeth Leigh; Jennings, Patricia A.; Onuchic, José N.

2012-01-01

MitoNEET is a recently identified diabetes drug target that coordinates a transferable 2Fe-2S cluster, and additionally contains an unusual strand swap. In this manuscript, we use a dual basin structure-based model to predict and characterize the folding and functionality of strand swapping in mitoNEET. We demonstrate that a strand unswapped conformation is kinetically accessible and that multiple levels of control are employed to regulate the conformational dynamics of the system. Environmental factors such as temperature can shift route preference toward the unswapped pathway. Additionally we see that a region recently identified as contributing to frustration in folding acts as a regulatory hinge loop that modulates conformational balance. Interestingly, strand unswapping transfers strain specifically to cluster-coordinating residues, opening the cluster-coordinating pocket. Strengthening contacts within the cluster-coordinating pocket opens a new pathway between the swapped and unswapped conformation that utilizes cracking to bypass the unfolded basin. These results suggest that local control within distinct regions affect motions important in regulating mitoNEET’s 2Fe-2S clusters. PMID:22308404

IMP-27, a Unique Metallo-β-Lactamase Identified in Geographically Distinct Isolates of Proteus mirabilis.

PubMed

Dixon, Nyssa; Fowler, Randal C; Yoshizumi, A; Horiyama, Tsukasa; Ishii, Y; Harrison, Lucas; Geyer, Chelsie N; Moland, Ellen Smith; Thomson, Kenneth; Hanson, Nancy D

2016-10-01

A novel metallo-β-lactamase gene, blaIMP-27, was identified in unrelated Proteus mirabilis isolates from two geographically distinct locations in the United States. Both isolates harbor blaIMP-27 as part of the first gene cassette in a class 2 integron. Antimicrobial susceptibility testing indicated susceptibility to aztreonam, piperacillin-tazobactam, and ceftazidime but resistance to ertapenem. However, hydrolysis assays indicated that ceftazidime was a substrate for IMP-27. Copyright © 2016 Dixon et al.

Closed-cage tungsten oxide clusters in the gas phase.

PubMed

Singh, D M David Jeba; Pradeep, T; Thirumoorthy, Krishnan; Balasubramanian, Krishnan

2010-05-06

During the course of a study on the clustering of W-Se and W-S mixtures in the gas phase using laser desorption ionization (LDI) mass spectrometry, we observed several anionic W-O clusters. Three distinct species, W(6)O(19)(-), W(13)O(29)(-), and W(14)O(32)(-), stand out as intense peaks in the regular mass spectral pattern of tungsten oxide clusters suggesting unusual stabilities for them. Moreover, these clusters do not fragment in the postsource decay analysis. While trying to understand the precursor material, which produced these clusters, we found the presence of nanoscale forms of tungsten oxide. The structure and thermodynamic parameters of tungsten clusters have been explored using relativistic quantum chemical methods. Our computed results of atomization energy are consistent with the observed LDI mass spectra. The computational results suggest that the clusters observed have closed-cage structure. These distinct W(13) and W(14) clusters were observed for the first time in the gas phase.

Vector dissimilarity and clustering.

PubMed

Lefkovitch, L P

1991-04-01

Based on the description of objects by m attributes, an m-element vector dissimilarity function is defined that, unlike scalar functions, retains the distinction among attributes. This function, which satisfies the conditions for a metric, allows the definition of betweenness, which can then be used for clustering. Applications to the subset-generation phase of conditional clustering and to nearest-neighbor-type algorithms are described.

Online discourse on fibromyalgia: text-mining to identify clinical distinction and patient concerns.

PubMed

Park, Jungsik; Ryu, Young Uk

2014-10-07

The purpose of this study was to evaluate the possibility of using text-mining to identify clinical distinctions and patient concerns in online memoires posted by patients with fibromyalgia (FM). A total of 399 memoirs were collected from an FM group website. The unstructured data of memoirs associated with FM were collected through a crawling process and converted into structured data with a concordance, parts of speech tagging, and word frequency. We also conducted a lexical analysis and phrase pattern identification. After examining the data, a set of FM-related keywords were obtained and phrase net relationships were set through a web-based visualization tool. The clinical distinction of FM was verified. Pain is the biggest issue to the FM patients. The pains were affecting body parts including 'muscles,' 'leg,' 'neck,' 'back,' 'joints,' and 'shoulders' with accompanying symptoms such as 'spasms,' 'stiffness,' and 'aching,' and were described as 'sever,' 'chronic,' and 'constant.' This study also demonstrated that it was possible to understand the interests and concerns of FM patients through text-mining. FM patients wanted to escape from the pain and symptoms, so they were interested in medical treatment and help. Also, they seemed to have interest in their work and occupation, and hope to continue to live life through the relationships with the people around them. This research shows the potential for extracting keywords to confirm the clinical distinction of a certain disease, and text-mining can help objectively understand the concerns of patients by generalizing their large number of subjective illness experiences. However, it is believed that there are limitations to the processes and methods for organizing and classifying large amounts of text, so these limits have to be considered when analyzing the results. The development of research methodology to overcome these limitations is greatly needed.

Hierarchical cluster analysis of technical replicates to identify interferents in untargeted mass spectrometry metabolomics.

PubMed

Caesar, Lindsay K; Kvalheim, Olav M; Cech, Nadja B

2018-08-27

Mass spectral data sets often contain experimental artefacts, and data filtering prior to statistical analysis is crucial to extract reliable information. This is particularly true in untargeted metabolomics analyses, where the analyte(s) of interest are not known a priori. It is often assumed that chemical interferents (i.e. solvent contaminants such as plasticizers) are consistent across samples, and can be removed by background subtraction from blank injections. On the contrary, it is shown here that chemical contaminants may vary in abundance across each injection, potentially leading to their misidentification as relevant sample components. With this metabolomics study, we demonstrate the effectiveness of hierarchical cluster analysis (HCA) of replicate injections (technical replicates) as a methodology to identify chemical interferents and reduce their contaminating contribution to metabolomics models. Pools of metabolites with varying complexity were prepared from the botanical Angelica keiskei Koidzumi and spiked with known metabolites. Each set of pools was analyzed in triplicate and at multiple concentrations using ultraperformance liquid chromatography coupled to mass spectrometry (UPLC-MS). Before filtering, HCA failed to cluster replicates in the data sets. To identify contaminant peaks, we developed a filtering process that evaluated the relative peak area variance of each variable within triplicate injections. These interferent peaks were found across all samples, but did not show consistent peak area from injection to injection, even when evaluating the same chemical sample. This filtering process identified 128 ions that appear to originate from the UPLC-MS system. Data sets collected for a high number of pools with comparatively simple chemical composition were highly influenced by these chemical interferents, as were samples that were analyzed at a low concentration. When chemical interferent masses were removed, technical replicates clustered in

Kinematic gait patterns in healthy runners: A hierarchical cluster analysis.

PubMed

Phinyomark, Angkoon; Osis, Sean; Hettinga, Blayne A; Ferber, Reed

2015-11-05

Previous studies have demonstrated distinct clusters of gait patterns in both healthy and pathological groups, suggesting that different movement strategies may be represented. However, these studies have used discrete time point variables and usually focused on only one specific joint and plane of motion. Therefore, the first purpose of this study was to determine if running gait patterns for healthy subjects could be classified into homogeneous subgroups using three-dimensional kinematic data from the ankle, knee, and hip joints. The second purpose was to identify differences in joint kinematics between these groups. The third purpose was to investigate the practical implications of clustering healthy subjects by comparing these kinematics with runners experiencing patellofemoral pain (PFP). A principal component analysis (PCA) was used to reduce the dimensionality of the entire gait waveform data and then a hierarchical cluster analysis (HCA) determined group sets of similar gait patterns and homogeneous clusters. The results show two distinct running gait patterns were found with the main between-group differences occurring in frontal and sagittal plane knee angles (P<0.001), independent of age, height, weight, and running speed. When these two groups were compared to PFP runners, one cluster exhibited greater while the other exhibited reduced peak knee abduction angles (P<0.05). The variability observed in running patterns across this sample could be the result of different gait strategies. These results suggest care must be taken when selecting samples of subjects in order to investigate the pathomechanics of injured runners. Copyright © 2015 Elsevier Ltd. All rights reserved.

Characterizing Suicide in Toronto: An Observational Study and Cluster Analysis

PubMed Central

Sinyor, Mark; Schaffer, Ayal; Streiner, David L

2014-01-01

Objective: To determine whether people who have died from suicide in a large epidemiologic sample form clusters based on demographic, clinical, and psychosocial factors. Method: We conducted a coroner’s chart review for 2886 people who died in Toronto, Ontario, from 1998 to 2010, and whose death was ruled as suicide by the Office of the Chief Coroner of Ontario. A cluster analysis using known suicide risk factors was performed to determine whether suicide deaths separate into distinct groups. Clusters were compared according to person- and suicide-specific factors. Results: Five clusters emerged. Cluster 1 had the highest proportion of females and nonviolent methods, and all had depression and a past suicide attempt. Cluster 2 had the highest proportion of people with a recent stressor and violent suicide methods, and all were married. Cluster 3 had mostly males between the ages of 20 and 64, and all had either experienced recent stressors, suffered from mental illness, or had a history of substance abuse. Cluster 4 had the youngest people and the highest proportion of deaths by jumping from height, few were married, and nearly one-half had bipolar disorder or schizophrenia. Cluster 5 had all unmarried people with no prior suicide attempts, and were the least likely to have an identified mental illness and most likely to leave a suicide note. Conclusions: People who die from suicide assort into different patterns of demographic, clinical, and death-specific characteristics. Identifying and studying subgroups of suicides may advance our understanding of the heterogeneous nature of suicide and help to inform development of more targeted suicide prevention strategies. PMID:24444321

Distant Cluster Hunting. II; A Comparison of X-Ray and Optical Cluster Detection Techniques and Catalogs from the ROSAT Optical X-Ray Survey

NASA Technical Reports Server (NTRS)

Donahue, Megan; Scharf, Caleb A.; Mack, Jennifer; Lee, Y. Paul; Postman, Marc; Rosait, Piero; Dickinson, Mark; Voit, G. Mark; Stocke, John T.

2002-01-01

We present and analyze the optical and X-ray catalogs of moderate-redshift cluster candidates from the ROSA TOptical X-Ray Survey, or ROXS. The survey covers the sky area contained in the fields of view of 23 deep archival ROSA T PSPC pointings, 4.8 square degrees. The cross-correlated cluster catalogs were con- structed by comparing two independent catalogs extracted from the optical and X-ray bandpasses, using a matched-filter technique for the optical data and a wavelet technique for the X-ray data. We cross-identified cluster candidates in each catalog. As reported in Paper 1, the matched-filter technique found optical counter- parts for at least 60% (26 out of 43) of the X-ray cluster candidates; the estimated redshifts from the matched filter algorithm agree with at least 7 of 1 1 spectroscopic confirmations (Az 5 0.10). The matched filter technique. with an imaging sensitivity of ml N 23, identified approximately 3 times the number of candidates (155 candidates, 142 with a detection confidence >3 u) found in the X-ray survey of nearly the same area. There are 57 X-ray candidates, 43 of which are unobscured by scattered light or bright stars in the optical images. Twenty-six of these have fairly secure optical counterparts. We find that the matched filter algorithm, when applied to images with galaxy flux sensitivities of mI N 23, is fairly well-matched to discovering z 5 1 clusters detected by wavelets in ROSAT PSPC exposures of 8000-60,000 s. The difference in the spurious fractions between the optical and X-ray (30%) and IO%, respectively) cannot account for the difference in source number. In Paper I, we compared the optical and X-ray cluster luminosity functions and we found that the luminosity functions are consistent if the relationship between X-ray and optical luminosities is steep (Lx o( L&f). Here, in Paper 11, we present the cluster catalogs and a numerical simulation of the ROXS. We also present color-magnitude plots for several of the cluster

A Measurement of CMB Cluster Lensing with SPT and DES Year 1 Data

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

Baxter, E.J.; et al.

2017-08-03

Clusters of galaxies gravitationally lens the cosmic microwave background (CMB) radiation, resulting in a distinct imprint in the CMB on arcminute scales. Measurement of this effect offers a promising way to constrain the masses of galaxy clusters, particularly those at high redshift. We use CMB maps from the South Pole Telescope Sunyaev-Zel'dovich (SZ) survey to measure the CMB lensing signal around galaxy clusters identified in optical imaging from first year observations of the Dark Energy Survey. We detect lensing of the CMB by the galaxy clusters at 6.5more » $$\\sigma$$ significance. Using the measured lensing signal, we constrain the amplitude of the relation between cluster mass and optical richness to roughly $$20\\%$$ precision, finding good agreement with recent constraints obtained with galaxy lensing. The error budget is dominated by statistical noise but includes significant contributions from systematic biases due to the thermal SZ effect and cluster miscentering.« less

Genome sequence of a distinct watermelon mosaic virus identified from ginseng (Panax ginseng) transcriptome.

PubMed

Park, D; Kim, H; Hahn, Y

Watermelon mosaic virus (WMV) is a member of the genus Potyvirus, which is the largest genus of plant viruses. WMV is a significant pathogen of crop plants, including Cucurbitaceae species. A WMV strain, designated as WMV-Pg, was identified in transcriptome data collected from ginseng (Panax ginseng) root. WMV-Pg showed 84% nucleotide sequence identity and 91% amino acid sequence identity with its closest related virus, WMV-Fr. A phylogenetic analysis of WMV-Pg with other WMVs and soybean mosaic viruses (SMVs) indicated that WMV-Pg is a distinct subtype of the WMV/SMV group of the genus Potyvirus in the family Potyviridae.

Method of identifying clusters representing statistical dependencies in multivariate data

NASA Technical Reports Server (NTRS)

Borucki, W. J.; Card, D. H.; Lyle, G. C.

1975-01-01

Approach is first to cluster and then to compute spatial boundaries for resulting clusters. Next step is to compute, from set of Monte Carlo samples obtained from scrambled data, estimates of probabilities of obtaining at least as many points within boundaries as were actually observed in original data.

Links between patterns of racial socialization and discrimination experiences and psychological adjustment: a cluster analysis.

PubMed

Ajayi, Alex A; Syed, Moin

2014-10-01

This study used a person-oriented analytic approach to identify meaningful patterns of barriers-focused racial socialization and perceived racial discrimination experiences in a sample of 295 late adolescents. Using cluster analysis, three distinct groups were identified: Low Barrier Socialization-Low Discrimination, High Barrier Socialization-Low Discrimination, and High Barrier Socialization-High Discrimination clusters. These groups were substantively unique in terms of the frequency of racial socialization messages about bias preparation and out-group mistrust its members received and their actual perceived discrimination experiences. Further, individuals in the High Barrier Socialization-High Discrimination cluster reported significantly higher depressive symptoms than those in the Low Barrier Socialization-Low Discrimination and High Barrier Socialization-Low Discrimination clusters. However, no differences in adjustment were observed between the Low Barrier Socialization-Low Discrimination and High Barrier Socialization-Low Discrimination clusters. Overall, the findings highlight important individual differences in how young people of color experience their race and how these differences have significant implications on psychological adjustment. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Epidemiological study of phylogenetic transmission clusters in a local HIV-1 epidemic reveals distinct differences between subtype B and non-B infections.

PubMed

Chalmet, Kristen; Staelens, Delfien; Blot, Stijn; Dinakis, Sylvie; Pelgrom, Jolanda; Plum, Jean; Vogelaers, Dirk; Vandekerckhove, Linos; Verhofstede, Chris

2010-09-07

The number of HIV-1 infected individuals in the Western world continues to rise. More in-depth understanding of regional HIV-1 epidemics is necessary for the optimal design and adequate use of future prevention strategies. The use of a combination of phylogenetic analysis of HIV sequences, with data on patients' demographics, infection route, clinical information and laboratory results, will allow a better characterization of individuals responsible for local transmission. Baseline HIV-1 pol sequences, obtained through routine drug-resistance testing, from 506 patients, newly diagnosed between 2001 and 2009, were used to construct phylogenetic trees and identify transmission-clusters. Patients' demographics, laboratory and clinical data, were retrieved anonymously. Statistical analysis was performed to identify subtype-specific and transmission-cluster-specific characteristics. Multivariate analysis showed significant differences between the 59.7% of individuals with subtype B infection and the 40.3% non-B infected individuals, with regard to route of transmission, origin, infection with Chlamydia (p = 0.01) and infection with Hepatitis C virus (p = 0.017). More and larger transmission-clusters were identified among the subtype B infections (p < 0.001). Overall, in multivariate analysis, clustering was significantly associated with Caucasian origin, infection through homosexual contact and younger age (all p < 0.001). Bivariate analysis additionally showed a correlation between clustering and syphilis (p < 0.001), higher CD4 counts (p = 0.002), Chlamydia infection (p = 0.013) and primary HIV (p = 0.017). Combination of phylogenetics with demographic information, laboratory and clinical data, revealed that HIV-1 subtype B infected Caucasian men-who-have-sex-with-men with high prevalence of sexually transmitted diseases, account for the majority of local HIV-transmissions. This finding elucidates observed epidemiological trends through molecular analysis, and

Fibers in the NGC 1333 proto-cluster

NASA Astrophysics Data System (ADS)

Hacar, A.; Tafalla, M.; Alves, J.

2017-10-01

Are the initial conditions for clustered star formation the same as for non-clustered star formation? To investigate the initial gas properties in young proto-clusters we carried out a comprehensive and high-sensitivity study of the internal structure, density, temperature, and kinematics of the dense gas content of the NGC 1333 region in Perseus, one of the nearest and best studied embedded clusters. The analysis of the gas velocities in the position-position-velocity space reveals an intricate underlying gas organization both in space and velocity. We identified a total of 14 velocity-coherent, (tran-)sonic structures within NGC 1333, with similar physical and kinematic properties than those quiescent, star-forming (aka fertile) fibers previously identified in low-mass star-forming clouds. These fibers are arranged in a complex spatial network, build-up the observed total column density, and contain the dense cores and protostars in this cloud. Our results demonstrate that the presence of fibers is not restricted to low-mass clouds but can be extended to regions of increasing mass and complexity. We propose that the observational dichotomy between clustered and non-clustered star-forming regions might be naturally explained by the distinct spatial density of fertile fibers in these environments. Based on observations carried out under project number 169-11 with the IRAM 30 m Telescope. IRAM is supported by INSU/CNRS (France), MPG (Germany) and IGN (Spain).Based on observations with the 100-m telescope of the MPIfR (Max-Planck-Institut für Radioastronomie) at Effelsberg.Molecular line observations (spectral cubes) are 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/606/A123

Identifying eating behavior phenotypes and their correlates: A novel direction toward improving weight management interventions.

PubMed

Bouhlal, Sofia; McBride, Colleen M; Trivedi, Niraj S; Agurs-Collins, Tanya; Persky, Susan

2017-04-01

Common reports of over-response to food cues, difficulties with calorie restriction, and difficulty adhering to dietary guidelines suggest that eating behaviors could be interrelated in ways that influence weight management efforts. The feasibility of identifying robust eating phenotypes (showing face, content, and criterion validity) was explored based on well-validated individual eating behavior assessments. Adults (n = 260; mean age 34 years) completed online questionnaires with measurements of nine eating behaviors including: appetite for palatable foods, binge eating, bitter taste sensitivity, disinhibition, food neophobia, pickiness and satiety responsiveness. Discovery-based visualization procedures that have the combined strengths of heatmaps and hierarchical clustering were used to investigate: 1) how eating behaviors cluster, 2) how participants can be grouped within eating behavior clusters, and 3) whether group clustering is associated with body mass index (BMI) and dietary self-efficacy levels. Two distinct eating behavior clusters and participant groups that aligned within these clusters were identified: one with higher drive to eat and another with food avoidance behaviors. Participants' BMI (p = 0.0002) and dietary self-efficacy (p < 0.0001) were associated with cluster membership. Eating behavior clusters showed content and criterion validity based on their association with BMI (associated, but not entirely overlapping) and dietary self-efficacy. Identifying eating behavior phenotypes appears viable. These efforts could be expanded and ultimately inform tailored weight management interventions. Published by Elsevier Ltd.

A remarkably stable TipE gene cluster: evolution of insect Para sodium channel auxiliary subunits

PubMed Central

2011-01-01

Background First identified in fruit flies with temperature-sensitive paralysis phenotypes, the Drosophila melanogaster TipE locus encodes four voltage-gated sodium (NaV) channel auxiliary subunits. This cluster of TipE-like genes on chromosome 3L, and a fifth family member on chromosome 3R, are important for the optional expression and functionality of the Para NaV channel but appear quite distinct from auxiliary subunits in vertebrates. Here, we exploited available arthropod genomic resources to trace the origin of TipE-like genes by mapping their evolutionary histories and examining their genomic architectures. Results We identified a remarkably conserved synteny block of TipE-like orthologues with well-maintained local gene arrangements from 21 insect species. Homologues in the water flea, Daphnia pulex, suggest an ancestral pancrustacean repertoire of four TipE-like genes; a subsequent gene duplication may have generated functional redundancy allowing gene losses in the silk moth and mosquitoes. Intronic nesting of the insect TipE gene cluster probably occurred following the divergence from crustaceans, but in the flour beetle and silk moth genomes the clusters apparently escaped from nesting. Across Pancrustacea, TipE gene family members have experienced intronic nesting, escape from nesting, retrotransposition, translocation, and gene loss events while generally maintaining their local gene neighbourhoods. D. melanogaster TipE-like genes exhibit coordinated spatial and temporal regulation of expression distinct from their host gene but well-correlated with their regulatory target, the Para NaV channel, suggesting that functional constraints may preserve the TipE gene cluster. We identified homology between TipE-like NaV channel regulators and vertebrate Slo-beta auxiliary subunits of big-conductance calcium-activated potassium (BKCa) channels, which suggests that ion channel regulatory partners have evolved distinct lineage-specific characteristics

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

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.

Analysis of the nucleoprotein gene identifies three distinct lineages of viral haemorrhagic septicemia virus (VHSV) within the European marine environment

USGS Publications Warehouse

Snow, M.; Cunningham, C.O.; Melvin, W.T.; Kurath, G.

1999-01-01

A ribonuclease (RNase) protection assay (RPA) has been used to detect nucleotide sequence variation within the nucleoprotein gene of 39 viral haemorrhagic septicaemia virus (VHSV) isolates of European marine origin. The classification of VHSV isolates based on RPA cleavage patterns permitted the identification of ten distinct groups of viruses based on differences at the molecular level. The nucleotide sequence of representatives of each of these groupings was determined and subjected to phylogenetic analysis. This revealed grouping of the European marine isolates of VHSV into three genotypes circulating within distinct geographic areas. A fourth genotype was identified comprising isolates originating from North America. Phylogenetic analyses indicated that VHSV isolates recovered from wild caught fish around the British Isles were genetically related to isolates responsible for losses in farmed turbot. Furthermore, a relationship between naturally occurring marine isolates and VHSV isolates causing mortality among rainbow trout in continental Europe was demonstrated. Analysis of the nucleoprotein gene identifies distinct lineages of viral haemorrhagic septicaemia virus within the European marine environment. Virus Res. 63, 35-44. Available from: 

Stellar Clusters in the NGC 6334 Star-Forming Complex

NASA Astrophysics Data System (ADS)

Feigelson, Eric D.; Martin, Amanda L.; McNeill, Collin J.; Broos, Patrick S.; Garmire, Gordon P.

2009-07-01

The full stellar population of NGC 6334, one of the most spectacular regions of massive star formation in the nearby Galaxy, has not been well sampled in past studies. We analyze here a mosaic of two Chandra X-ray Observatory images of the region using sensitive data analysis methods, giving a list of 1607 faint X-ray sources with arcsecond positions and approximate line-of-sight absorption. About 95% of these are expected to be cluster members, most lower mass pre-main-sequence stars. Extrapolating to low X-ray levels, the total stellar population is estimated to be 20,000-30,000 pre-main-sequence stars. The X-ray sources show a complicated spatial pattern with ~10 distinct star clusters. The heavily obscured clusters are mostly associated with previously known far-infrared sources and radio H II regions. The lightly obscured clusters are mostly newly identified in the X-ray images. Dozens of likely OB stars are found, both in clusters and dispersed throughout the region, suggesting that star formation in the complex has proceeded over millions of years. A number of extraordinarily heavily absorbed X-ray sources are associated with the active regions of star formation.

Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach.

PubMed

Andreatta, Massimo; Lund, Ole; Nielsen, Morten

2013-01-01

Proteins recognizing short peptide fragments play a central role in cellular signaling. As a result of high-throughput technologies, peptide-binding protein specificities can be studied using large peptide libraries at dramatically lower cost and time. Interpretation of such large peptide datasets, however, is a complex task, especially when the data contain multiple receptor binding motifs, and/or the motifs are found at different locations within distinct peptides. The algorithm presented in this article, based on Gibbs sampling, identifies multiple specificities in peptide data by performing two essential tasks simultaneously: alignment and clustering of peptide data. We apply the method to de-convolute binding motifs in a panel of peptide datasets with different degrees of complexity spanning from the simplest case of pre-aligned fixed-length peptides to cases of unaligned peptide datasets of variable length. Example applications described in this article include mixtures of binders to different MHC class I and class II alleles, distinct classes of ligands for SH3 domains and sub-specificities of the HLA-A*02:01 molecule. The Gibbs clustering method is available online as a web server at http://www.cbs.dtu.dk/services/GibbsCluster.

A measurement of CMB cluster lensing with SPT and DES year 1 data

NASA Astrophysics Data System (ADS)

Baxter, E. J.; Raghunathan, S.; Crawford, T. M.; Fosalba, P.; Hou, Z.; Holder, G. P.; Omori, Y.; Patil, S.; Rozo, E.; Abbott, T. M. C.; Annis, J.; Aylor, K.; Benoit-Lévy, A.; Benson, B. A.; Bertin, E.; Bleem, L.; Buckley-Geer, E.; Burke, D. L.; Carlstrom, J.; Carnero Rosell, A.; Carrasco Kind, M.; Carretero, J.; Chang, C. L.; Cho, H.-M.; Crites, A. T.; Crocce, M.; Cunha, C. E.; da Costa, L. N.; D'Andrea, C. B.; Davis, C.; de Haan, T.; Desai, S.; Dietrich, J. P.; Dobbs, M. A.; Dodelson, S.; Doel, P.; Drlica-Wagner, A.; Estrada, J.; Everett, W. B.; Fausti Neto, A.; Flaugher, B.; Frieman, J.; García-Bellido, J.; George, E. M.; Gaztanaga, E.; Giannantonio, T.; Gruen, D.; Gruendl, R. A.; Gschwend, J.; Gutierrez, G.; Halverson, N. W.; Harrington, N. L.; Hartley, W. G.; Holzapfel, W. L.; Honscheid, K.; Hrubes, J. D.; Jain, B.; James, D. J.; Jarvis, M.; Jeltema, T.; Knox, L.; Krause, E.; Kuehn, K.; Kuhlmann, S.; Kuropatkin, N.; Lahav, O.; Lee, A. T.; Leitch, E. M.; Li, T. S.; Lima, M.; Luong-Van, D.; Manzotti, A.; March, M.; Marrone, D. P.; Marshall, J. L.; Martini, P.; McMahon, J. J.; Melchior, P.; Menanteau, F.; Meyer, S. S.; Miller, C. J.; Miquel, R.; Mocanu, L. M.; Mohr, J. J.; Natoli, T.; Nord, B.; Ogando, R. L. C.; Padin, S.; Plazas, A. A.; Pryke, C.; Rapetti, D.; Reichardt, C. L.; Romer, A. K.; Roodman, A.; Ruhl, J. E.; Rykoff, E.; Sako, M.; Sanchez, E.; Sayre, J. T.; Scarpine, V.; Schaffer, K. K.; Schindler, R.; Schubnell, M.; Sevilla-Noarbe, I.; Shirokoff, E.; Smith, M.; Smith, R. C.; Soares-Santos, M.; Sobreira, F.; Staniszewski, Z.; Stark, A.; Story, K.; Suchyta, E.; Tarle, G.; Thomas, D.; Troxel, M. A.; Vanderlinde, K.; Vieira, J. D.; Walker, A. R.; Williamson, R.; Zhang, Y.; Zuntz, J.

2018-05-01

Clusters of galaxies gravitationally lens the cosmic microwave background (CMB) radiation, resulting in a distinct imprint in the CMB on arcminute scales. Measurement of this effect offers a promising way to constrain the masses of galaxy clusters, particularly those at high redshift. We use CMB maps from the South Pole Telescope Sunyaev-Zel'dovich (SZ) survey to measure the CMB lensing signal around galaxy clusters identified in optical imaging from first year observations of the Dark Energy Survey. The cluster catalogue used in this analysis contains 3697 members with mean redshift of \\bar{z} = 0.45. We detect lensing of the CMB by the galaxy clusters at 8.1σ significance. Using the measured lensing signal, we constrain the amplitude of the relation between cluster mass and optical richness to roughly 17 {per cent} precision, finding good agreement with recent constraints obtained with galaxy lensing. The error budget is dominated by statistical noise but includes significant contributions from systematic biases due to the thermal SZ effect and cluster miscentring.

A measurement of CMB cluster lensing with SPT and DES year 1 data

DOE PAGES

Baxter, E. J.; Raghunathan, S.; Crawford, T. M.; ...

2018-02-09

Clusters of galaxies gravitationally lens the cosmic microwave background (CMB) radiation, resulting in a distinct imprint in the CMB on arcminute scales. Measurement of this effect offers a promising way to constrain the masses of galaxy clusters, particularly those at high redshift. We use CMB maps from the South Pole Telescope Sunyaev-Zel'dovich (SZ) survey to measure the CMB lensing signal around galaxy clusters identified in optical imaging from first year observations of the Dark Energy Survey. The cluster catalog used in this analysis contains 3697 members with mean redshift ofmore » $$\\bar{z} = 0.45$$. We detect lensing of the CMB by the galaxy clusters at $$8.1\\sigma$$ significance. Using the measured lensing signal, we constrain the amplitude of the relation between cluster mass and optical richness to roughly $$17\\%$$ precision, finding good agreement with recent constraints obtained with galaxy lensing. The error budget is dominated by statistical noise but includes significant contributions from systematic biases due to the thermal SZ effect and cluster miscentering.« less

A measurement of CMB cluster lensing with SPT and DES year 1 data

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

Baxter, E. J.; Raghunathan, S.; Crawford, T. M.

Clusters of galaxies gravitationally lens the cosmic microwave background (CMB) radiation, resulting in a distinct imprint in the CMB on arcminute scales. Measurement of this effect offers a promising way to constrain the masses of galaxy clusters, particularly those at high redshift. We use CMB maps from the South Pole Telescope Sunyaev-Zel'dovich (SZ) survey to measure the CMB lensing signal around galaxy clusters identified in optical imaging from first year observations of the Dark Energy Survey. The cluster catalog used in this analysis contains 3697 members with mean redshift ofmore » $$\\bar{z} = 0.45$$. We detect lensing of the CMB by the galaxy clusters at $$8.1\\sigma$$ significance. Using the measured lensing signal, we constrain the amplitude of the relation between cluster mass and optical richness to roughly $$17\\%$$ precision, finding good agreement with recent constraints obtained with galaxy lensing. The error budget is dominated by statistical noise but includes significant contributions from systematic biases due to the thermal SZ effect and cluster miscentering.« less

Predicting healthcare outcomes in prematurely born infants using cluster analysis.

PubMed

MacBean, Victoria; Lunt, Alan; Drysdale, Simon B; Yarzi, Muska N; Rafferty, Gerrard F; Greenough, Anne

2018-05-23

Prematurely born infants are at high risk of respiratory morbidity following neonatal unit discharge, though prediction of outcomes is challenging. We have tested the hypothesis that cluster analysis would identify discrete groups of prematurely born infants with differing respiratory outcomes during infancy. A total of 168 infants (median (IQR) gestational age 33 (31-34) weeks) were recruited in the neonatal period from consecutive births in a tertiary neonatal unit. The baseline characteristics of the infants were used to classify them into hierarchical agglomerative clusters. Rates of viral lower respiratory tract infections (LRTIs) were recorded for 151 infants in the first year after birth. Infants could be classified according to birth weight and duration of neonatal invasive mechanical ventilation (MV) into three clusters. Cluster one (MV ≤5 days) had few LRTIs. Clusters two and three (both MV ≥6 days, but BW ≥or <882 g respectively), had significantly higher LRTI rates. Cluster two had a higher proportion of infants experiencing respiratory syncytial virus LRTIs (P = 0.01) and cluster three a higher proportion of rhinovirus LRTIs (P < 0.001) CONCLUSIONS: Readily available clinical data allowed classification of prematurely born infants into one of three distinct groups with differing subsequent respiratory morbidity in infancy. © 2018 Wiley Periodicals, Inc.

Identifying poor metabolic adaptation during early lactation in dairy cows using cluster analysis.

PubMed

Tremblay, M; Kammer, M; Lange, H; Plattner, S; Baumgartner, C; Stegeman, J A; Duda, J; Mansfeld, R; Döpfer, D

2018-05-02

Currently, cows with poor metabolic adaptation during early lactation, or poor metabolic adaptation syndrome (PMAS), are often identified based on detection of hyperketonemia. Unfortunately, elevated blood ketones do not manifest consistently with indications of PMAS. Expected indicators of PMAS include elevated liver enzymes and bilirubin, decreased rumen fill, reduced rumen contractions, and a decrease in milk production. Cows with PMAS typically are higher producing, older cows that are earlier in lactation and have greater body condition score at the start of lactation. It was our aim to evaluate commonly used measures of metabolic health (input variables) that were available [i.e., blood β-hydroxybutyrate acid, milk fat:protein ratio, blood nonesterified fatty acids (NEFA)] to characterize PMAS. Bavarian farms (n = 26) with robotic milking systems were enrolled for weekly visits for an average of 6.7 wk. Physical examinations of the cows (5-50 d in milk) were performed by veterinarians during each visit, and blood and milk samples were collected. Resulting data included 790 observations from 312 cows (309 Simmental, 1 Red Holstein, 2 Holstein). Principal component analysis was conducted on the 3 input variables, followed by K-means cluster analysis of the first 2 orthogonal components. The 5 resulting clusters were then ascribed to low, intermediate, or high PMAS classes based on their degree of agreement with expected PMAS indicators and characteristics in comparison with other clusters. Results revealed that PMAS classes were most significantly associated with blood NEFA levels. Next, we evaluated NEFA values that classify observations into appropriate PMAS classes in this data set, which we called separation values. Our resulting NEFA separation values [<0.39 mmol/L (95% confidence limits = 0.360-0.410) to identify low PMAS observations and ≥0.7 mmol/L (95% confidence limits = 0.650-0.775) to identify high PMAS observations] were similar to values

Microsatellite markers identify three lineages of Phytophthora ramorum in US nurseries, yet single lineages in US forest and European nursery populations.

PubMed

Ivors, K; Garbelotto, M; Vries, I D E; Ruyter-Spira, C; Te Hekkert, B; Rosenzweig, N; Bonants, P

2006-05-01

Analysis of 12 polymorphic simple sequence repeats identified in the genome sequence of Phytophthora ramorum, causal agent of 'sudden oak death', revealed genotypic diversity to be significantly higher in nurseries (91% of total) than in forests (18% of total). Our analysis identified only two closely related genotypes in US forests, while the genetic structure of populations from European nurseries was of intermediate complexity, including multiple, closely related genotypes. Multilocus analysis determined populations in US forests reproduce clonally and are likely descendants of a single introduced individual. The 151 isolates analysed clustered in three clades. US forest and European nursery isolates clustered into two distinct clades, while one isolate from a US nursery belonged to a third novel clade. The combined microsatellite, sequencing and morphological analyses suggest the three clades represent distinct evolutionary lineages. All three clades were identified in some US nurseries, emphasizing the role of commercial plant trade in the movement of this pathogen.

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.

Onto-clust--a methodology for combining clustering analysis and ontological methods for identifying groups of comorbidities for developmental disorders.

PubMed

Peleg, Mor; Asbeh, Nuaman; Kuflik, Tsvi; Schertz, Mitchell

2009-02-01

Children with developmental disorders usually exhibit multiple developmental problems (comorbidities). Hence, such diagnosis needs to revolve on developmental disorder groups. Our objective is to systematically identify developmental disorder groups and represent them in an ontology. We developed a methodology that combines two methods (1) a literature-based ontology that we created, which represents developmental disorders and potential developmental disorder groups, and (2) clustering for detecting comorbid developmental disorders in patient data. The ontology is used to interpret and improve clustering results and the clustering results are used to validate the ontology and suggest directions for its development. We evaluated our methodology by applying it to data of 1175 patients from a child development clinic. We demonstrated that the ontology improves clustering results, bringing them closer to an expert generated gold-standard. We have shown that our methodology successfully combines an ontology with a clustering method to support systematic identification and representation of developmental disorder groups.

Whole-Genome and Epigenomic Landscapes of Etiologically Distinct Subtypes of Cholangiocarcinoma

PubMed Central

Jusakul, Apinya; Cutcutache, Ioana; Yong, Chern Han; Lim, Jing Quan; Huang, Mi Ni; Padmanabhan, Nisha; Nellore, Vishwa; Kongpetch, Sarinya; Ng, Alvin Wei Tian; Ng, Ley Moy; Choo, Su Pin; Myint, Swe Swe; Thanan, Raynoo; Nagarajan, Sanjanaa; Lim, Weng Khong; Ng, Cedric Chuan Young; Boot, Arnoud; Liu, Mo; Ong, Choon Kiat; Rajasegaran, Vikneswari; Lie, Stefanus; Lim, Alvin Soon Tiong; Lim, Tse Hui; Tan, Jing; Loh, Jia Liang; McPherson, John R.; Khuntikeo, Narong; Bhudhisawasdi, Vajaraphongsa; Yongvanit, Puangrat; Wongkham, Sopit; Totoki, Yasushi; Nakamura, Hiromi; Arai, Yasuhito; Yamasaki, Satoshi; Chow, Pierce Kah-Hoe; Chung, Alexander Yaw Fui; Ooi, London Lucien Peng Jin; Lim, Kiat Hon; Dima, Simona; Duda, Dan G.; Popescu, Irinel; Broet, Philippe; Hsieh, Sen-Yung; Yu, Ming-Chin; Scarpa, Aldo; Lai, Jiaming; Luo, Di-Xian; Carvalho, André Lopes; Vettore, André Luiz; Rhee, Hyungjin; Park, Young Nyun; Alexandrov, Ludmil B.; Gordân, Raluca; Rozen, Steven G.; Shibata, Tatsuhiro; Pairojkul, Chawalit; Teh, Bin Tean; Tan, Patrick

2017-01-01

Cholangiocarcinoma (CCA) is a hepatobiliary malignancy exhibiting high incidence in countries with endemic liver-fluke infection. We analysed 489 CCAs from 10 countries, combining whole-genome (71 cases), targeted/exome, copy-number, gene expression, and DNA methylation information. Integrative clustering defined four CCA clusters – Fluke-Positive CCAs (Clusters 1/2) are enriched in ERBB2 amplifications and TP53 mutations, conversely Fluke-Negative CCAs (Clusters 3/4) exhibit high copy-number alterations and PD-1/PD-L2 expression, or epigenetic mutations (IDH1/2, BAP1) and FGFR/PRKA-related gene rearrangements. Whole-genome analysis highlighted FGFR2 3′UTR deletion as a mechanism of FGFR2 upregulation. Integration of non-coding promoter mutations with protein-DNA binding profiles demonstrates pervasive modulation of H3K27me3-associated sites in CCA. Clusters 1 and 4 exhibit distinct DNA hypermethylation patterns targeting either CpG islands or shores – mutation signature and subclonality analysis suggests that these reflect different mutational pathways. Our results exemplify how genetics, epigenetics and environmental carcinogens can interplay across different geographies to generate distinct molecular subtypes of cancer. PMID:28667006

Whole-Genome and Epigenomic Landscapes of Etiologically Distinct Subtypes of Cholangiocarcinoma

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

Jusakul, Apinya; Cutcutache, Ioana; Yong, Chern Han

Cholangiocarcinoma (CCA) is a hepatobiliary malignancy exhibiting high incidence in countries with endemic liver-fluke infection. We analysed 489 CCAs from 10 countries, combining whole-genome (71 cases), targeted/exome, copy-number, gene expression, and DNA methylation information. Integrative clustering defined four CCA clusters - Fluke- Positive CCAs (Clusters 1/2) are enriched in ERBB2 amplifications and TP53 mutations, conversely Fluke-Negative CCAs (Clusters 3/4) exhibit high copy-number alterations and PD-1/PD-L2 expression, or epigenetic mutations (IDH1/2, BAP1) and FGFR/PRKA-related gene rearrangements. Whole-genome analysis highlighted FGFR2 3’UTR deletion as a mechanism of FGFR2 upregulation. Integration of non-coding promoter mutations with protein-DNA binding profiles demonstrates pervasive modulation ofmore » H3K27me3-associated sites in CCA. Clusters 1 and 4 exhibit distinct DNA hypermethylation patterns targeting either CpG islands or shores - mutation signature and subclonality analysis suggests that these reflect different mutational pathways. Lastly, our results exemplify how genetics, epigenetics and environmental carcinogens can interplay across different geographies to generate distinct molecular subtypes of cancer.« less

Whole-Genome and Epigenomic Landscapes of Etiologically Distinct Subtypes of Cholangiocarcinoma

DOE PAGES

Jusakul, Apinya; Cutcutache, Ioana; Yong, Chern Han; ...

2017-06-30

Cholangiocarcinoma (CCA) is a hepatobiliary malignancy exhibiting high incidence in countries with endemic liver-fluke infection. We analysed 489 CCAs from 10 countries, combining whole-genome (71 cases), targeted/exome, copy-number, gene expression, and DNA methylation information. Integrative clustering defined four CCA clusters - Fluke- Positive CCAs (Clusters 1/2) are enriched in ERBB2 amplifications and TP53 mutations, conversely Fluke-Negative CCAs (Clusters 3/4) exhibit high copy-number alterations and PD-1/PD-L2 expression, or epigenetic mutations (IDH1/2, BAP1) and FGFR/PRKA-related gene rearrangements. Whole-genome analysis highlighted FGFR2 3’UTR deletion as a mechanism of FGFR2 upregulation. Integration of non-coding promoter mutations with protein-DNA binding profiles demonstrates pervasive modulation ofmore » H3K27me3-associated sites in CCA. Clusters 1 and 4 exhibit distinct DNA hypermethylation patterns targeting either CpG islands or shores - mutation signature and subclonality analysis suggests that these reflect different mutational pathways. Lastly, our results exemplify how genetics, epigenetics and environmental carcinogens can interplay across different geographies to generate distinct molecular subtypes of cancer.« less

Identifying and reducing error in cluster-expansion approximations of protein energies.

PubMed

Hahn, Seungsoo; Ashenberg, Orr; Grigoryan, Gevorg; Keating, Amy E

2010-12-01

Protein design involves searching a vast space for sequences that are compatible with a defined structure. This can pose significant computational challenges. Cluster expansion is a technique that can accelerate the evaluation of protein energies by generating a simple functional relationship between sequence and energy. The method consists of several steps. First, for a given protein structure, a training set of sequences with known energies is generated. Next, this training set is used to expand energy as a function of clusters consisting of single residues, residue pairs, and higher order terms, if required. The accuracy of the sequence-based expansion is monitored and improved using cross-validation testing and iterative inclusion of additional clusters. As a trade-off for evaluation speed, the cluster-expansion approximation causes prediction errors, which can be reduced by including more training sequences, including higher order terms in the expansion, and/or reducing the sequence space described by the cluster expansion. This article analyzes the sources of error and introduces a method whereby accuracy can be improved by judiciously reducing the described sequence space. The method is applied to describe the sequence-stability relationship for several protein structures: coiled-coil dimers and trimers, a PDZ domain, and T4 lysozyme as examples with computationally derived energies, and SH3 domains in amphiphysin-1 and endophilin-1 as examples where the expanded pseudo-energies are obtained from experiments. Our open-source software package Cluster Expansion Version 1.0 allows users to expand their own energy function of interest and thereby apply cluster expansion to custom problems in protein design. © 2010 Wiley Periodicals, Inc.

A framework to spatially cluster air pollution monitoring sites in US based on the PM2.5 composition

PubMed Central

Austin, Elena; Coull, Brent A.; Zanobetti, Antonella; Koutrakis, Petros

2013-01-01

Background Heterogeneity in the response to PM2.5 is hypothesized to be related to differences in particle composition across monitoring sites which reflect differences in source types as well as climatic and topographic conditions impacting different geographic locations. Identifying spatial patterns in particle composition is a multivariate problem that requires novel methodologies. Objectives Use cluster analysis methods to identify spatial patterns in PM2.5 composition. Verify that the resulting clusters are distinct and informative. Methods 109 monitoring sites with 75% reported speciation data during the period 2003–2008 were selected. These sites were categorized based on their average PM2.5 composition over the study period using k-means cluster analysis. The obtained clusters were validated and characterized based on their physico-chemical characteristics, geographic locations, emissions profiles, population density and proximity to major emission sources. Results Overall 31 clusters were identified. These include 21 clusters with 2 or more sites which were further grouped into 4 main types using hierarchical clustering. The resulting groupings are chemically meaningful and represent broad differences in emissions. The remaining clusters, encompassing single sites, were characterized based on their particle composition and geographic location. Conclusions The framework presented here provides a novel tool which can be used to identify and further classify sites based on their PM2.5 composition. The solution presented is fairly robust and yielded groupings that were meaningful in the context of air-pollution research. PMID:23850585

Characterizing the course of back pain after osteoporotic vertebral fracture: a hierarchical cluster analysis of a prospective cohort study.

PubMed

Toyoda, Hiromitsu; Takahashi, Shinji; Hoshino, Masatoshi; Takayama, Kazushi; Iseki, Kazumichi; Sasaoka, Ryuichi; Tsujio, Tadao; Yasuda, Hiroyuki; Sasaki, Takeharu; Kanematsu, Fumiaki; Kono, Hiroshi; Nakamura, Hiroaki

2017-09-23

This study demonstrated four distinct patterns in the course of back pain after osteoporotic vertebral fracture (OVF). Greater angular instability in the first 6 months after the baseline was one factor affecting back pain after OVF. Understanding the natural course of symptomatic acute OVF is important in deciding the optimal treatment strategy. We used latent class analysis to classify the course of back pain after OVF and identify the risk factors associated with persistent pain. This multicenter cohort study included 218 consecutive patients with ≤ 2-week-old OVFs who were enrolled at 11 institutions. Dynamic x-rays and back pain assessment with a visual analog scale (VAS) were obtained at enrollment and at 1-, 3-, and 6-month follow-ups. The VAS scores were used to characterize patient groups, using hierarchical cluster analysis. VAS for 128 patients was used for hierarchical cluster analysis. Analysis yielded four clusters representing different patterns of back pain progression. Cluster 1 patients (50.8%) had stable, mild pain. Cluster 2 patients (21.1%) started with moderate pain and progressed quickly to very low pain. Patients in cluster 3 (10.9%) had moderate pain that initially improved but worsened after 3 months. Cluster 4 patients (17.2%) had persistent severe pain. Patients in cluster 4 showed significant high baseline pain intensity, higher degree of angular instability, and higher number of previous OVFs, and tended to lack regular exercise. In contrast, patients in cluster 2 had significantly lower baseline VAS and less angular instability. We identified four distinct groups of OVF patients with different patterns of back pain progression. Understanding the course of back pain after OVF may help in its management and contribute to future treatment trials.

Visualization and unsupervised predictive clustering of high-dimensional multimodal neuroimaging data.

PubMed

Mwangi, Benson; Soares, Jair C; Hasan, Khader M

2014-10-30

Neuroimaging machine learning studies have largely utilized supervised algorithms - meaning they require both neuroimaging scan data and corresponding target variables (e.g. healthy vs. diseased) to be successfully 'trained' for a prediction task. Noticeably, this approach may not be optimal or possible when the global structure of the data is not well known and the researcher does not have an a priori model to fit the data. We set out to investigate the utility of an unsupervised machine learning technique; t-distributed stochastic neighbour embedding (t-SNE) in identifying 'unseen' sample population patterns that may exist in high-dimensional neuroimaging data. Multimodal neuroimaging scans from 92 healthy subjects were pre-processed using atlas-based methods, integrated and input into the t-SNE algorithm. Patterns and clusters discovered by the algorithm were visualized using a 2D scatter plot and further analyzed using the K-means clustering algorithm. t-SNE was evaluated against classical principal component analysis. Remarkably, based on unlabelled multimodal scan data, t-SNE separated study subjects into two very distinct clusters which corresponded to subjects' gender labels (cluster silhouette index value=0.79). The resulting clusters were used to develop an unsupervised minimum distance clustering model which identified 93.5% of subjects' gender. Notably, from a neuropsychiatric perspective this method may allow discovery of data-driven disease phenotypes or sub-types of treatment responders. Copyright © 2014 Elsevier B.V. All rights reserved.

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

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

Whole Genome Analysis of Injectional Anthrax Identifies Two Disease Clusters Spanning More Than 13 Years.

PubMed

Keim, Paul; Grunow, Roland; Vipond, Richard; Grass, Gregor; Hoffmaster, Alex; Birdsell, Dawn N; Klee, Silke R; Pullan, Steven; Antwerpen, Markus; Bayer, Brittany N; Latham, Jennie; Wiggins, Kristin; Hepp, Crystal; Pearson, Talima; Brooks, Tim; Sahl, Jason; Wagner, David M

2015-11-01

from the German Ministry of Defense (Sonderforschungsprojekt 25Z1-S-431,214). Support for sequencing was also obtained from Illumina, Inc. These sources had no role in the data generation or interpretation, and had not role in the manuscript preparation. We searched PubMed for any article published before Jun. 17, 2015, with the terms "Bacillus anthracis" and "heroin", or "injectional anthrax". Other than our previously published work (Price et al., 2012), we found no other relevant studies on elucidating the global phylogenetic relationships of B. anthracis strains associated with injectional anthrax caused by recreational heroin consumption of spore-contaminated drug. There were, however, publically available genome sequences of two strains involved (Price et al., 2012, Grunow et al., 2013) and the draft genome sequence of Bacillus anthracis UR-1, isolated from a German heroin user (Ruckert et al., 2012) with only limited information on the genotyping of closely related strains (Price et al., 2012, Grunow et al., 2013). Injectional anthrax has been plaguing heroin drug users across Europe for more than 10 years. In order to better understand this outbreak, we assessed genomic relationships of all available injectional anthrax strains from four countries spanning a > 12 year period. Very few differences were identified using genome-based analysis, but these differentiated the isolates into two distinct clusters. This strongly supports a hypothesis of at least two separate anthrax spore contamination events perhaps during the drug production processes. Identification of two events would not have been possible from standard epidemiological analysis. These comprehensive data will be invaluable for classifying future injectional anthrax isolates and for future geographic attribution.

Homologues of a single resistance-gene cluster in potato confer resistance to distinct pathogens: a virus and a nematode.

PubMed

van der Vossen, E A; van der Voort, J N; Kanyuka, K; Bendahmane, A; Sandbrink, H; Baulcombe, D C; Bakker, J; Stiekema, W J; Klein-Lankhorst, R M

2000-09-01

The isolation of the nematode-resistance gene Gpa2 in potato is described, and it is demonstrated that highly homologous resistance genes of a single resistance-gene cluster can confer resistance to distinct pathogen species. Molecular analysis of the Gpa2 locus resulted in the identification of an R-gene cluster of four highly homologous genes in a region of approximately 115 kb. At least two of these genes are active: one corresponds to the previously isolated Rx1 gene that confers resistance to potato virus X, while the other corresponds to the Gpa2 gene that confers resistance to the potato cyst nematode Globodera pallida. The proteins encoded by the Gpa2 and the Rx1 genes share an overall homology of over 88% (amino-acid identity) and belong to the leucine-zipper, nucleotide-binding site, leucine-rich repeat (LZ-NBS-LRR)-containing class of plant resistance genes. From the sequence conservation between Gpa2 and Rx1 it is clear that there is a direct evolutionary relationship between the two proteins. Sequence diversity is concentrated in the LRR region and in the C-terminus. The putative effector domains are more conserved suggesting that, at least in this case, nematode and virus resistance cascades could share common components. These findings underline the potential of protein breeding for engineering new resistance specificities against plant pathogens in vitro.

NGC 6362: THE LEAST MASSIVE GLOBULAR CLUSTER WITH CHEMICALLY DISTINCT MULTIPLE POPULATIONS

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

Mucciarelli, Alessio; Dalessandro, Emanuele; Ferraro, Francesco R.

2016-06-20

We present the first measure of Fe and Na abundances in NGC 6362, a low-mass globular cluster (GC) where first- and second-generation stars are fully spatially mixed. A total of 160 member stars (along the red giant branch (RGB) and the red horizontal branch (RHB)) were observed with the multi-object spectrograph FLAMES at the Very Large Telescope. We find that the cluster has an iron abundance of [Fe/H] = −1.09 ± 0.01 dex, without evidence of intrinsic dispersion. On the other hand, the [Na/Fe] distribution turns out to be intrinsically broad and bimodal. The Na-poor and Na-rich stars populate, respectively,more » the bluest and the reddest RGBs detected in the color–magnitude diagrams including the U filter. The RGB is composed of a mixture of first- and second-generation stars in a similar proportion, while almost all the RHB stars belong to the first cluster generation. To date, NGC 6362 is the least massive GC where both the photometric and spectroscopic signatures of multiple populations have been detected.« less

Clustering the Orion B giant molecular cloud based on its molecular emission

PubMed Central

Bron, Emeric; Daudon, Chloé; Pety, Jérôme; Levrier, François; Gerin, Maryvonne; Gratier, Pierre; Orkisz, Jan H.; Guzman, Viviana; Bardeau, Sébastien; Goicoechea, Javier R.; Liszt, Harvey; Öberg, Karin; Peretto, Nicolas; Sievers, Albrecht; Tremblin, Pascal

2017-01-01

densest regions are also identified, likely related to the higher critical density of the CN and HCO+ (1 – 0) lines. These distinctions are only possible because the high-density regions are spatially resolved. Conclusions Molecules are versatile tracers of GMCs because their line intensities bear the signature of the physics and chemistry at play in the gas. The association of simultaneous multi-line, wide-field mapping and powerful machine learning methods such as the Meanshift clustering algorithm reveals how to decode the complex information available in these molecular tracers. PMID:29456256

Clustering the Orion B giant molecular cloud based on its molecular emission.

PubMed

Bron, Emeric; Daudon, Chloé; Pety, Jérôme; Levrier, François; Gerin, Maryvonne; Gratier, Pierre; Orkisz, Jan H; Guzman, Viviana; Bardeau, Sébastien; Goicoechea, Javier R; Liszt, Harvey; Öberg, Karin; Peretto, Nicolas; Sievers, Albrecht; Tremblin, Pascal

2018-02-01

identified, likely related to the higher critical density of the CN and HCO + (1 - 0) lines. These distinctions are only possible because the high-density regions are spatially resolved. Molecules are versatile tracers of GMCs because their line intensities bear the signature of the physics and chemistry at play in the gas. The association of simultaneous multi-line, wide-field mapping and powerful machine learning methods such as the Meanshift clustering algorithm reveals how to decode the complex information available in these molecular tracers.

Novel linkage disequilibrium clustering algorithm identifies new lupus genes on meta-analysis of GWAS datasets.

PubMed

Saeed, Mohammad

2017-05-01

Systemic lupus erythematosus (SLE) is a complex disorder. Genetic association studies of complex disorders suffer from the following three major issues: phenotypic heterogeneity, false positive (type I error), and false negative (type II error) results. Hence, genes with low to moderate effects are missed in standard analyses, especially after statistical corrections. OASIS is a novel linkage disequilibrium clustering algorithm that can potentially address false positives and negatives in genome-wide association studies (GWAS) of complex disorders such as SLE. OASIS was applied to two SLE dbGAP GWAS datasets (6077 subjects; ∼0.75 million single-nucleotide polymorphisms). OASIS identified three known SLE genes viz. IFIH1, TNIP1, and CD44, not previously reported using these GWAS datasets. In addition, 22 novel loci for SLE were identified and the 5 SLE genes previously reported using these datasets were verified. OASIS methodology was validated using single-variant replication and gene-based analysis with GATES. This led to the verification of 60% of OASIS loci. New SLE genes that OASIS identified and were further verified include TNFAIP6, DNAJB3, TTF1, GRIN2B, MON2, LATS2, SNX6, RBFOX1, NCOA3, and CHAF1B. This study presents the OASIS algorithm, software, and the meta-analyses of two publicly available SLE GWAS datasets along with the novel SLE genes. Hence, OASIS is a novel linkage disequilibrium clustering method that can be universally applied to existing GWAS datasets for the identification of new genes.

Clustering of disulfide-rich peptides provides scaffolds for hit discovery by phage display: application to interleukin-23.

PubMed

Barkan, David T; Cheng, Xiao-Li; Celino, Herodion; Tran, Tran T; Bhandari, Ashok; Craik, Charles S; Sali, Andrej; Smythe, Mark L

2016-11-23

Disulfide-rich peptides (DRPs) are found throughout nature. They are suitable scaffolds for drug development due to their small cores, whose disulfide bonds impart extraordinary chemical and biological stability. A challenge in developing a DRP therapeutic is to engineer binding to a specific target. This challenge can be overcome by (i) sampling the large sequence space of a given scaffold through a phage display library and by (ii) panning multiple libraries encoding structurally distinct scaffolds. Here, we implement a protocol for defining these diverse scaffolds, based on clustering structurally defined DRPs according to their conformational similarity. We developed and applied a hierarchical clustering protocol based on DRP structural similarity, followed by two post-processing steps, to classify 806 unique DRP structures into 81 clusters. The 20 most populated clusters comprised 85% of all DRPs. Representative scaffolds were selected from each of these clusters; the representatives were structurally distinct from one another, but similar to other DRPs in their respective clusters. To demonstrate the utility of the clusters, phage libraries were constructed for three of the representative scaffolds and panned against interleukin-23. One library produced a peptide that bound to this target with an IC 50 of 3.3 μM. Most DRP clusters contained members that were diverse in sequence, host organism, and interacting proteins, indicating that cluster members were functionally diverse despite having similar structure. Only 20 peptide scaffolds accounted for most of the natural DRP structural diversity, providing suitable starting points for seeding phage display experiments. Through selection of the scaffold surface to vary in phage display, libraries can be designed that present sequence diversity in architecturally distinct, biologically relevant combinations of secondary structures. We supported this hypothesis with a proof-of-concept experiment in which three

A cross-sectional cluster analysis of the combined association of physical activity and sleep with sociodemographic and health characteristics in mid-aged and older adults.

PubMed

Rayward, Anna T; Duncan, Mitch J; Brown, Wendy J; Plotnikoff, Ronald C; Burton, Nicola W

2017-08-01

This study aimed to identify how different patterns of physical activity, sleep duration and sleep quality cluster together, and to examine how the identified clusters differ in terms of socio-demographic and health characteristics. Participants were adults from Brisbane, Australia, aged 42-72 years who reported their physical activity, sleep duration, sleep quality, socio-demographic and health characteristics in 2011 (n=5854). Two-step Cluster Analyses were used to identify clusters. Cluster differences in socio-demographic and health characteristics were examined using chi square tests (p<0.05). Four clusters were identified: 'Poor Sleepers' (31.2%), 'Moderate Sleepers' (30.7%), 'Mixed Sleepers/Highly Active' (20.5%), and 'Excellent Sleepers/Mixed Activity' (17.6%). The 'Poor Sleepers' cluster had the highest proportion of participants with less-than-recommended sleep duration and poor sleep quality, had the poorest health characteristics and a high proportion of participants with low physical activity. Physical activity, sleep duration and sleep quality cluster together in distinct patterns and clusters of poor behaviours are associated with poor health status. Multiple health behaviour change interventions which target both physical activity and sleep should be prioritised to improve health outcomes in mid-aged adults. Copyright © 2017 Elsevier B.V. All rights reserved.

Clustering autism: using neuroanatomical differences in 26 mouse models to gain insight into the heterogeneity.

PubMed

Ellegood, J; Anagnostou, E; Babineau, B A; Crawley, J N; Lin, L; Genestine, M; DiCicco-Bloom, E; Lai, J K Y; Foster, J A; Peñagarikano, O; Geschwind, D H; Pacey, L K; Hampson, D R; Laliberté, C L; Mills, A A; Tam, E; Osborne, L R; Kouser, M; Espinosa-Becerra, F; Xuan, Z; Powell, C M; Raznahan, A; Robins, D M; Nakai, N; Nakatani, J; Takumi, T; van Eede, M C; Kerr, T M; Muller, C; Blakely, R D; Veenstra-VanderWeele, J; Henkelman, R M; Lerch, J P

2015-02-01

Autism is a heritable disorder, with over 250 associated genes identified to date, yet no single gene accounts for >1-2% of cases. The clinical presentation, behavioural symptoms, imaging and histopathology findings are strikingly heterogeneous. A more complete understanding of autism can be obtained by examining multiple genetic or behavioural mouse models of autism using magnetic resonance imaging (MRI)-based neuroanatomical phenotyping. Twenty-six different mouse models were examined and the consistently found abnormal brain regions across models were parieto-temporal lobe, cerebellar cortex, frontal lobe, hypothalamus and striatum. These models separated into three distinct clusters, two of which can be linked to the under and over-connectivity found in autism. These clusters also identified previously unknown connections between Nrxn1α, En2 and Fmr1; Nlgn3, BTBR and Slc6A4; and also between X monosomy and Mecp2. With no single treatment for autism found, clustering autism using neuroanatomy and identifying these strong connections may prove to be a crucial step in predicting treatment response.

Comparison of Intra-cluster and M87 Halo Orphan Globular Clusters in the Virgo Cluster

NASA Astrophysics Data System (ADS)

Louie, Tiffany Kaye; Tuan, Jin Zong; Martellini, Adhara; Guhathakurta, Puragra; Toloba, Elisa; Peng, Eric; Longobardi, Alessia; Lim, Sungsoon

2018-01-01

We present a study of “orphan” globular clusters (GCs) — GCs with no identifiable nearby host galaxy — discovered in NGVS, a 104 deg2 CFHT/MegaCam imaging survey. At the distance of the Virgo cluster, GCs are bright enough to make good spectroscopic targets and many are barely resolved in good ground-based seeing. Our orphan GC sample is derived from a subset of NGVS-selected GC candidates that were followed up with Keck/DEIMOS spectroscopy. While our primary spectroscopic targets were candidate GC satellites of Virgo dwarf elliptical and ultra-diffuse galaxies, many objects turned out to be non-satellites based on a radial velocity mismatch with the Virgo galaxy they are projected close to. Using a combination of spectral characteristics (e.g., absorption vs. emission), Gaussian mixture modeling of radial velocity and positions, and extreme deconvolution analysis of ugrizk photometry and image morphology, these non-satellites were classified into: (1) intra-cluster GCs (ICGCs) in the Virgo cluster, (2) GCs in the outer halo of M87, (3) foreground Milky Way stars, and (4) background galaxies. The statistical distinction between ICGCs and M87 halo GCs is based on velocity distributions (mean of 1100 vs. 1300 km/s and dispersions of 700 vs. 400 km/s, respectively) and radial distribution (diffuse vs. centrally concentrated, respectively). We used coaddition to increase the spectral SNR for the two classes of orphan GCs and measured the equivalent widths (EWs) of the Mg b and H-beta absorption lines. These EWs were compared to single stellar population models to obtain mean age and metallicity estimates. The ICGCs and M87 halo GCs have <[Fe/H> = –0.6+/–0.3 and –0.4+/–0.3 dex, respectively, and mean ages of >~ 5 and >~ 10 Gyr, respectively. This suggests the M87 halo GCs formed in relatively high-mass galaxies that avoided being tidally disrupted by M87 until they were close to the cluster center, while IGCCs formed in relatively low-mass galaxies that

A cluster analysis of tic symptoms in children and adults with Tourette syndrome: clinical correlates and treatment outcome.

PubMed

McGuire, Joseph F; Nyirabahizi, Epiphanie; Kircanski, Katharina; Piacentini, John; Peterson, Alan L; Woods, Douglas W; Wilhelm, Sabine; Walkup, John T; Scahill, Lawrence

2013-12-30

Cluster analytic methods have examined the symptom presentation of chronic tic disorders (CTDs), with limited agreement across studies. The present study investigated patterns, clinical correlates, and treatment outcome of tic symptoms. 239 youth and adults with CTDs completed a battery of assessments at baseline to determine diagnoses, tic severity, and clinical characteristics. Participants were randomly assigned to receive either a comprehensive behavioral intervention for tics (CBIT) or psychoeducation and supportive therapy (PST). A cluster analysis was conducted on the baseline Yale Global Tic Severity Scale (YGTSS) symptom checklist to identify the constellations of tic symptoms. Four tic clusters were identified: Impulse Control and Complex Phonic Tics; Complex Motor Tics; Simple Head Motor/Vocal Tics; and Primarily Simple Motor Tics. Frequencies of tic symptoms showed few differences across youth and adults. Tic clusters had small associations with clinical characteristics and showed no associations to the presence of coexisting psychiatric conditions. Cluster membership scores did not predict treatment response to CBIT or tic severity reductions. Tic symptoms distinctly cluster with little difference across youth and adults, or coexisting conditions. This study, which is the first to examine tic clusters and response to treatment, suggested that tic symptom profiles respond equally well to CBIT. Clinical trials.gov. identifiers: NCT00218777; NCT00231985. © 2013 Elsevier Ireland Ltd. All rights reserved.

Identifying areas of need relative to liver disease: geographic clustering within a health service district.

PubMed

El-Atem, Nathan; Irvine, Katharine M; Valery, Patricia C; Wojcik, Kyle; Horsfall, Leigh; Johnson, Tracey; Janda, Monika; McPhail, Steven M; Powell, Elizabeth E

2017-08-01

Background Many people with chronic liver disease (CLD) are not detected until they present to hospital with advanced disease, when opportunities for intervention are reduced and morbidity is high. In order to build capacity and liver expertise in the community, it is important to focus liver healthcare resources in high-prevalence disease areas and specific populations with an identified need. The aim of the present study was to examine the geographic location of people seen in a tertiary hospital hepatology clinic, as well as ethnic and sociodemographic characteristics of these geographic areas. Methods The geographic locations of hepatology out-patients were identified via the out-patient scheduling database and grouped into statistical area (SA) regions for demographic analysis using data compiled by the Australian Bureau of Statistics. Results During the 3-month study period, 943 individuals from 71 SA Level 3 regions attended clinic. Nine SA Level 3 regions accounted for 55% of the entire patient cohort. Geographic clustering was seen especially for people living with chronic hepatitis B virus. There was a wide spectrum of socioeconomic advantage and disadvantage in areas with high liver disease prevalence. Conclusions The geographic area from which people living with CLD travel to access liver health care is extensive. However, the greatest demand for tertiary liver disease speciality care is clustered within specific geographic areas. Outreach programs targeted to these areas may enhance liver disease-specific health service resourcing. What is known about the topic? The demand for tertiary hospital clinical services in CLD is rising. However, there is limited knowledge about the geographic areas from which people living with CLD travel to access liver services, or the ethnic, socioeconomic and education characteristics of these areas. What does this paper add? The present study demonstrates that a substantial proportion of people living with CLD and

A Scale-Independent Clustering Method with Automatic Variable Selection Based on Trees

DTIC Science & Technology

2014-03-01

veterans fought. They then clustered the data and were able to identify three distinct post-combat syndromes associated with different eras...granting some legitimacy to proposed medical conditions such as the Gulf War Syndrome (Jones et al., 2002, pp. 321–324) D. MEASURING DISTANCES BETWEEN...chosen so as to minimize the sum of squared errors of the response across the two regions (Equation 2.1). The average y for the left and right child

Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis.

PubMed

Bos, L D; Schouten, L R; van Vught, L A; Wiewel, M A; Ong, D S Y; Cremer, O; Artigas, A; Martin-Loeches, I; Hoogendijk, A J; van der Poll, T; Horn, J; Juffermans, N; Calfee, C S; Schultz, M J

2017-10-01

We hypothesised that patients with acute respiratory distress syndrome (ARDS) can be clustered based on concentrations of plasma biomarkers and that the thereby identified biological phenotypes are associated with mortality. Consecutive patients with ARDS were included in this prospective observational cohort study. Cluster analysis of 20 biomarkers of inflammation, coagulation and endothelial activation provided the phenotypes in a training cohort, not taking any outcome data into account. Logistic regression with backward selection was used to select the most predictive biomarkers, and these predicted phenotypes were validated in a separate cohort. Multivariable logistic regression was used to quantify the independent association with mortality. Two phenotypes were identified in 454 patients, which we named 'uninflamed' (N=218) and 'reactive' (N=236). A selection of four biomarkers (interleukin-6, interferon gamma, angiopoietin 1/2 and plasminogen activator inhibitor-1) could be used to accurately predict the phenotype in the training cohort (area under the receiver operating characteristics curve: 0.98, 95% CI 0.97 to 0.99). Mortality rates were 15.6% and 36.4% (p<0.001) in the training cohort and 13.6% and 37.5% (p<0.001) in the validation cohort (N=207). The 'reactive phenotype' was independent from confounders associated with intensive care unit mortality (training cohort: OR 1.13, 95% CI 1.04 to 1.23; validation cohort: OR 1.18, 95% CI 1.06 to 1.31). Patients with ARDS can be clustered into two biological phenotypes, with different mortality rates. Four biomarkers can be used to predict the phenotype with high accuracy. The phenotypes were very similar to those found in cohorts derived from randomised controlled trials, and these results may improve patient selection for future clinical trials targeting host response in patients with ARDS. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please

Hydrophobic mismatch sorts SNARE proteins into distinct membrane domains

PubMed Central

Milovanovic, Dragomir; Honigmann, Alf; Koike, Seiichi; Göttfert, Fabian; Pähler, Gesa; Junius, Meike; Müllar, Stefan; Diederichsen, Ulf; Janshoff, Andreas; Grubmüller, Helmut; Risselada, Herre J.; Eggeling, Christian; Hell, Stefan W.; van den Bogaart, Geert; Jahn, Reinhard

2015-01-01

The clustering of proteins and lipids in distinct microdomains is emerging as an important principle for the spatial patterning of biological membranes. Such domain formation can be the result of hydrophobic and ionic interactions with membrane lipids as well as of specific protein–protein interactions. Here using plasma membrane-resident SNARE proteins as model, we show that hydrophobic mismatch between the length of transmembrane domains (TMDs) and the thickness of the lipid membrane suffices to induce clustering of proteins. Even when the TMDs differ in length by only a single residue, hydrophobic mismatch can segregate structurally closely homologous membrane proteins in distinct membrane domains. Domain formation is further fine-tuned by interactions with polyanionic phosphoinositides and homo and heterotypic protein interactions. Our findings demonstrate that hydrophobic mismatch contributes to the structural organization of membranes. PMID:25635869

Gene Cluster Encoding Cholate Catabolism in Rhodococcus spp.

PubMed Central

Wilbrink, Maarten H.; Casabon, Israël; Stewart, Gordon R.; Liu, Jie; van der Geize, Robert; Eltis, Lindsay D.

2012-01-01

Bile acids are highly abundant steroids with important functions in vertebrate digestion. Their catabolism by bacteria is an important component of the carbon cycle, contributes to gut ecology, and has potential commercial applications. We found that Rhodococcus jostii RHA1 grows well on cholate, as well as on its conjugates, taurocholate and glycocholate. The transcriptome of RHA1 growing on cholate revealed 39 genes upregulated on cholate, occurring in a single gene cluster. Reverse transcriptase quantitative PCR confirmed that selected genes in the cluster were upregulated 10-fold on cholate versus on cholesterol. One of these genes, kshA3, encoding a putative 3-ketosteroid-9α-hydroxylase, was deleted and found essential for growth on cholate. Two coenzyme A (CoA) synthetases encoded in the cluster, CasG and CasI, were heterologously expressed. CasG was shown to transform cholate to cholyl-CoA, thus initiating side chain degradation. CasI was shown to form CoA derivatives of steroids with isopropanoyl side chains, likely occurring as degradation intermediates. Orthologous gene clusters were identified in all available Rhodococcus genomes, as well as that of Thermomonospora curvata. Moreover, Rhodococcus equi 103S, Rhodococcus ruber Chol-4 and Rhodococcus erythropolis SQ1 each grew on cholate. In contrast, several mycolic acid bacteria lacking the gene cluster were unable to grow on cholate. Our results demonstrate that the above-mentioned gene cluster encodes cholate catabolism and is distinct from a more widely occurring gene cluster encoding cholesterol catabolism. PMID:23024343

Anti-MDA5 autoantibodies in juvenile dermatomyositis identify a distinct clinical phenotype: a prospective cohort study

PubMed Central

2014-01-01

Introduction The aim of this study was to define the frequency and associated clinical phenotype of anti-MDA5 autoantibodies in a large UK based, predominantly Caucasian, cohort of patients with juvenile dermatomyositis (JDM). Methods Serum samples and clinical data were obtained from 285 patients with JDM recruited to the UK Juvenile Dermatomyositis Cohort and Biomarker Study. The presence of anti-MDA5 antibodies was determined by immunoprecipitation and confirmed by ELISA using recombinant MDA5 protein. Results were compared with matched clinical data, muscle biopsies (scored by an experienced paediatric neuropathologist) and chest imaging (reviewed by an experienced paediatric radiologist). Results Anti-MDA5 antibodies were identified in 7.4% of JDM patients and were associated with a distinct clinical phenotype including skin ulceration (P = 0.03) oral ulceration (P = 0.01), arthritis (P <0.01) and milder muscle disease both clinically (as determined by Childhood Myositis Assessment Score (P = 0.03)) and histologically (as determined by a lower JDM muscle biopsy score (P <0.01)) than patients who did not have anti-MDA5 antibodies. A greater proportion of children with anti-MDA5 autoantibodies achieved disease inactivity at two years post-diagnosis according to PRINTO criteria (P = 0.02). A total of 4 out of 21 children with anti-MDA5 had interstitial lung disease; none had rapidly progressive interstitial lung disease. Conclusions Anti-MDA5 antibodies can be identified in a small but significant proportion of patients with JDM and identify a distinctive clinical sub-group. Screening for anti-MDA5 autoantibodies at diagnosis would be useful to guide further investigation for lung disease, inform on prognosis and potentially confirm the diagnosis, as subtle biopsy changes could otherwise be missed. PMID:24989778

Diffusion-weighted MRI derived apparent diffusion coefficient identifies prognostically distinct subgroups of pediatric diffuse intrinsic pontine glioma.

PubMed

Lober, Robert M; Cho, Yoon-Jae; Tang, Yujie; Barnes, Patrick D; Edwards, Michael S; Vogel, Hannes; Fisher, Paul G; Monje, Michelle; Yeom, Kristen W

2014-03-01

While pediatric diffuse intrinsic pontine gliomas (DIPG) remain fatal, recent data have shown subgroups with distinct molecular biology and clinical behavior. We hypothesized that diffusion-weighted MRI can be used as a prognostic marker to stratify DIPG subsets with distinct clinical behavior. Apparent diffusion coefficient (ADC) values derived from diffusion-weighted MRI were computed in 20 consecutive children with treatment-naïve DIPG tumors. The median ADC for the cohort was used to stratify the tumors into low and high ADC groups. Survival, gender, therapy, and potential steroid effects were compared between the ADC groups. Median age at diagnosis was 6.6 (range 2.3-13.2) years, with median follow-up seven (range 1-36) months. There were 14 boys and six girls. Seventeen patients received radiotherapy, five received chemotherapy, and six underwent cerebrospinal fluid diversion. The median ADC of 1,295 × 10(-6) mm(2)/s for the cohort partitioned tumors into low or high diffusion groups, which had distinct median survivals of 3 and 13 months, respectively (log-rank p < 0.001). Low ADC tumors were found only in boys, whereas high ADC tumors were found in both boys and girls. Available tissue specimens in three low ADC tumors demonstrated high-grade histology, whereas one high ADC tumor demonstrated low-grade histology with a histone H3.1 K27M mutation and high-grade metastatic lesion at autopsy. ADC derived from diffusion-weighted MRI may identify prognostically distinct subgroups of pediatric DIPG.

Seismic clusters analysis in Northeastern Italy by the nearest-neighbor approach

NASA Astrophysics Data System (ADS)

Peresan, Antonella; Gentili, Stefania

2018-01-01

The main features of earthquake clusters in Northeastern Italy are explored, with the aim to get new insights on local scale patterns of seismicity in the area. The study is based on a systematic analysis of robustly and uniformly detected seismic clusters, which are identified by a statistical method, based on nearest-neighbor distances of events in the space-time-energy domain. The method permits us to highlight and investigate the internal structure of earthquake sequences, and to differentiate the spatial properties of seismicity according to the different topological features of the clusters structure. To analyze seismicity of Northeastern Italy, we use information from local OGS bulletins, compiled at the National Institute of Oceanography and Experimental Geophysics since 1977. A preliminary reappraisal of the earthquake bulletins is carried out and the area of sufficient completeness is outlined. Various techniques are considered to estimate the scaling parameters that characterize earthquakes occurrence in the region, namely the b-value and the fractal dimension of epicenters distribution, required for the application of the nearest-neighbor technique. Specifically, average robust estimates of the parameters of the Unified Scaling Law for Earthquakes, USLE, are assessed for the whole outlined region and are used to compute the nearest-neighbor distances. Clusters identification by the nearest-neighbor method turn out quite reliable and robust with respect to the minimum magnitude cutoff of the input catalog; the identified clusters are well consistent with those obtained from manual aftershocks identification of selected sequences. We demonstrate that the earthquake clusters have distinct preferred geographic locations, and we identify two areas that differ substantially in the examined clustering properties. Specifically, burst-like sequences are associated with the north-western part and swarm-like sequences with the south-eastern part of the study

Population genetics of Vibrio vulnificus: identification of two divisions and a distinct eel-pathogenic clone.

PubMed

Gutacker, Michaela; Conza, Nadine; Benagli, Cinzia; Pedroli, Ambra; Bernasconi, Marco Valerio; Permin, Lise; Aznar, Rosa; Piffaretti, Jean-Claude

2003-06-01

Genetic relationships among 62 Vibrio vulnificus strains of different geographical and host origins were analyzed by multilocus enzyme electrophoresis (MLEE), random amplification of polymorphic DNA (RAPD), and sequence analyses of the recA and glnA genes. Out of 15 genetic loci analyzed by MLEE, 11 were polymorphic. Cluster analysis identified 43 distinct electrophoretic types (ETs) separating the V. vulnificus population into two divisions (divisions I and II). One ET (ET 35) included all indole-negative isolates from diseased eels worldwide (biotype 2). A second ET (ET 2) marked all of the strains from Israel isolated from patients who handled St. Peter's fish (biotype 3). RAPD analysis of the 62 V. vulnificus isolates identified 26 different profiles separated into two divisions as well. In general, this subdivision was comparable (but not identical) to that observed by MLEE. Phylogenetic analysis of 543 bp of the recA gene and of 402 bp of the glnA gene also separated the V. vulnificus population into two major divisions in a manner similar to that by MLEE and RAPD. Sequence data again indicated the overall subdivision of the V. vulnificus population into different biotypes. In particular, indole-negative eel-pathogenic isolates (biotype 2) on one hand and the Israeli isolates (biotype 3) on the other tended to cluster together in both gene trees. None of the methods showed an association between distinct clones and human clinical manifestations. Furthermore, except for the Israeli strains, only minor clusters comprising geographically related isolates were observed. In conclusion, all three approaches (MLEE, RAPD, and DNA sequencing) generated comparable but not always equivalent results. The significance of the two divisions (divisions I and II) still remains to be clarified, and a reevaluation of the definition of the biotypes is also needed.

Symptom Cluster Trajectories During Chemotherapy in Breast Cancer Outpatients.

PubMed

Hsu, Hsin-Tien; Lin, Kuan-Chia; Wu, Li-Min; Juan, Chiung-Hui; Hou, Ming-Feng; Hwang, Shiow-Li; Liu, Yi; Dodd, Marylin J

2017-06-01

Breast cancer patients often experience multiple symptoms and substantial discomfort. Some symptoms may occur simultaneously and throughout the duration of chemotherapy treatment. The aim of this study was to investigate symptom severity and symptom cluster trajectories during chemotherapy in outpatients with breast cancer in Taiwan. This prospective, longitudinal, repeated measures study administered a standardized questionnaire (M. D. Anderson Symptom Inventory Taiwan version) to 103 breast cancer patients during each day of the third 21-day cycle of chemotherapy. Latent class growth analysis was performed to examine symptom cluster trajectories. Three symptom clusters were identified within the first 14 days of the 21-day chemotherapy cycle: the neurocognition cluster (pain, shortness of breath, vomiting, memory problems, and numbness/tingling) with a trajectory of Y = 2.09 - 0.11 (days), the emotion-nausea cluster (nausea, disturbed sleep, distress/upset, drowsiness, and sadness) with a trajectory ofY = 3.57 - 0.20 (days), and the fatigue-anorexia cluster (fatigue, lack of appetite, and dry mouth) with a trajectory of Y = 4.22 - 0.21 (days). The "fatigue-anorexia cluster" and "emotion-nausea cluster" peaked at moderate levels on chemotherapy days 3-5, and then gradually decreased to mild levels within the first 14 days of the 21-day chemotherapy cycle. Distinct symptom clusters were observed during the third cycle of chemotherapy. Systematic and ongoing evaluation of symptom cluster trajectories during cancer treatment is essential. Healthcare providers can use these findings to enhance communication with their breast cancer patients and to prioritize symptoms that require attention and intervention. Copyright © 2017 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Structural motifs of pre-nucleation clusters.

PubMed

Zhang, Y; Türkmen, I R; Wassermann, B; Erko, A; Rühl, E

2013-10-07

Structural motifs of pre-nucleation clusters prepared in single, optically levitated supersaturated aqueous aerosol microparticles containing CaBr2 as a model system are reported. Cluster formation is identified by means of X-ray absorption in the Br K-edge regime. The salt concentration beyond the saturation point is varied by controlling the humidity in the ambient atmosphere surrounding the 15-30 μm microdroplets. This leads to the formation of metastable supersaturated liquid particles. Distinct spectral shifts in near-edge spectra as a function of salt concentration are observed, in which the energy position of the Br K-edge is red-shifted by up to 7.1 ± 0.4 eV if the dilute solution is compared to the solid. The K-edge positions of supersaturated solutions are found between these limits. The changes in electronic structure are rationalized in terms of the formation of pre-nucleation clusters. This assumption is verified by spectral simulations using first-principle density functional theory and molecular dynamics calculations, in which structural motifs are considered, explaining the experimental results. These consist of solvated CaBr2 moieties, rather than building blocks forming calcium bromide hexahydrates, the crystal system that is formed by drying aqueous CaBr2 solutions.

Functionally distinct amygdala subregions identified using DTI and high-resolution fMRI

PubMed Central

Balderston, Nicholas L.; Schultz, Douglas H.; Hopkins, Lauren

2015-01-01

Although the amygdala is often directly linked with fear and emotion, amygdala neurons are activated by a wide variety of emotional and non-emotional stimuli. Different subregions within the amygdala may be engaged preferentially by different aspects of emotional and non-emotional tasks. To test this hypothesis, we measured and compared the effects of novelty and fear on amygdala activity. We used high-resolution blood oxygenation level-dependent (BOLD) imaging and streamline tractography to subdivide the amygdala into three distinct functional subunits. We identified a laterobasal subregion connected with the visual cortex that responds generally to visual stimuli, a non-projecting region that responds to salient visual stimuli, and a centromedial subregion connected with the diencephalon that responds only when a visual stimulus predicts an aversive outcome. We provide anatomical and functional support for a model of amygdala function where information enters through the laterobasal subregion, is processed by intrinsic circuits in the interspersed tissue, and is then passed to the centromedial subregion, where activation leads to behavioral output. PMID:25969533

A perfect starburst cluster made in one go: The NGC 3603 young cluster

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

Banerjee, Sambaran; Kroupa, Pavel

2014-06-01

Understanding how distinct, near-spherical gas-free clusters of very young, massive stars shape out of vast, complex clouds of molecular hydrogen is one of the biggest challenges in astrophysics. A popular thought dictates that a single gas cloud fragments into many newborn stars which, in turn, energize and rapidly expel the residual gas to form a gas-free cluster. This study demonstrates that the above classical paradigm remarkably reproduces the well-observed central, young cluster (HD 97950) of the Galactic NGC 3603 star-forming region, in particular, its shape, internal motion, and mass distribution of stars naturally and consistently follow from a single modelmore » calculation. Remarkably, the same parameters (star formation efficiency, gas expulsion timescale, and delay) reproduce HD 97950, as were found to reproduce the Orion Nebula Cluster, Pleiades, and R136. The present results therefore provide intriguing evidence of formation of star clusters through single-starburst events followed by significant residual gas expulsion.« less

Widespread Micropollutant Monitoring in the Hudson River Estuary Reveals Spatiotemporal Micropollutant Clusters and Their Sources.

PubMed

Carpenter, Corey M G; Helbling, Damian E

2018-06-05

The objective of this study was to identify sources of micropollutants in the Hudson River Estuary (HRE). We collected 127 grab samples at 17 sites along the HRE over 2 years and screened for up to 200 micropollutants. We quantified 168 of the micropollutants in at least one of the samples. Atrazine, gabapentin, metolachlor, and sucralose were measured in every sample. We used data-driven unsupervised methods to cluster the micropollutants on the basis of their spatiotemporal occurrence and normalized-concentration patterns. Three major clusters of micropollutants were identified: ubiquitous and mixed-use (core micropollutants), sourced from sewage treatment plant outfalls (STP micropollutants), and derived from diffuse upstream sources (diffuse micropollutants). Each of these clusters was further refined into subclusters that were linked to specific sources on the basis of relationships identified through geospatial analysis of watershed features. Evaluation of cumulative loadings of each subcluster revealed that the Mohawk River and Rondout Creek are major contributors of most core micropollutants and STP micropollutants and the upper HRE is a major contributor of diffuse micropollutants. These data provide the first comprehensive evaluation of micropollutants in the HRE and define distinct spatiotemporal micropollutant clusters that are linked to sources and conserved across surface water systems around the world.

Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis.

PubMed

Cohen, Mitchell J; Grossman, Adam D; Morabito, Diane; Knudson, M Margaret; Butte, Atul J; Manley, Geoffrey T

2010-01-01

Advances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome. Multivariate physiologic and ventilator data were collected continuously using a multimodal bioinformatics system in the surgical ICU at San Francisco General Hospital. These data were incorporated with non-continuous data and stored on a server in the ICU. A hierarchical clustering algorithm grouped each minute of data into 1 of 10 clusters. Clusters were correlated with outcome measures including incidence of infection, multiple organ failure (MOF), and mortality. We identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters. Here we show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new

Using Hierarchical Cluster Models to Systematically Identify Groups of Jobs With Similar Occupational Questionnaire Response Patterns to Assist Rule-Based Expert Exposure Assessment in Population-Based Studies

PubMed Central

Friesen, Melissa C.; Shortreed, Susan M.; Wheeler, David C.; Burstyn, Igor; Vermeulen, Roel; Pronk, Anjoeka; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Schwenn, Molly; Johnson, Alison; Armenti, Karla R.; Silverman, Debra T.; Yu, Kai

2015-01-01

Objectives: Rule-based expert exposure assessment based on questionnaire response patterns in population-based studies improves the transparency of the decisions. The number of unique response patterns, however, can be nearly equal to the number of jobs. An expert may reduce the number of patterns that need assessment using expert opinion, but each expert may identify different patterns of responses that identify an exposure scenario. Here, hierarchical clustering methods are proposed as a systematic data reduction step to reproducibly identify similar questionnaire response patterns prior to obtaining expert estimates. As a proof-of-concept, we used hierarchical clustering methods to identify groups of jobs (clusters) with similar responses to diesel exhaust-related questions and then evaluated whether the jobs within a cluster had similar (previously assessed) estimates of occupational diesel exhaust exposure. Methods: Using the New England Bladder Cancer Study as a case study, we applied hierarchical cluster models to the diesel-related variables extracted from the occupational history and job- and industry-specific questionnaires (modules). Cluster models were separately developed for two subsets: (i) 5395 jobs with ≥1 variable extracted from the occupational history indicating a potential diesel exposure scenario, but without a module with diesel-related questions; and (ii) 5929 jobs with both occupational history and module responses to diesel-relevant questions. For each subset, we varied the numbers of clusters extracted from the cluster tree developed for each model from 100 to 1000 groups of jobs. Using previously made estimates of the probability (ordinal), intensity (µg m−3 respirable elemental carbon), and frequency (hours per week) of occupational exposure to diesel exhaust, we examined the similarity of the exposure estimates for jobs within the same cluster in two ways. First, the clusters’ homogeneity (defined as >75% with the same estimate

Cross-entropy clustering framework for catchment classification

NASA Astrophysics Data System (ADS)

Tongal, Hakan; Sivakumar, Bellie

2017-09-01

There is an increasing interest in catchment classification and regionalization in hydrology, as they are useful for identification of appropriate model complexity and transfer of information from gauged catchments to ungauged ones, among others. This study introduces a nonlinear cross-entropy clustering (CEC) method for classification of catchments. The method specifically considers embedding dimension (m), sample entropy (SampEn), and coefficient of variation (CV) to represent dimensionality, complexity, and variability of the time series, respectively. The method is applied to daily streamflow time series from 217 gauging stations across Australia. The results suggest that a combination of linear and nonlinear parameters (i.e. m, SampEn, and CV), representing different aspects of the underlying dynamics of streamflows, could be useful for determining distinct patterns of flow generation mechanisms within a nonlinear clustering framework. For the 217 streamflow time series, nine hydrologically homogeneous clusters that have distinct patterns of flow regime characteristics and specific dominant hydrological attributes with different climatic features are obtained. Comparison of the results with those obtained using the widely employed k-means clustering method (which results in five clusters, with the loss of some information about the features of the clusters) suggests the superiority of the cross-entropy clustering method. The outcomes from this study provide a useful guideline for employing the nonlinear dynamic approaches based on hydrologic signatures and for gaining an improved understanding of streamflow variability at a large scale.

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

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

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.

Using hierarchical cluster models to systematically identify groups of jobs with similar occupational questionnaire response patterns to assist rule-based expert exposure assessment in population-based studies.

PubMed

Friesen, Melissa C; Shortreed, Susan M; Wheeler, David C; Burstyn, Igor; Vermeulen, Roel; Pronk, Anjoeka; Colt, Joanne S; Baris, Dalsu; Karagas, Margaret R; Schwenn, Molly; Johnson, Alison; Armenti, Karla R; Silverman, Debra T; Yu, Kai

2015-05-01

Rule-based expert exposure assessment based on questionnaire response patterns in population-based studies improves the transparency of the decisions. The number of unique response patterns, however, can be nearly equal to the number of jobs. An expert may reduce the number of patterns that need assessment using expert opinion, but each expert may identify different patterns of responses that identify an exposure scenario. Here, hierarchical clustering methods are proposed as a systematic data reduction step to reproducibly identify similar questionnaire response patterns prior to obtaining expert estimates. As a proof-of-concept, we used hierarchical clustering methods to identify groups of jobs (clusters) with similar responses to diesel exhaust-related questions and then evaluated whether the jobs within a cluster had similar (previously assessed) estimates of occupational diesel exhaust exposure. Using the New England Bladder Cancer Study as a case study, we applied hierarchical cluster models to the diesel-related variables extracted from the occupational history and job- and industry-specific questionnaires (modules). Cluster models were separately developed for two subsets: (i) 5395 jobs with ≥1 variable extracted from the occupational history indicating a potential diesel exposure scenario, but without a module with diesel-related questions; and (ii) 5929 jobs with both occupational history and module responses to diesel-relevant questions. For each subset, we varied the numbers of clusters extracted from the cluster tree developed for each model from 100 to 1000 groups of jobs. Using previously made estimates of the probability (ordinal), intensity (µg m(-3) respirable elemental carbon), and frequency (hours per week) of occupational exposure to diesel exhaust, we examined the similarity of the exposure estimates for jobs within the same cluster in two ways. First, the clusters' homogeneity (defined as >75% with the same estimate) was examined compared

Resistance gene candidates identified by PCR with degenerate oligonucleotide primers map to clusters of resistance genes in lettuce.

PubMed

Shen, K A; Meyers, B C; Islam-Faridi, M N; Chin, D B; Stelly, D M; Michelmore, R W

1998-08-01

The recent cloning of genes for resistance against diverse pathogens from a variety of plants has revealed that many share conserved sequence motifs. This provides the possibility of isolating numerous additional resistance genes by polymerase chain reaction (PCR) with degenerate oligonucleotide primers. We amplified resistance gene candidates (RGCs) from lettuce with multiple combinations of primers with low degeneracy designed from motifs in the nucleotide binding sites (NBSs) of RPS2 of Arabidopsis thaliana and N of tobacco. Genomic DNA, cDNA, and bacterial artificial chromosome (BAC) clones were successfully used as templates. Four families of sequences were identified that had the same similarity to each other as to resistance genes from other species. The relationship of the amplified products to resistance genes was evaluated by several sequence and genetic criteria. The amplified products contained open reading frames with additional sequences characteristic of NBSs. Hybridization of RGCs to genomic DNA and to BAC clones revealed large numbers of related sequences. Genetic analysis demonstrated the existence of clustered multigene families for each of the four RGC sequences. This parallels classical genetic data on clustering of disease resistance genes. Two of the four families mapped to known clusters of resistance genes; these two families were therefore studied in greater detail. Additional evidence that these RGCs could be resistance genes was gained by the identification of leucine-rich repeat (LRR) regions in sequences adjoining the NBS similar to those in RPM1 and RPS2 of A. thaliana. Fluorescent in situ hybridization confirmed the clustered genomic distribution of these sequences. The use of PCR with degenerate oligonucleotide primers is therefore an efficient method to identify numerous RGCs in plants.

Identifying and ranking influential spreaders in complex networks by combining a local-degree sum and the clustering coefficient

NASA Astrophysics Data System (ADS)

Li, Mengtian; Zhang, Ruisheng; Hu, Rongjing; Yang, Fan; Yao, Yabing; Yuan, Yongna

2018-03-01

Identifying influential spreaders is a crucial problem that can help authorities to control the spreading process in complex networks. Based on the classical degree centrality (DC), several improved measures have been presented. However, these measures cannot rank spreaders accurately. In this paper, we first calculate the sum of the degrees of the nearest neighbors of a given node, and based on the calculated sum, a novel centrality named clustered local-degree (CLD) is proposed, which combines the sum and the clustering coefficients of nodes to rank spreaders. By assuming that the spreading process in networks follows the susceptible-infectious-recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performances between the CLD centrality and other six measures. The results show that the CLD centrality has a competitive performance in distinguishing the spreading ability of nodes, and exposes the best performance to identify influential spreaders accurately.

A Cluster Analysis of Tic Symptoms in Children and Adults with Tourette Syndrome: Clinical Correlates and Treatment Outcome

PubMed Central

McGuire, Joseph F.; Nyirabahizi, Epiphanie; Kircanski, Katharina; Piacentini, John; Peterson, Alan L.; Woods, Douglas W.; Wilhelm, Sabine; Walkup, John T.; Scahill, Lawrence

2013-01-01

Cluster analytic methods have examined the symptom presentation of chronic tic disorders (CTDs), with limited agreement across studies. The present study investigated patterns, clinical correlates, and treatment outcome of tic symptoms. 239 youth and adults with CTDs completed a battery of assessments at baseline to determine diagnoses, tic severity, and clinical characteristics. Participants were randomly assigned to receive either a comprehensive behavioral intervention for tics (CBIT) or psychoeducation and supportive therapy (PST). A cluster analysis was conducted on the baseline Yale Global Tic Severity Scale (YGTSS) symptom checklist to identify the constellations of tic symptoms. Four tic clusters were identified: Impulse Control and Complex Phonic Tics; Complex Motor Tics; Simple Head Motor/Vocal Tics; and Primarily Simple Motor Tics. Frequencies of tic symptoms showed few differences across youth and adults. Tic clusters had small associations with clinical characteristics and showed no associations to the presence of coexisting psychiatric conditions. Cluster membership scores did not predict treatment response to CBIT or tic severity reductions. Tic symptoms distinctly cluster with few difference across youth and adults, or coexisting conditions. This study, which is the first to examine tic clusters in relation to treatment, suggested that tic symptom profiles respond equally well to CBIT. PMID:24144615

Distribution of Suicin Gene Clusters in Streptococcus suis Serotype 2 Belonging to Sequence Types 25 and 28.

PubMed

Athey, Taryn B T; Vaillancourt, Katy; Frenette, Michel; Fittipaldi, Nahuel; Gottschalk, Marcelo; Grenier, Daniel

2016-01-01

Recently, we reported the purification and characterization of three distinct lantibiotics (named suicin 90-1330, suicin 3908, and suicin 65) produced by Streptococcus suis . In this study, we investigated the distribution of the three suicin lantibiotic gene clusters among serotype 2 S. suis strains belonging to sequence type (ST) 25 and ST28, the two dominant STs identified in North America. The genomes of 102 strains were interrogated for the presence of suicin gene clusters encoding suicins 90-1330, 3908, and 65. The gene cluster encoding suicin 65 was the most prevalent and mainly found among ST25 strains. In contrast, none of the genes related to suicin 90-1330 production were identified in 51 ST25 strains nor in 35/51 ST28 strains. However, the complete suicin 90-1330 gene cluster was found in ten ST28 strains, although some genes in the cluster were truncated in three of these isolates. The vast majority (101/102) of S. suis strains did not possess any of the genes encoding suicin 3908. In conclusion, this study indicates heterogeneous distribution of suicin genes in S. suis .

Module Cluster: UG - 001.00 (GSC) Urban Geography.

ERIC Educational Resources Information Center

Currier, Wade R.

This is one of several module clusters developed for the Camden Teacher Corps project. This module cluster is designed to introduce students to urban studies through the application of a geographic approach. Although geography shares with other social sciences many concepts and methods, it has contributed a distinctive set of viewpoints and a…

Poly-Small Ubiquitin-like Modifier (PolySUMO)-binding Proteins Identified through a String Search*

PubMed Central

Sun, Huaiyu; Hunter, Tony

2012-01-01

Polysumoylation is a crucial cellular response to stresses against genomic integrity or proteostasis. Like the small ubiquitin-like modifier (SUMO)-targeted ubiquitin ligase RNF4, proteins with clustered SUMO-interacting motifs (SIMs) can be important signal transducers downstream of polysumoylation. To identify novel polySUMO-binding proteins, we conducted a computational string search with a custom Python script. We found clustered SIMs in another RING domain protein Arkadia/RNF111. Detailed biochemical analysis of the Arkadia SIMs revealed that dominant SIMs in a SIM cluster often contain a pentameric VIDLT ((V/I/L/F/Y)(V/I)DLT) core sequence that is also found in the SIMs in PIAS family E3s and is likely the best-fitted structure for SUMO recognition. This idea led to the identification of additional novel SIM clusters in FLASH/CASP8AP2, C5orf25, and SOBP/JXC1. We suggest that the clustered SIMs in these proteins form distinct SUMO binding domains to recognize diverse forms of protein sumoylation. PMID:23086935

Exome sequencing identifies NFS1 deficiency in a novel Fe-S cluster disease, infantile mitochondrial complex II/III deficiency.

PubMed

Farhan, Sali M K; Wang, Jian; Robinson, John F; Lahiry, Piya; Siu, Victoria M; Prasad, Chitra; Kronick, Jonathan B; Ramsay, David A; Rupar, C Anthony; Hegele, Robert A

2014-01-01

Iron-sulfur (Fe-S) clusters are a class of highly conserved and ubiquitous prosthetic groups with unique chemical properties that allow the proteins that contain them, Fe-S proteins, to assist in various key biochemical pathways. Mutations in Fe-S proteins often disrupt Fe-S cluster assembly leading to a spectrum of severe disorders such as Friedreich's ataxia or iron-sulfur cluster assembly enzyme (ISCU) myopathy. Herein, we describe infantile mitochondrial complex II/III deficiency, a novel autosomal recessive mitochondrial disease characterized by lactic acidemia, hypotonia, respiratory chain complex II and III deficiency, multisystem organ failure and abnormal mitochondria. Through autozygosity mapping, exome sequencing, in silico analyses, population studies and functional tests, we identified c.215G>A, p.Arg72Gln in NFS1 as the likely causative mutation. We describe the first disease in man likely caused by deficiency in NFS1, a cysteine desulfurase that is implicated in respiratory chain function and iron maintenance by initiating Fe-S cluster biosynthesis. Our results further demonstrate the importance of sufficient NFS1 expression in human physiology.

Spatial clustering of metal and metalloid mixtures in unregulated water sources on the Navajo Nation - Arizona, New Mexico, and Utah, USA.

PubMed

Hoover, Joseph H; Coker, Eric; Barney, Yolanda; Shuey, Chris; Lewis, Johnnye

2018-08-15

Contaminant mixtures are identified regularly in public and private drinking water supplies throughout the United States; however, the complex and often correlated nature of mixtures makes identification of relevant combinations challenging. This study employed a Bayesian clustering method to identify subgroups of water sources with similar metal and metalloid profiles. Additionally, a spatial scan statistic assessed spatial clustering of these subgroups and a human health metric was applied to investigate potential for human toxicity. These methods were applied to a dataset comprised of metal and metalloid measurements from unregulated water sources located on the Navajo Nation, in the southwest United States. Results indicated distinct subgroups of water sources with similar contaminant profiles and that some of these subgroups were spatially clustered. Several profiles had metal and metalloid concentrations that may have potential for human toxicity including arsenic, uranium, lead, manganese, and selenium. This approach may be useful for identifying mixtures in water sources, spatially evaluating the clusters, and help inform toxicological research investigating mixtures. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

A 6-gene signature identifies four molecular subgroups of neuroblastoma

PubMed Central

2011-01-01

Background There are currently three postulated genomic subtypes of the childhood tumour neuroblastoma (NB); Type 1, Type 2A, and Type 2B. The most aggressive forms of NB are characterized by amplification of the oncogene MYCN (MNA) and low expression of the favourable marker NTRK1. Recently, mutations or high expression of the familial predisposition gene Anaplastic Lymphoma Kinase (ALK) was associated to unfavourable biology of sporadic NB. Also, various other genes have been linked to NB pathogenesis. Results The present study explores subgroup discrimination by gene expression profiling using three published microarray studies on NB (47 samples). Four distinct clusters were identified by Principal Components Analysis (PCA) in two separate data sets, which could be verified by an unsupervised hierarchical clustering in a third independent data set (101 NB samples) using a set of 74 discriminative genes. The expression signature of six NB-associated genes ALK, BIRC5, CCND1, MYCN, NTRK1, and PHOX2B, significantly discriminated the four clusters (p < 0.05, one-way ANOVA test). PCA clusters p1, p2, and p3 were found to correspond well to the postulated subtypes 1, 2A, and 2B, respectively. Remarkably, a fourth novel cluster was detected in all three independent data sets. This cluster comprised mainly 11q-deleted MNA-negative tumours with low expression of ALK, BIRC5, and PHOX2B, and was significantly associated with higher tumour stage, poor outcome and poor survival compared to the Type 1-corresponding favourable group (INSS stage 4 and/or dead of disease, p < 0.05, Fisher's exact test). Conclusions Based on expression profiling we have identified four molecular subgroups of neuroblastoma, which can be distinguished by a 6-gene signature. The fourth subgroup has not been described elsewhere, and efforts are currently made to further investigate this group's specific characteristics. PMID:21492432

Pressure of the hot gas in simulations of galaxy clusters

NASA Astrophysics Data System (ADS)

Planelles, S.; Fabjan, D.; Borgani, S.; Murante, G.; Rasia, E.; Biffi, V.; Truong, N.; Ragone-Figueroa, C.; Granato, G. L.; Dolag, K.; Pierpaoli, E.; Beck, A. M.; Steinborn, Lisa K.; Gaspari, M.

2017-06-01

We analyse the radial pressure profiles, the intracluster medium (ICM) clumping factor and the Sunyaev-Zel'dovich (SZ) scaling relations of a sample of simulated galaxy clusters and groups identified in a set of hydrodynamical simulations based on an updated version of the treepm-SPH GADGET-3 code. Three different sets of simulations are performed: the first assumes non-radiative physics, the others include, among other processes, active galactic nucleus (AGN) and/or stellar feedback. Our results are analysed as a function of redshift, ICM physics, cluster mass and cluster cool-coreness or dynamical state. In general, the mean pressure profiles obtained for our sample of groups and clusters show a good agreement with X-ray and SZ observations. Simulated cool-core (CC) and non-cool-core (NCC) clusters also show a good match with real data. We obtain in all cases a small (if any) redshift evolution of the pressure profiles of massive clusters, at least back to z = 1. We find that the clumpiness of gas density and pressure increases with the distance from the cluster centre and with the dynamical activity. The inclusion of AGN feedback in our simulations generates values for the gas clumping (√{C}_{ρ }˜ 1.2 at R200) in good agreement with recent observational estimates. The simulated YSZ-M scaling relations are in good accordance with several observed samples, especially for massive clusters. As for the scatter of these relations, we obtain a clear dependence on the cluster dynamical state, whereas this distinction is not so evident when looking at the subsamples of CC and NCC clusters.

A New Approach to Identify High Burnout Medical Staffs by Kernel K-Means Cluster Analysis in a Regional Teaching Hospital in Taiwan

PubMed Central

Lee, Yii-Ching; Huang, Shian-Chang; Huang, Chih-Hsuan; Wu, Hsin-Hung

2016-01-01

This study uses kernel k-means cluster analysis to identify medical staffs with high burnout. The data collected in October to November 2014 are from the emotional exhaustion dimension of the Chinese version of Safety Attitudes Questionnaire in a regional teaching hospital in Taiwan. The number of effective questionnaires including the entire staffs such as physicians, nurses, technicians, pharmacists, medical administrators, and respiratory therapists is 680. The results show that 8 clusters are generated by kernel k-means method. Employees in clusters 1, 4, and 5 are relatively in good conditions, whereas employees in clusters 2, 3, 6, 7, and 8 need to be closely monitored from time to time because they have relatively higher degree of burnout. When employees with higher degree of burnout are identified, the hospital management can take actions to improve the resilience, reduce the potential medical errors, and, eventually, enhance the patient safety. This study also suggests that the hospital management needs to keep track of medical staffs’ fatigue conditions and provide timely assistance for burnout recovery through employee assistance programs, mindfulness-based stress reduction programs, positivity currency buildup, and forming appreciative inquiry groups. PMID:27895218

A New Approach to Identify High Burnout Medical Staffs by Kernel K-Means Cluster Analysis in a Regional Teaching Hospital in Taiwan.

PubMed

Lee, Yii-Ching; Huang, Shian-Chang; Huang, Chih-Hsuan; Wu, Hsin-Hung

2016-01-01

This study uses kernel k-means cluster analysis to identify medical staffs with high burnout. The data collected in October to November 2014 are from the emotional exhaustion dimension of the Chinese version of Safety Attitudes Questionnaire in a regional teaching hospital in Taiwan. The number of effective questionnaires including the entire staffs such as physicians, nurses, technicians, pharmacists, medical administrators, and respiratory therapists is 680. The results show that 8 clusters are generated by kernel k-means method. Employees in clusters 1, 4, and 5 are relatively in good conditions, whereas employees in clusters 2, 3, 6, 7, and 8 need to be closely monitored from time to time because they have relatively higher degree of burnout. When employees with higher degree of burnout are identified, the hospital management can take actions to improve the resilience, reduce the potential medical errors, and, eventually, enhance the patient safety. This study also suggests that the hospital management needs to keep track of medical staffs' fatigue conditions and provide timely assistance for burnout recovery through employee assistance programs, mindfulness-based stress reduction programs, positivity currency buildup, and forming appreciative inquiry groups. © The Author(s) 2016.

Differential expression of vesicular glutamate transporters 1 and 2 may identify distinct modes of glutamatergic transmission in the macaque visual system

PubMed Central

Balaram, Pooja; Hackett, Troy A.; Kaas, Jon H.

2013-01-01

Glutamate is the primary neurotransmitter utilized by the mammalian visual system for excitatory neurotransmission. The sequestration of glutamate into synaptic vesicles, and the subsequent transport of filled vesicles to the presynaptic terminal membrane, is regulated by a family of proteins known as vesicular glutamate transporters (VGLUTs). Two VGLUT proteins, VGLUT1 and VGLUT2, characterize distinct sets of glutamatergic projections between visual structures in rodents and prosimian primates, yet little is known about their distributions in the visual system of anthropoid primates. We have examined the mRNA and protein expression patterns of VGLUT1 and VGLUT2 in the visual system of macaque monkeys, an Old World anthropoid primate, in order to determine their relative distributions in the superior colliculus, lateral geniculate nucleus, pulvinar complex, V1 and V2. Distinct expression patterns for both VGLUT1 and VGLUT2 identified architectonic boundaries in all structures, as well as anatomical subdivisions of the superior colliculus, pulvinar complex, and V1. These results suggest that VGLUT1 and VGLUT2 clearly identify regions of glutamatergic input in visual structures, and may identify common architectonic features of visual areas and nuclei across the primate radiation. Additionally, we find that VGLUT1 and VGLUT2 characterize distinct subsets of glutamatergic projections in the macaque visual system; VGLUT2 predominates in driving or feedforward projections from lower order to higher order visual structures while VGLUT1 predominates in modulatory or feedback projections from higher order to lower order visual structures. The distribution of these two proteins suggests that VGLUT1 and VGLUT2 may identify class 1 and class 2 type glutamatergic projections within the primate visual system (Sherman and Guillery, 2006). PMID:23524295

Differential expression of vesicular glutamate transporters 1 and 2 may identify distinct modes of glutamatergic transmission in the macaque visual system.

PubMed

Balaram, Pooja; Hackett, Troy A; Kaas, Jon H

2013-05-01

Glutamate is the primary neurotransmitter utilized by the mammalian visual system for excitatory neurotransmission. The sequestration of glutamate into synaptic vesicles, and the subsequent transport of filled vesicles to the presynaptic terminal membrane, is regulated by a family of proteins known as vesicular glutamate transporters (VGLUTs). Two VGLUT proteins, VGLUT1 and VGLUT2, characterize distinct sets of glutamatergic projections between visual structures in rodents and prosimian primates, yet little is known about their distributions in the visual system of anthropoid primates. We have examined the mRNA and protein expression patterns of VGLUT1 and VGLUT2 in the visual system of macaque monkeys, an Old World anthropoid primate, in order to determine their relative distributions in the superior colliculus, lateral geniculate nucleus, pulvinar complex, V1 and V2. Distinct expression patterns for both VGLUT1 and VGLUT2 identified architectonic boundaries in all structures, as well as anatomical subdivisions of the superior colliculus, pulvinar complex, and V1. These results suggest that VGLUT1 and VGLUT2 clearly identify regions of glutamatergic input in visual structures, and may identify common architectonic features of visual areas and nuclei across the primate radiation. Additionally, we find that VGLUT1 and VGLUT2 characterize distinct subsets of glutamatergic projections in the macaque visual system; VGLUT2 predominates in driving or feedforward projections from lower order to higher order visual structures while VGLUT1 predominates in modulatory or feedback projections from higher order to lower order visual structures. The distribution of these two proteins suggests that VGLUT1 and VGLUT2 may identify class 1 and class 2 type glutamatergic projections within the primate visual system (Sherman and Guillery, 2006). Copyright © 2013 Elsevier B.V. All rights reserved.

COVARIATE-ADAPTIVE CLUSTERING OF EXPOSURES FOR AIR POLLUTION EPIDEMIOLOGY COHORTS*

PubMed Central

Keller, Joshua P.; Drton, Mathias; Larson, Timothy; Kaufman, Joel D.; Sandler, Dale P.; Szpiro, Adam A.

2017-01-01

Cohort studies in air pollution epidemiology aim to establish associations between health outcomes and air pollution exposures. Statistical analysis of such associations is complicated by the multivariate nature of the pollutant exposure data as well as the spatial misalignment that arises from the fact that exposure data are collected at regulatory monitoring network locations distinct from cohort locations. We present a novel clustering approach for addressing this challenge. Specifically, we present a method that uses geographic covariate information to cluster multi-pollutant observations and predict cluster membership at cohort locations. Our predictive k-means procedure identifies centers using a mixture model and is followed by multi-class spatial prediction. In simulations, we demonstrate that predictive k-means can reduce misclassification error by over 50% compared to ordinary k-means, with minimal loss in cluster representativeness. The improved prediction accuracy results in large gains of 30% or more in power for detecting effect modification by cluster in a simulated health analysis. In an analysis of the NIEHS Sister Study cohort using predictive k-means, we find that the association between systolic blood pressure (SBP) and long-term fine particulate matter (PM2.5) exposure varies significantly between different clusters of PM2.5 component profiles. Our cluster-based analysis shows that for subjects assigned to a cluster located in the Midwestern U.S., a 10 μg/m3 difference in exposure is associated with 4.37 mmHg (95% CI, 2.38, 6.35) higher SBP. PMID:28572869

Clinical Phenotype of Diabetic Peripheral Neuropathy and Relation to Symptom Patterns: Cluster and Factor Analysis in Patients with Type 2 Diabetes in Korea.

PubMed

Won, Jong Chul; Im, Yong-Jin; Lee, Ji-Hyun; Kim, Chong Hwa; Kwon, Hyuk Sang; Cha, Bong-Yun; Park, Tae Sun

2017-01-01

Patients with diabetic peripheral neuropathy (DPN) is the most common complication. However, patients are usually suffering from not only diverse sensory deficit but also neuropathy-related discomforts. The aim of this study is to identify distinct groups of patients with DPN with respect to its clinical impacts on symptom patterns and comorbidities. A hierarchical cluster analysis and factor analysis were performed to identify relevant subgroups of patients with DPN ( n = 1338) and symptom patterns. Patients with DPN were divided into three clusters: asymptomatic (cluster 1, n = 448, 33.5%), moderate symptoms with disturbed sleep (cluster 2, n = 562, 42.0%), and severe symptoms with decreased quality of life (cluster 3, n = 328, 24.5%). Patients in cluster 3, compared with clusters 1 and 2, were characterized by higher levels of HbA1c and more severe pain and physical impairments. Patients in cluster 2 had moderate pain levels but disturbed sleep patterns comparable to those in cluster 3. The frequency of symptoms on each item of MNSI by "painful" symptom pattern showed a similar distribution pattern with increasing intensities along the three clusters. Cluster and factor analysis endorsed the use of comprehensive and symptomatic subgrouping to individualize the evaluation of patients with DPN.

Identifying patterns of general practitioner service utilisation and their relationship with potentially preventable hospitalisations in people with diabetes: The utility of a cluster analysis approach.

PubMed

Ha, Ninh Thi; Harris, Mark; Preen, David; Robinson, Suzanne; Moorin, Rachael

2018-04-01

We aimed to characterise use of general practitioners (GP) simultaneously across multiple attributes in people with diabetes and examine its impact on diabetes related potentially preventable hospitalisations (PPHs). Five-years of panel data from 40,625 adults with diabetes were sourced from Western Australian administrative health records. Cluster analysis (CA) was used to group individuals with similar patterns of GP utilisation characterised by frequency and recency of services. The relationship between GP utilisation cluster and the risk of PPHs was examined using multivariable random-effects negative binomial regression. CA categorised GP utilisation into three clusters: moderate; high and very high usage, having distinct patient characteristics. After adjusting for potential confounders, the rate of PPHs was significantly lower across all GP usage clusters compared with those with no GP usage; IRR = 0.67 (95%CI: 0.62-0.71) among the moderate, IRR = 0.70 (95%CI 0.66-0.73) high and IRR = 0.76 (95%CI 0.72-0.80) very high GP usage clusters. Combination of temporal factors with measures of frequency of use of GP services revealed patterns of primary health care utilisation associated with different underlying patient characteristics. Incorporation of multiple attributes, that go beyond frequency-based approaches may better characterise the complex relationship between use of GP services and diabetes-related hospitalisation. Copyright © 2018 Elsevier B.V. All rights reserved.

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

Identifying models of HIV care and treatment service delivery in Tanzania, Uganda, and Zambia using cluster analysis and Delphi survey.

PubMed

Tsui, Sharon; Denison, Julie A; Kennedy, Caitlin E; Chang, Larry W; Koole, Olivier; Torpey, Kwasi; Van Praag, Eric; Farley, Jason; Ford, Nathan; Stuart, Leine; Wabwire-Mangen, Fred

2017-12-06

Organization of HIV care and treatment services, including clinic staffing and services, may shape clinical and financial outcomes, yet there has been little attempt to describe different models of HIV care in sub-Saharan Africa (SSA). Information about the relative benefits and drawbacks of different models could inform the scale-up of antiretroviral therapy (ART) and associated services in resource-limited settings (RLS), especially in light of expanded client populations with country adoption of WHO's test and treat recommendation. We characterized task-shifting/task-sharing practices in 19 diverse ART clinics in Tanzania, Uganda, and Zambia and used cluster analysis to identify unique models of service provision. We ran descriptive statistics to explore how the clusters varied by environmental factors and programmatic characteristics. Finally, we employed the Delphi Method to make systematic use of expert opinions to ensure that the cluster variables were meaningful in the context of actual task-shifting of ART services in SSA. The cluster analysis identified three task-shifting/task-sharing models. The main differences across models were the availability of medical doctors, the scope of clinical responsibility assigned to nurses, and the use of lay health care workers. Patterns of healthcare staffing in HIV service delivery were associated with different environmental factors (e.g., health facility levels, urban vs. rural settings) and programme characteristics (e.g., community ART distribution or integrated tuberculosis treatment on-site). Understanding the relative advantages and disadvantages of different models of care can help national programmes adapt to increased client load, select optimal adherence strategies within decentralized models of care, and identify differentiated models of care for clients to meet the growing needs of long-term ART patients who require more complicated treatment management.

Identifying common traits among Australian irrigators using cluster analysis.

PubMed

Kuehne, G; Bjornlund, H; Cheers, B

2008-01-01

In Australia there is a growing awareness that the over-allocation of water entitlements to irrigators needs to be reduced so that environmental flow allocations can be increased. This means that some water will need to be acquired from irrigators and returned to the environment. Most current water reform policies assume that irrigators are solely motivated by profit and will be willing sellers of water, but this might be an untenable approach. Authorities will need to consider new ways of encouraging the participation of irrigators in water reform. The main aim of this research was to identify the non-commercial influences acting on irrigators' behaviour, especially the influence of the values that they hold toward family, land, water, community and lifestyle. The study also aimed to investigate whether it is possible to group irrigators according to these values and then use the groupings to describe how these might affect their willingness to participate in environmental reforms. We clustered the irrigators into three groups with differing orientations; (i) Investors [25%]-profit oriented, (ii) Lifestylers [25%]-lifestyle oriented, (iii) Providers [50%]-family-succession oriented. This research indicates that when designing policy instruments to acquire water for environmental purposes policy-makers should pay more attention to the factors influencing irrigators' decision making, especially non-commercial factors. (c) IWA Publishing 2008.

Membrane lipid patterns typify distinct anaerobic methanotrophic consortia

PubMed Central

Blumenberg, Martin; Seifert, Richard; Reitner, Joachim; Pape, Thomas; Michaelis, Walter

2004-01-01

The anaerobic oxidation of methane (AOM) is one of the major sinks of this substantial greenhouse gas in marine environments. Recent investigations have shown that diverse communities of anaerobic archaea and sulfate-reducing bacteria are involved in AOM. Most of the relevant archaea are assigned to two distinct phylogenetic clusters, ANME-1 and ANME-2. A suite of specific 13C-depleted lipids demonstrating the presence of consortia mediating AOM in fossil and recent environments has been established. Here we report on substantial differences in the lipid composition of microbial consortia sampled from distinct compartments of AOM-driven carbonate reefs growing in the northwestern Black Sea. Communities in which the dominant archaea are from the ANME-1 cluster yield internally cyclized tetraether lipids typical of thermophiles. Those in which ANME-2 archaea are dominant yield sn-2-hydroxyarchaeol accompanied by crocetane and crocetenes. The bacterial lipids from these communities are also distinct even though the sulfate-reducing bacteria all belong to the Desulfosarcina/Desulfococcus group. Nonisoprenoidal glycerol diethers are predominantly associated with ANME-1-dominated communities. Communities with ANME-2 yield mainly conventional, ester-linked diglycerides. ANME-1 archaea and associated sulfate-reducing bacteria seem to be enabled to use low concentrations of methane and to grow within a broad range of temperatures. Our results offer a tool for the study of recent and especially of fossil methane environments. PMID:15258285

Genomic characterization of a new endophytic Streptomyces kebangsaanensis identifies biosynthetic pathway gene clusters for novel phenazine antibiotic production

PubMed Central

Remali, Juwairiah; Sarmin, Nurul ‘Izzah Mohd; Ng, Chyan Leong; Tiong, John J.L.; Aizat, Wan M.; Keong, Loke Kok

2017-01-01

Background Streptomyces are well known for their capability to produce many bioactive secondary metabolites with medical and industrial importance. Here we report a novel bioactive phenazine compound, 6-((2-hydroxy-4-methoxyphenoxy) carbonyl) phenazine-1-carboxylic acid (HCPCA) extracted from Streptomyces kebangsaanensis, an endophyte isolated from the ethnomedicinal Portulaca oleracea. Methods The HCPCA chemical structure was determined using nuclear magnetic resonance spectroscopy. We conducted whole genome sequencing for the identification of the gene cluster(s) believed to be responsible for phenazine biosynthesis in order to map its corresponding pathway, in addition to bioinformatics analysis to assess the potential of S. kebangsaanensis in producing other useful secondary metabolites. Results The S. kebangsaanensis genome comprises an 8,328,719 bp linear chromosome with high GC content (71.35%) consisting of 12 rRNA operons, 81 tRNA, and 7,558 protein coding genes. We identified 24 gene clusters involved in polyketide, nonribosomal peptide, terpene, bacteriocin, and siderophore biosynthesis, as well as a gene cluster predicted to be responsible for phenazine biosynthesis. Discussion The HCPCA phenazine structure was hypothesized to derive from the combination of two biosynthetic pathways, phenazine-1,6-dicarboxylic acid and 4-methoxybenzene-1,2-diol, originated from the shikimic acid pathway. The identification of a biosynthesis pathway gene cluster for phenazine antibiotics might facilitate future genetic engineering design of new synthetic phenazine antibiotics. Additionally, these findings confirm the potential of S. kebangsaanensis for producing various antibiotics and secondary metabolites. PMID:29201559

The open cluster IC 4665

NASA Technical Reports Server (NTRS)

Prosser, Charles F.

1993-01-01

The results of a combined astrometric, photometric, and spectroscopic program to identify members of the open cluster IC 4665 are presented. Numerous new proper motion/photometric candidate members and at least 23 M dwarfs with H-alpha emission have been identified. A reanalysis of IC 4665 age using different methods yields conflicting results ranging from about 3 X 10 exp 7 yr to the age of the Pleiades. This study provides a list of candidate cluster members in the intermediate and low-mass regime of this cluster. Future spectroscopic observations of these candidates should eventually identify true cluster members.

Sca-1 Identifies a Distinct Androgen-Independent Murine Prostatic Luminal Cell Lineage with Bipotent Potential

PubMed Central

Kwon, Oh-Joon; Zhang, Li; Xin, Li

2016-01-01

Recent lineage tracing studies support the existence of prostate luminal progenitors that possess extensive regenerative capacity, but their identity remains unknown. We show that Sca-1 (Stem Cell Antigen-1) identifies a small population of murine prostate luminal cells that reside in the proximal prostatic ducts adjacent to the urethra. Sca-1+ luminal cells do not express Nkx3.1. They do not carry the secretory function, although they express the androgen receptor. These cells are enriched in the prostates of castrated mice. In the in vitro prostate organoid assay, a small fraction of the Sca-1+ luminal cells are capable of generating budding organoids that are morphologically distinct from those derived from other cell lineages. Histologically, this type of organoid is composed of multiple inner layers of luminal cells surrounded by multiple outer layers of basal cells. When passaged, these organoids retain their morphological and histological features. Finally, the Sca-1+ luminal cells are capable of forming small prostate glands containing both basal and luminal cells in an in vivo prostate regeneration assay. Collectively, our study establishes the androgen-independent and bipotent organoid-forming Sca-1+ luminal cells as a functionally distinct cellular entity. These cells may represent a putative luminal progenitor population and serve as a cellular origin for castration resistant prostate cancer. PMID:26418304

Distinct pathological profiles of inmates showcasing cluster B personality traits, mental disorders and substance use regarding violent behaviors.

PubMed

Dellazizzo, Laura; Dugré, Jules R; Berwald, Marieke; Stafford, Marie-Christine; Côté, Gilles; Potvin, Stéphane; Dumais, Alexandre

2017-12-06

High rates of violence are found amid offenders with severe mental illnesses (SMI), substance use disorders (SUDs) and Cluster B personality disorders. Elevated rates of comorbidity lead to inconsistencies when it comes to this relationship. Furthermore, overlapping Cluster B personality traits have been associated with violence. Using multiple correspondence analysis and cluster analysis, this study was designed to differentiate profiles of 728 male inmates from penitentiary and psychiatric settings marked by personality traits, SMI and SUDs following different violent patterns. Six significantly differing clusters emerged. Cluster 1, "Sensation seekers", presented recklessness with SUDs and low prevalence's of SMI and auto-aggression. Two clusters committed more sexual offenses. While Cluster 2, "Opportunistic-sexual offenders", had more antisocial lifestyles and SUDs, Cluster 6, "Emotional-sexual offenders", displayed more emotional disturbances with SMI and violence. Clusters 3 and 4, representing "Life-course-persistent offenders", shared early signs of persistent antisocial conduct and severe violence. Cluster 3, "Early-onset violent delinquents", emerged as more severely antisocial with SUDs. Cluster 4, "Early-onset unstable-mentally ill delinquents", were more emotionally driven, with SMI and auto-aggression. Cluster 5, "Late-start offenders", was less severely violent, and emotionally driven with antisocial behavior beginning later. This study suggests the presence of specific psychopathological organizations in violent inmates. Copyright © 2017 Elsevier B.V. All rights reserved.

Vibrio cholerae O1 El Tor from southern Vietnam in 2010 was molecularly distinct from that present from 1999 to 2004.

PubMed

Nguyen, V H; Pham, H T; Diep, T T; Phan, C D H; Nguyen, T Q; Nguyen, N T N; Ngo, T C; Nguyen, T V; Do, Q K; Phan, H C; Nguyen, B M; Ehara, M; Ohnishi, M; Yamashiro, T; Nguyen, L T P; Izumiya, H

2016-04-01

The Vibrio cholerae O1 (VCO1) El Tor biotype appeared during the seventh cholera pandemic starting in 1961, and new variants of this biotype have been identified since the early 1990s. This pandemic has affected Vietnam, and a large outbreak was reported in southern Vietnam in 2010. Pulsed-field gel electrophoresis (PFGE) and multilocus variable-number tandem-repeat analyses (MLVA) were used to screen 34 VCO1 isolates from the southern Vietnam 2010 outbreak (23 patients, five contact persons, and six environmental isolates) to determine if it was genetically distinct from 18 isolates from outbreaks in southern Vietnam from 1999 to 2004, and two isolates from northern Vietnam (2008). Twenty-seven MLVA types and seven PFGE patterns were identified. Both analyses showed that the 2008 and 2010 isolates were distinctly clustered and separated from the 1999-2004 isolates.

FACTOR ANALYTIC MODELS OF CLUSTERED MULTIVARIATE DATA WITH INFORMATIVE CENSORING

EPA Science Inventory

This paper describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censorin...

Review of methods for handling confounding by cluster and informative cluster size in clustered data

PubMed Central

Seaman, Shaun; Pavlou, Menelaos; Copas, Andrew

2014-01-01

Clustered data are common in medical research. Typically, one is interested in a regression model for the association between an outcome and covariates. Two complications that can arise when analysing clustered data are informative cluster size (ICS) and confounding by cluster (CBC). ICS and CBC mean that the outcome of a member given its covariates is associated with, respectively, the number of members in the cluster and the covariate values of other members in the cluster. Standard generalised linear mixed models for cluster-specific inference and standard generalised estimating equations for population-average inference assume, in general, the absence of ICS and CBC. Modifications of these approaches have been proposed to account for CBC or ICS. This article is a review of these methods. We express their assumptions in a common format, thus providing greater clarity about the assumptions that methods proposed for handling CBC make about ICS and vice versa, and about when different methods can be used in practice. We report relative efficiencies of methods where available, describe how methods are related, identify a previously unreported equivalence between two key methods, and propose some simple additional methods. Unnecessarily using a method that allows for ICS/CBC has an efficiency cost when ICS and CBC are absent. We review tools for identifying ICS/CBC. A strategy for analysis when CBC and ICS are suspected is demonstrated by examining the association between socio-economic deprivation and preterm neonatal death in Scotland. PMID:25087978

Mass Cytometry Identifies Distinct Lung CD4+ T Cell Patterns in Löfgren’s Syndrome and Non-Löfgren’s Syndrome Sarcoidosis

PubMed Central

Kaiser, Ylva; Lakshmikanth, Tadepally; Chen, Yang; Mikes, Jaromir; Eklund, Anders; Brodin, Petter; Achour, Adnane; Grunewald, Johan

2017-01-01

Sarcoidosis is a granulomatous disorder of unknown etiology, characterized by accumulation of activated CD4+ T cells in the lungs. Disease phenotypes Löfgren’s syndrome (LS) and “non-LS” differ in terms of clinical manifestations, genetic background, HLA association, and prognosis, but the underlying inflammatory mechanisms largely remain unknown. Bronchoalveolar lavage fluid cells from four HLA-DRB1*03+ LS and four HLA-DRB1*03− non-LS patients were analyzed by mass cytometry, using a panel of 33 unique markers. Differentially regulated CD4+ T cell populations were identified using the Citrus algorithm, and t-stochastic neighborhood embedding was applied for dimensionality reduction and single-cell data visualization. We identified 19 individual CD4+ T cell clusters differing significantly in abundance between LS and non-LS patients. Seven clusters more frequent in LS patients were characterized by significantly higher expression of regulatory receptors CTLA-4, PD-1, and ICOS, along with low expression of adhesion marker CD44. In contrast, 12 clusters primarily found in non-LS displayed elevated expression of activation and effector markers HLA-DR, CD127, CD39, as well as CD44. Hierarchical clustering further indicated functional heterogeneity and diverse origins of T cell receptor Vα2.3/Vβ22-restricted cells in LS. Finally, a near-complete overlap of CD8 and Ki-67 expression suggested larger influence of CD8+ T cell activity on sarcoid inflammation than previously appreciated. In this study, we provide detailed characterization of pulmonary T cells and immunological parameters that define separate disease pathways in LS and non-LS. With direct association to clinical parameters, such as granuloma persistence, resolution, or chronic inflammation, these results provide a valuable foundation for further exploration and potential clinical application. PMID:28955342

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

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

Similar bowtie structures and distinct largest strong components are identified in the transcriptional regulatory networks of Arabidopsis thaliana during photomorphogenesis and heat shock.

PubMed

Luo, Shitao; Zhang, Fengming; Ruan, Yingfei; Li, Jie; Zhang, Zheng; Sun, Yan; Deng, Shixiong; Peng, Rui

2018-06-01

Photomorphogenesis and heat shock are critical biological processes of plants. A recent research constructed the transcriptional regulatory networks (TRNs) of Arabidopsis thaliana during these processes using DNase-seq. In this study, by strong decomposition, we revealed that each of these TRNs can be represented as a similar bowtie structure with only one non-trivial and distinct strong component. We further identified distinct patterns of variation of a few light-related genes in these bowtie structures during photomorphogenesis. These results suggest that bowtie structure may be a common property of TRNs of plants, and distinct variation patterns of genes in bowtie structures of TRNs during biological processes may reflect distinct functions. Overall, our study provides an insight into the molecular mechanisms underlying photomorphogenesis and heat shock, and emphasizes the necessity to investigate the strong connectivity structures while studying TRNs. Copyright © 2018 Elsevier B.V. All rights reserved.

Bipolar disorder with comorbid cluster B personality disorder features: impact on suicidality.

PubMed

Garno, Jessica L; Goldberg, Joseph F; Ramirez, Paul Michael; Ritzler, Barry A

2005-03-01

Because of their overlapping phenomenology and mutually chronic, persistent nature, distinctions between bipolar disorder and cluster B personality disorders remain a source of unresolved clinical controversy. The extent to which comorbid personality disorders impact course and outcome for bipolar patients also has received little systematic study. One hundred DSM-IV bipolar I (N = 73) or II (N = 27) patients consecutively underwent diagnostic evaluations with structured clinical interviews for DSM-IV Axis I and cluster B Axis II disorders, along with assessments of histories of childhood trauma or abuse. Cluster B diagnostic comorbidity was examined relative to lifetime substance abuse, suicide attempt histories, and other clinical features. Thirty percent of subjects met DSM-IV criteria for a cluster B personality disorder (17% borderline, 6% antisocial, 5% histrionic, 8% narcissistic). Cluster B diagnoses were significantly linked with histories of childhood emotional abuse (p = .009), physical abuse (p = .014), and emotional neglect (p = .022), but not sexual abuse or physical neglect. Cluster B comorbidity was associated with significantly more lifetime suicide attempts and current depression. Lifetime suicide attempts were significantly associated with cluster B comorbidity (OR = 3.195, 95% CI = 1.124 to 9.088), controlling for current depression severity, lifetime substance abuse, and past sexual or emotional abuse. Cluster B personality disorders are prevalent comorbid conditions identifiable in a substantial number of individuals with bipolar disorder, making an independent contribution to increased lifetime suicide risk.

The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies

NASA Astrophysics Data System (ADS)

Grasha, K.; Calzetti, D.; Adamo, A.; Kim, H.; Elmegreen, B. G.; Gouliermis, D. A.; Dale, D. A.; Fumagalli, M.; Grebel, E. K.; Johnson, K. E.; Kahre, L.; Kennicutt, R. C.; Messa, M.; Pellerin, A.; Ryon, J. E.; Smith, L. J.; Shabani, F.; Thilker, D.; Ubeda, L.

2017-05-01

We present a study of the hierarchical clustering of the young stellar clusters in six local (3-15 Mpc) star-forming galaxies using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey). We identified 3685 likely clusters and associations, each visually classified by their morphology, and we use the angular two-point correlation function to study the clustering of these stellar systems. We find that the spatial distribution of the young clusters and associations are clustered with respect to each other, forming large, unbound hierarchical star-forming complexes that are in general very young. The strength of the clustering decreases with increasing age of the star clusters and stellar associations, becoming more homogeneously distributed after ˜40-60 Myr and on scales larger than a few hundred parsecs. In all galaxies, the associations exhibit a global behavior that is distinct and more strongly correlated from compact clusters. Thus, populations of clusters are more evolved than associations in terms of their spatial distribution, traveling significantly from their birth site within a few tens of Myr, whereas associations show evidence of disruption occurring very quickly after their formation. The clustering of the stellar systems resembles that of a turbulent interstellar medium that drives the star formation process, correlating the components in unbound star-forming complexes in a hierarchical manner, dispersing shortly after formation, suggestive of a single, continuous mode of star formation across all galaxies.

Diversity of nonribosomal peptide synthetase and polyketide synthase gene clusters among taxonomically close Streptomyces strains.

PubMed

Komaki, Hisayuki; Sakurai, Kenta; Hosoyama, Akira; Kimura, Akane; Igarashi, Yasuhiro; Tamura, Tomohiko

2018-05-02

To identify the species of butyrolactol-producing Streptomyces strain TP-A0882, whole genome-sequencing of three type strains in a close taxonomic relationship was performed. In silico DNA-DNA hybridization using the genome sequences suggested that Streptomyces sp. TP-A0882 is classified as Streptomyces diastaticus subsp. ardesiacus. Strain TP-A0882, S. diastaticus subsp. ardesiacus NBRC 15402 T , Streptomyces coelicoflavus NBRC 15399 T , and Streptomyces rubrogriseus NBRC 15455 T harbor at least 14, 14, 10, and 12 biosynthetic gene clusters (BGCs), respectively, coding for nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs). All 14 gene clusters were shared by S. diastaticus subsp. ardesiacus strains TP-A0882 and NBRC 15402 T , while only four gene clusters were shared by the three distinct species. Although BGCs for bacteriocin, ectoine, indole, melanine, siderophores such as deferrioxamine, terpenes such as albaflavenone, hopene, carotenoid and geosmin are shared by the three species, many BGCs for secondary metabolites such as butyrolactone, lantipeptides, oligosaccharide, some terpenes are species-specific. These results indicate the possibility that strains belonging to the same species possess the same set of secondary metabolite-biosynthetic pathways, whereas strains belonging to distinct species have species-specific pathways, in addition to some common pathways, even if the strains are taxonomically close.

An EST-based analysis identifies new genes and reveals distinctive gene expression features of Coffea arabica and Coffea canephora

PubMed Central

2011-01-01

Background Coffee is one of the world's most important crops; it is consumed worldwide and plays a significant role in the economy of producing countries. Coffea arabica and C. canephora are responsible for 70 and 30% of commercial production, respectively. C. arabica is an allotetraploid from a recent hybridization of the diploid species, C. canephora and C. eugenioides. C. arabica has lower genetic diversity and results in a higher quality beverage than C. canephora. Research initiatives have been launched to produce genomic and transcriptomic data about Coffea spp. as a strategy to improve breeding efficiency. Results Assembling the expressed sequence tags (ESTs) of C. arabica and C. canephora produced by the Brazilian Coffee Genome Project and the Nestlé-Cornell Consortium revealed 32,007 clusters of C. arabica and 16,665 clusters of C. canephora. We detected different GC3 profiles between these species that are related to their genome structure and mating system. BLAST analysis revealed similarities between coffee and grape (Vitis vinifera) genes. Using KA/KS analysis, we identified coffee genes under purifying and positive selection. Protein domain and gene ontology analyses suggested differences between Coffea spp. data, mainly in relation to complex sugar synthases and nucleotide binding proteins. OrthoMCL was used to identify specific and prevalent coffee protein families when compared to five other plant species. Among the interesting families annotated are new cystatins, glycine-rich proteins and RALF-like peptides. Hierarchical clustering was used to independently group C. arabica and C. canephora expression clusters according to expression data extracted from EST libraries, resulting in the identification of differentially expressed genes. Based on these results, we emphasize gene annotation and discuss plant defenses, abiotic stress and cup quality-related functional categories. Conclusion We present the first comprehensive genome-wide transcript

[Prognostic differences of phenotypes in pT1-2N0 invasive breast cancer: a large cohort study with cluster analysis].

PubMed

Wang, Z; Wang, W H; Wang, S L; Jin, J; Song, Y W; Liu, Y P; Ren, H; Fang, H; Tang, Y; Chen, B; Qi, S N; Lu, N N; Li, N; Tang, Y; Liu, X F; Yu, Z H; Li, Y X

2016-06-23

To find phenotypic subgroups of patients with pT1-2N0 invasive breast cancer by means of cluster analysis and estimate the prognosis and clinicopathological features of these subgroups. From 1999 to 2013, 4979 patients with pT1-2N0 invasive breast cancer were recruited for hierarchical clustering analysis. Age (≤40, 41-70, 70+ years), size of primary tumor, pathological type, grade of differentiation, microvascular invasion, estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER-2) were chosen as distance metric between patients. Hierarchical cluster analysis was performed using Ward's method. Cophenetic correlation coefficient (CPCC) and Spearman correlation coefficient were used to validate clustering structures. The CPCC was 0.603. The Spearman correlation coefficient was 0.617 (P<0.001), which indicated a good fit of hierarchy to the data. A twelve-cluster model seemed to best illustrate our patient cohort. Patients in cluster 5, 9 and 12 had best prognosis and were characterized by age >40 years, smaller primary tumor, lower histologic grade, positive ER and PR status, and mainly negative HER-2. Patients in the cluster 1 and 11 had the worst prognosis, The cluster 1 was characterized by a larger tumor, higher grade and negative ER and PR status, while the cluster 11 was characterized by positive microvascular invasion. Patients in other 7 clusters had a moderate prognosis, and patients in each cluster had distinctive clinicopathological features and recurrent patterns. This study identified distinctive clinicopathologic phenotypes in a large cohort of patients with pT1-2N0 breast cancer through hierarchical clustering and revealed different prognosis. This integrative model may help physicians to make more personalized decisions regarding adjuvant therapy.

Gene expression profiling reveals distinct molecular signatures associated with the rupture of intracranial aneurysm.

PubMed

Nakaoka, Hirofumi; Tajima, Atsushi; Yoneyama, Taku; Hosomichi, Kazuyoshi; Kasuya, Hidetoshi; Mizutani, Tohru; Inoue, Ituro

2014-08-01

The rupture of intracranial aneurysm (IA) causes subarachnoid hemorrhage associated with high morbidity and mortality. We compared gene expression profiles in aneurysmal domes between unruptured IAs and ruptured IAs (RIAs) to elucidate biological mechanisms predisposing to the rupture of IA. We determined gene expression levels of 8 RIAs, 5 unruptured IAs, and 10 superficial temporal arteries with the Agilent microarrays. To explore biological heterogeneity of IAs, we classified the samples into subgroups showing similar gene expression patterns, using clustering methods. The clustering analysis identified 4 groups: superficial temporal arteries and unruptured IAs were aggregated into their own clusters, whereas RIAs segregated into 2 distinct subgroups (early and late RIAs). Comparing gene expression levels between early RIAs and unruptured IAs, we identified 430 upregulated and 617 downregulated genes in early RIAs. The upregulated genes were associated with inflammatory and immune responses and phagocytosis including S100/calgranulin genes (S100A8, S100A9, and S100A12). The downregulated genes suggest mechanical weakness of aneurysm walls. The expressions of Krüppel-like family of transcription factors (KLF2, KLF12, and KLF15), which were anti-inflammatory regulators, and CDKN2A, which was located on chromosome 9p21 that was the most consistently replicated locus in genome-wide association studies of IA, were also downregulated. We demonstrate that gene expression patterns of RIAs were different according to the age of patients. The results suggest that macrophage-mediated inflammation is a key biological pathway for IA rupture. The identified genes can be good candidates for molecular markers of rupture-prone IAs and therapeutic targets. © 2014 American Heart Association, Inc.

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.

The HectoMAP Cluster Survey. II. X-Ray Clusters

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

Sohn, Jubee; Chon, Gayoung; Bohringer, Hans

Here, we apply a friends-of-friends algorithm to the HectoMAP redshift survey and cross-identify associated X-ray emission in the ROSAT All-Sky Survey data (RASS). The resulting flux-limited catalog of X-ray cluster surveys is complete to a limiting flux of ~3 × 10 –13 erg s –1 cm –2 and includes 15 clusters (7 newly discovered) with redshifts z ≤ 0.4. HectoMAP is a dense survey (~1200 galaxies deg –2) that provides ~50 members (median) in each X-ray cluster. We provide redshifts for the 1036 cluster members. Subaru/Hyper Suprime-Cam imaging covers three of the X-ray systems and confirms that they are impressivemore » clusters. The HectoMAP X-ray clusters have an L X–σ cl scaling relation similar to that of known massive X-ray clusters. The HectoMAP X-ray cluster sample predicts ~12,000 ± 3000 detectable X-ray clusters in RASS to the limiting flux, comparable with previous estimates.« less

The HectoMAP Cluster Survey. II. X-Ray Clusters

DOE PAGES

Sohn, Jubee; Chon, Gayoung; Bohringer, Hans; ...

2018-03-10

Here, we apply a friends-of-friends algorithm to the HectoMAP redshift survey and cross-identify associated X-ray emission in the ROSAT All-Sky Survey data (RASS). The resulting flux-limited catalog of X-ray cluster surveys is complete to a limiting flux of ~3 × 10 –13 erg s –1 cm –2 and includes 15 clusters (7 newly discovered) with redshifts z ≤ 0.4. HectoMAP is a dense survey (~1200 galaxies deg –2) that provides ~50 members (median) in each X-ray cluster. We provide redshifts for the 1036 cluster members. Subaru/Hyper Suprime-Cam imaging covers three of the X-ray systems and confirms that they are impressivemore » clusters. The HectoMAP X-ray clusters have an L X–σ cl scaling relation similar to that of known massive X-ray clusters. The HectoMAP X-ray cluster sample predicts ~12,000 ± 3000 detectable X-ray clusters in RASS to the limiting flux, comparable with previous estimates.« less

Effects of Cluster Environment on Chemical Abundances in Virgo Cluster Spirals

NASA Astrophysics Data System (ADS)

Kennicutt, R. C.; Skillman, E. D.; Shields, G. A.; Zaritsky, D.

1995-12-01

We have obtained new chemical abundance measurements of HII regions in Virgo cluster spiral galaxies, in order to test whether the cluster environment has significantly influenced the gas-phase abundances and chemical evolution of spiral disks. The sample of 9 Virgo spirals covers a narrow range of morphological type (Sbc - Sc) but shows broad ranges in HI deficiencies and radii in the cluster. This allows us to compare the Virgo sample as a whole to field spirals, using a large sample from Zaritsky, Kennicutt, & Huchra, and to test for systematic trends with HI content and location within the cluster. The Virgo spirals show a wide dispersion in mean disk abundances and abundance gradients. Strongly HI deficient spirals closest to the cluster core show anomalously high oxygen abundances (by 0.3 to 0.5 dex), while outlying spirals with normal HI content show abundances similar to those of field spirals. The most HI depleted spirals also show weaker abundance gradients on average, but the formal significance of this trend is marginal. We find a strong correlation between mean abundance and HI/optical diameter ratio that is quite distinct from the behavior seen in field galaxies. This suggests that dynamical processes associated with the cluster environment are more important than cluster membership in determining the evolution of chemical abundances and stellar populations in spiral galaxies. Simple chemical evolution models are calculated to predict the magnitude of the abundance enhancement expected if ram-pressure stripping or curtailment of infall is responsible for the gas deficiencies. The increased abundances of the spirals in the cluster core may have significant effects on their use as cosmological standard candles.

Clustering the Orion B giant molecular cloud based on its molecular emission

NASA Astrophysics Data System (ADS)

Bron, Emeric; Daudon, Chloé; Pety, Jérôme; Levrier, François; Gerin, Maryvonne; Gratier, Pierre; Orkisz, Jan H.; Guzman, Viviana; Bardeau, Sébastien; Goicoechea, Javier R.; Liszt, Harvey; Öberg, Karin; Peretto, Nicolas; Sievers, Albrecht; Tremblin, Pascal

2018-02-01

identified, likely related to the higher critical density of the CN and HCO+ (1-0) lines. These distinctions are only possible because the high-density regions are spatially resolved. Conclusions: Molecules are versatile tracers of GMCs because their line intensities bear the signature of the physics and chemistry at play in the gas. The association of simultaneous multi-line, wide-field mapping and powerful machine learning methods such as the Meanshift clustering algorithm reveals how to decode the complex information available in these molecular tracers. Data products associated with this paper are 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/610/A12 and at http://www.iram.fr/ pety/ORION-B

Do Clustering Monoclonal Antibody Solutions Really Have a Concentration Dependence of Viscosity?

PubMed Central

Pathak, Jai A.; Sologuren, Rumi R.; Narwal, Rojaramani

2013-01-01

Protein solution rheology data in the biophysics literature have incompletely identified factors that govern hydrodynamics. Whereas spontaneous protein adsorption at the air/water (A/W) interface increases the apparent viscosity of surfactant-free globular protein solutions, it is demonstrated here that irreversible clusters also increase system viscosity in the zero shear limit. Solution rheology measured with double gap geometry in a stress-controlled rheometer on a surfactant-free Immunoglobulin solution demonstrated that both irreversible clusters and the A/W interface increased the apparent low shear rate viscosity. Interfacial shear rheology data showed that the A/W interface yields, i.e., shows solid-like behavior. The A/W interface contribution was smaller, yet nonnegligible, in double gap compared to cone-plate geometry. Apparent nonmonotonic composition dependence of viscosity at low shear rates due to irreversible (nonequilibrium) clusters was resolved by filtration to recover a monotonically increasing viscosity-concentration curve, as expected. Although smaller equilibrium clusters also existed, their size and effective volume fraction were unaffected by filtration, rendering their contribution to viscosity invariant. Surfactant-free antibody systems containing clusters have complex hydrodynamic response, reflecting distinct bulk and interface-adsorbed protein as well as irreversible cluster contributions. Literature models for solution viscosity lack the appropriate physics to describe the bulk shear viscosity of unstable surfactant-free antibody solutions. PMID:23442970

Two Distinct Patterns of Clostridium Difficile Diversity Across Europe Indicates Contrasting Routes of Spread.

PubMed

Eyre, David W; Davies, Kerrie A; Davis, Georgina; Fawley, Warren N; Dingle, Kate E; De Maio, Nicola; Karas, Andreas; Crook, Derrick W; Peto, Tim E A; Walker, A Sarah; Wilcox, Mark H

2018-04-06

Rates of Clostridium difficile infection vary widely across Europe, as do prevalent ribotypes. The extent of Europe-wide diversity within each ribotype is however unknown. Inpatient diarrhoeal faecal samples submitted on one day in summer and winter (2012-2013) to laboratories in 482 European hospitals were cultured for C. difficile, and isolates ribotyped; those from the 10 most prevalent ribotypes were Illumina whole-genome sequenced. Pairwise single nucleotide differences (SNPs) were obtained from recombination-corrected maximum-likelihood phylogenies. Within each ribotype, country-based sequence clustering was assessed using the ratio of the median SNPs between isolates within versus across different countries using permutation tests. Time-scaled Bayesian phylogenies where used to reconstruct the historic location of each lineage. Sequenced isolates (n=624) were from 19 countries. Five ribotypes had within-country clustering: ribotype-356, only in Italy; ribotype-018, predominantly in Italy; ribotype-176, with distinct Czech and German clades; ribotype-001/072, including distinct German, Slovakian, and Spanish clades; and ribotype-027, with multiple predominantly country-specific clades including in Hungary, Italy, Germany, Romania and Poland. By contrast, we found no within-country clustering for ribotypes 078, 015, 002, 014, and 020, consistent with a Europe-wide distribution. Fluoroquinolone-resistance was significantly more common in within-country clustered ribotypes (p=0.009). Fluoroquinolone-resistant isolates were also more tightly geographically clustered, median (IQR) 43 (0-213) miles between each isolate and the most closely genetically-related isolate vs. 421 (204-680) in non-resistant pairs (p<0.001). Two distinct patterns of C. difficile ribotype spread were observed, consistent with either predominantly healthcare-associated acquisition or Europe-wide dissemination via other routes/sources, e.g. the food chain.

Joint-specific DNA methylation and transcriptome signatures in rheumatoid arthritis identify distinct pathogenic processes

PubMed Central

Ai, Rizi; Hammaker, Deepa; Boyle, David L.; Morgan, Rachel; Walsh, Alice M.; Fan, Shicai; Firestein, Gary S.; Wang, Wei

2016-01-01

Stratifying patients on the basis of molecular signatures could facilitate development of therapeutics that target pathways specific to a particular disease or tissue location. Previous studies suggest that pathogenesis of rheumatoid arthritis (RA) is similar in all affected joints. Here we show that distinct DNA methylation and transcriptome signatures not only discriminate RA fibroblast-like synoviocytes (FLS) from osteoarthritis FLS, but also distinguish RA FLS isolated from knees and hips. Using genome-wide methods, we show differences between RA knee and hip FLS in the methylation of genes encoding biological pathways, such as IL-6 signalling via JAK-STAT pathway. Furthermore, differentially expressed genes are identified between knee and hip FLS using RNA-sequencing. Double-evidenced genes that are both differentially methylated and expressed include multiple HOX genes. Joint-specific DNA signatures suggest that RA disease mechanisms might vary from joint to joint, thus potentially explaining some of the diversity of drug responses in RA patients. PMID:27282753

Comprehensive Expression Map of Transcription Regulators in the Adult Zebrafish Telencephalon Reveals Distinct Neurogenic Niches

PubMed Central

Diotel, Nicolas; Rodriguez Viales, Rebecca; Armant, Olivier; März, Martin; Ferg, Marco; Rastegar, Sepand; Strähle, Uwe

2015-01-01

The zebrafish has become a model to study adult vertebrate neurogenesis. In particular, the adult telencephalon has been an intensely studied structure in the zebrafish brain. Differential expression of transcriptional regulators (TRs) is a key feature of development and tissue homeostasis. Here we report an expression map of 1,202 TR genes in the telencephalon of adult zebrafish. Our results are summarized in a database with search and clustering functions to identify genes expressed in particular regions of the telencephalon. We classified 562 genes into 13 distinct patterns, including genes expressed in the proliferative zone. The remaining 640 genes displayed unique and complex patterns of expression and could thus not be grouped into distinct classes. The neurogenic ventricular regions express overlapping but distinct sets of TR genes, suggesting regional differences in the neurogenic niches in the telencephalon. In summary, the small telencephalon of the zebrafish shows a remarkable complexity in TR gene expression. The adult zebrafish telencephalon has become a model to study neurogenesis. We established the expression pattern of more than 1200 transcription regulators (TR) in the adult telencephalon. The neurogenic regions express overlapping but distinct sets of TR genes suggesting regional differences in the neurogenic potential. J. Comp. Neurol. 523:1202–1221, 2015. © 2015 Wiley Periodicals, Inc. PMID:25556858

Comprehensive expression map of transcription regulators in the adult zebrafish telencephalon reveals distinct neurogenic niches.

PubMed

Diotel, Nicolas; Rodriguez Viales, Rebecca; Armant, Olivier; März, Martin; Ferg, Marco; Rastegar, Sepand; Strähle, Uwe

2015-06-01

The zebrafish has become a model to study adult vertebrate neurogenesis. In particular, the adult telencephalon has been an intensely studied structure in the zebrafish brain. Differential expression of transcriptional regulators (TRs) is a key feature of development and tissue homeostasis. Here we report an expression map of 1,202 TR genes in the telencephalon of adult zebrafish. Our results are summarized in a database with search and clustering functions to identify genes expressed in particular regions of the telencephalon. We classified 562 genes into 13 distinct patterns, including genes expressed in the proliferative zone. The remaining 640 genes displayed unique and complex patterns of expression and could thus not be grouped into distinct classes. The neurogenic ventricular regions express overlapping but distinct sets of TR genes, suggesting regional differences in the neurogenic niches in the telencephalon. In summary, the small telencephalon of the zebrafish shows a remarkable complexity in TR gene expression. The adult zebrafish telencephalon has become a model to study neurogenesis. We established the expression pattern of more than 1200 transcription regulators (TR) in the adult telencephalon. The neurogenic regions express overlapping but distinct sets of TR genes suggesting regional differences in the neurogenic potential. © 2015 Wiley Periodicals, Inc.

A proximity-based graph clustering method for the identification and application of transcription factor clusters.

PubMed

Spadafore, Maxwell; Najarian, Kayvan; Boyle, Alan P

2017-11-29

Transcription factors (TFs) form a complex regulatory network within the cell that is crucial to cell functioning and human health. While methods to establish where a TF binds to DNA are well established, these methods provide no information describing how TFs interact with one another when they do bind. TFs tend to bind the genome in clusters, and current methods to identify these clusters are either limited in scope, unable to detect relationships beyond motif similarity, or not applied to TF-TF interactions. Here, we present a proximity-based graph clustering approach to identify TF clusters using either ChIP-seq or motif search data. We use TF co-occurrence to construct a filtered, normalized adjacency matrix and use the Markov Clustering Algorithm to partition the graph while maintaining TF-cluster and cluster-cluster interactions. We then apply our graph structure beyond clustering, using it to increase the accuracy of motif-based TFBS searching for an example TF. We show that our method produces small, manageable clusters that encapsulate many known, experimentally validated transcription factor interactions and that our method is capable of capturing interactions that motif similarity methods might miss. Our graph structure is able to significantly increase the accuracy of motif TFBS searching, demonstrating that the TF-TF connections within the graph correlate with biological TF-TF interactions. The interactions identified by our method correspond to biological reality and allow for fast exploration of TF clustering and regulatory dynamics.

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.

Testing the Large-scale Environments of Cool-core and Non-cool-core Clusters with Clustering Bias

NASA Astrophysics Data System (ADS)

Medezinski, Elinor; Battaglia, Nicholas; Coupon, Jean; Cen, Renyue; Gaspari, Massimo; Strauss, Michael A.; Spergel, David N.

2017-02-01

There are well-observed differences between cool-core (CC) and non-cool-core (NCC) clusters, but the origin of this distinction is still largely unknown. Competing theories can be divided into internal (inside-out), in which internal physical processes transform or maintain the NCC phase, and external (outside-in), in which the cluster type is determined by its initial conditions, which in turn leads to different formation histories (I.e., assembly bias). We propose a new method that uses the relative assembly bias of CC to NCC clusters, as determined via the two-point cluster-galaxy cross-correlation function (CCF), to test whether formation history plays a role in determining their nature. We apply our method to 48 ACCEPT clusters, which have well resolved central entropies, and cross-correlate with the SDSS-III/BOSS LOWZ galaxy catalog. We find that the relative bias of NCC over CC clusters is b = 1.42 ± 0.35 (1.6σ different from unity). Our measurement is limited by the small number of clusters with core entropy information within the BOSS footprint, 14 CC and 34 NCC clusters. Future compilations of X-ray cluster samples, combined with deep all-sky redshift surveys, will be able to better constrain the relative assembly bias of CC and NCC clusters and determine the origin of the bimodality.

Testing the Large-scale Environments of Cool-core and Non-cool-core Clusters with Clustering Bias

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

Medezinski, Elinor; Battaglia, Nicholas; Cen, Renyue

2017-02-10

There are well-observed differences between cool-core (CC) and non-cool-core (NCC) clusters, but the origin of this distinction is still largely unknown. Competing theories can be divided into internal (inside-out), in which internal physical processes transform or maintain the NCC phase, and external (outside-in), in which the cluster type is determined by its initial conditions, which in turn leads to different formation histories (i.e., assembly bias). We propose a new method that uses the relative assembly bias of CC to NCC clusters, as determined via the two-point cluster-galaxy cross-correlation function (CCF), to test whether formation history plays a role in determiningmore » their nature. We apply our method to 48 ACCEPT clusters, which have well resolved central entropies, and cross-correlate with the SDSS-III/BOSS LOWZ galaxy catalog. We find that the relative bias of NCC over CC clusters is b = 1.42 ± 0.35 (1.6 σ different from unity). Our measurement is limited by the small number of clusters with core entropy information within the BOSS footprint, 14 CC and 34 NCC clusters. Future compilations of X-ray cluster samples, combined with deep all-sky redshift surveys, will be able to better constrain the relative assembly bias of CC and NCC clusters and determine the origin of the bimodality.« less

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.

Psychophysiological whole-brain network clustering based on connectivity dynamics analysis in naturalistic conditions.

PubMed

Raz, Gal; Shpigelman, Lavi; Jacob, Yael; Gonen, Tal; Benjamini, Yoav; Hendler, Talma

2016-12-01

We introduce a novel method for delineating context-dependent functional brain networks whose connectivity dynamics are synchronized with the occurrence of a specific psychophysiological process of interest. In this method of context-related network dynamics analysis (CRNDA), a continuous psychophysiological index serves as a reference for clustering the whole-brain into functional networks. We applied CRNDA to fMRI data recorded during the viewing of a sadness-inducing film clip. The method reliably demarcated networks in which temporal patterns of connectivity related to the time series of reported emotional intensity. Our work successfully replicated the link between network connectivity and emotion rating in an independent sample group for seven of the networks. The demarcated networks have clear common functional denominators. Three of these networks overlap with distinct empathy-related networks, previously identified in distinct sets of studies. The other networks are related to sensorimotor processing, language, attention, and working memory. The results indicate that CRNDA, a data-driven method for network clustering that is sensitive to transient connectivity patterns, can productively and reliably demarcate networks that follow psychologically meaningful processes. Hum Brain Mapp 37:4654-4672, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Aggregates and Superaggregates of Soot with Four Distinct Fractal Morphologies

NASA Technical Reports Server (NTRS)

Sorensen, C. M.; Kim, W.; Fry, D.; Chakrabarti, A.

2004-01-01

Soot formed in laminar diffusion flames of heavily sooting fuels evolves through four distinct growth stages which give rise to four distinct aggregate fractal morphologies. These results were inferred from large and small angle static light scattering from the flames, microphotography of the flames, and analysis of soot sampled from the flames. The growth stages occur approximately over four successive orders of magnitude in aggregate size. Comparison to computer simulations suggests that these four growth stages involve either diffusion limited cluster aggregation or percolation in either three or two dimensions.

Relationship between Distinct African Cholera Epidemics Revealed via MLVA Haplotyping of 337 Vibrio cholerae Isolates.

PubMed

Moore, Sandra; Miwanda, Berthe; Sadji, Adodo Yao; Thefenne, Hélène; Jeddi, Fakhri; Rebaudet, Stanislas; de Boeck, Hilde; Bidjada, Bawimodom; Depina, Jean-Jacques; Bompangue, Didier; Abedi, Aaron Aruna; Koivogui, Lamine; Keita, Sakoba; Garnotel, Eric; Plisnier, Pierre-Denis; Ruimy, Raymond; Thomson, Nicholas; Muyembe, Jean-Jacques; Piarroux, Renaud

2015-01-01

Since cholera appeared in Africa during the 1970s, cases have been reported on the continent every year. In Sub-Saharan Africa, cholera outbreaks primarily cluster at certain hotspots including the African Great Lakes Region and West Africa. In this study, we applied MLVA (Multi-Locus Variable Number Tandem Repeat Analysis) typing of 337 Vibrio cholerae isolates from recent cholera epidemics in the Democratic Republic of the Congo (DRC), Zambia, Guinea and Togo. We aimed to assess the relationship between outbreaks. Applying this method, we identified 89 unique MLVA haplotypes across our isolate collection. MLVA typing revealed the short-term divergence and microevolution of these Vibrio cholerae populations to provide insight into the dynamics of cholera outbreaks in each country. Our analyses also revealed strong geographical clustering. Isolates from the African Great Lakes Region (DRC and Zambia) formed a closely related group, while West African isolates (Togo and Guinea) constituted a separate cluster. At a country-level scale our analyses revealed several distinct MLVA groups, most notably DRC 2011/2012, DRC 2009, Zambia 2012 and Guinea 2012. We also found that certain MLVA types collected in the DRC persisted in the country for several years, occasionally giving rise to expansive epidemics. Finally, we found that the six environmental isolates in our panel were unrelated to the epidemic isolates. To effectively combat the disease, it is critical to understand the mechanisms of cholera emergence and diffusion in a region-specific manner. Overall, these findings demonstrate the relationship between distinct epidemics in West Africa and the African Great Lakes Region. This study also highlights the importance of monitoring and analyzing Vibrio cholerae isolates.

Single-cell RNA-sequencing reveals a distinct population of proglucagon-expressing cells specific to the mouse upper small intestine.

PubMed

Glass, Leslie L; Calero-Nieto, Fernando J; Jawaid, Wajid; Larraufie, Pierre; Kay, Richard G; Göttgens, Berthold; Reimann, Frank; Gribble, Fiona M

2017-10-01

To identify sub-populations of intestinal preproglucagon-expressing (PPG) cells producing Glucagon-like Peptide-1, and their associated expression profiles of sensory receptors, thereby enabling the discovery of therapeutic strategies that target these cell populations for the treatment of diabetes and obesity. We performed single cell RNA sequencing of PPG-cells purified by flow cytometry from the upper small intestine of 3 GLU-Venus mice. Cells from 2 mice were sequenced at low depth, and from the third mouse at high depth. High quality sequencing data from 234 PPG-cells were used to identify clusters by tSNE analysis. qPCR was performed to compare the longitudinal and crypt/villus locations of cluster-specific genes. Immunofluorescence and mass spectrometry were used to confirm protein expression. PPG-cells formed 3 major clusters: a group with typical characteristics of classical L-cells, including high expression of Gcg and Pyy (comprising 51% of all PPG-cells); a cell type overlapping with Gip-expressing K-cells (14%); and a unique cluster expressing Tph1 and Pzp that was predominantly located in proximal small intestine villi and co-produced 5-HT (35%). Expression of G-protein coupled receptors differed between clusters, suggesting the cell types are differentially regulated and would be differentially targetable. Our findings support the emerging concept that many enteroendocrine cell populations are highly overlapping, with individual cells producing a range of peptides previously assigned to distinct cell types. Different receptor expression profiles across the clusters highlight potential drug targets to increase gut hormone secretion for the treatment of diabetes and obesity. Copyright © 2017 The Authors. Published by Elsevier GmbH.. All rights reserved.

The smart cluster method. Adaptive earthquake cluster identification and analysis in strong seismic regions

NASA Astrophysics Data System (ADS)

Schaefer, Andreas M.; Daniell, James E.; Wenzel, Friedemann

2017-07-01

Earthquake clustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation for probabilistic seismic hazard assessment. This study introduces the Smart Cluster Method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal cluster identification. It utilises the magnitude-dependent spatio-temporal earthquake density to adjust the search properties, subsequently analyses the identified clusters to determine directional variation and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010-2011 Darfield-Christchurch sequence, a reclassification procedure is applied to disassemble subsequent ruptures using near-field searches, nearest neighbour classification and temporal splitting. The method is capable of identifying and classifying earthquake clusters in space and time. It has been tested and validated using earthquake data from California and New Zealand. A total of more than 1500 clusters have been found in both regions since 1980 with M m i n = 2.0. Utilising the knowledge of cluster classification, the method has been adjusted to provide an earthquake declustering algorithm, which has been compared to existing methods. Its performance is comparable to established methodologies. The analysis of earthquake clustering statistics lead to various new and updated correlation functions, e.g. for ratios between mainshock and strongest aftershock and general aftershock activity metrics.

Analysis of correlated mutations in HIV-1 protease using spectral clustering.

PubMed

Liu, Ying; Eyal, Eran; Bahar, Ivet

2008-05-15

The ability of human immunodeficiency virus-1 (HIV-1) protease to develop mutations that confer multi-drug resistance (MDR) has been a major obstacle in designing rational therapies against HIV. Resistance is usually imparted by a cooperative mechanism that can be elucidated by a covariance analysis of sequence data. Identification of such correlated substitutions of amino acids may be obscured by evolutionary noise. HIV-1 protease sequences from patients subjected to different specific treatments (set 1), and from untreated patients (set 2) were subjected to sequence covariance analysis by evaluating the mutual information (MI) between all residue pairs. Spectral clustering of the resulting covariance matrices disclosed two distinctive clusters of correlated residues: the first, observed in set 1 but absent in set 2, contained residues involved in MDR acquisition; and the second, included those residues differentiated in the various HIV-1 protease subtypes, shortly referred to as the phylogenetic cluster. The MDR cluster occupies sites close to the central symmetry axis of the enzyme, which overlap with the global hinge region identified from coarse-grained normal-mode analysis of the enzyme structure. The phylogenetic cluster, on the other hand, occupies solvent-exposed and highly mobile regions. This study demonstrates (i) the possibility of distinguishing between the correlated substitutions resulting from neutral mutations and those induced by MDR upon appropriate clustering analysis of sequence covariance data and (ii) a connection between global dynamics and functional substitution of amino acids.

Nonclinical and Clinical Enterococcus faecium Strains, but Not Enterococcus faecalis Strains, Have Distinct Structural and Functional Genomic Features

PubMed Central

Kim, Eun Bae

2014-01-01

Certain strains of Enterococcus faecium and Enterococcus faecalis contribute beneficially to animal health and food production, while others are associated with nosocomial infections. To determine whether there are structural and functional genomic features that are distinct between nonclinical (NC) and clinical (CL) strains of those species, we analyzed the genomes of 31 E. faecium and 38 E. faecalis strains. Hierarchical clustering of 7,017 orthologs found in the E. faecium pangenome revealed that NC strains clustered into two clades and are distinct from CL strains. NC E. faecium genomes are significantly smaller than CL genomes, and this difference was partly explained by significantly fewer mobile genetic elements (ME), virulence factors (VF), and antibiotic resistance (AR) genes. E. faecium ortholog comparisons identified 68 and 153 genes that are enriched for NC and CL strains, respectively. Proximity analysis showed that CL-enriched loci, and not NC-enriched loci, are more frequently colocalized on the genome with ME. In CL genomes, AR genes are also colocalized with ME, and VF are more frequently associated with CL-enriched loci. Genes in 23 functional groups are also differentially enriched between NC and CL E. faecium genomes. In contrast, differences were not observed between NC and CL E. faecalis genomes despite their having larger genomes than E. faecium. Our findings show that unlike E. faecalis, NC and CL E. faecium strains are equipped with distinct structural and functional genomic features indicative of adaptation to different environments. PMID:24141120

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

Discovery of the Kinematic Alignment of Early-type Galaxies in the Virgo Cluster

NASA Astrophysics Data System (ADS)

Kim, Suk; Jeong, Hyunjin; Lee, Jaehyun; Lee, Youngdae; Joo, Seok-Joo; Kim, Hak-Sub; Rey, Soo-Chang

2018-06-01

Using the kinematic position angles (PAkin), an accurate indicator for the spin axis of a galaxy, obtained from the ATLAS3D integral-field-unit (IFU) spectroscopic data, we discovered that 57 Virgo early-type galaxies tend to prefer the specific PAkin values of 20° and 100°, suggesting that they are kinematically aligned with each other. These kinematic alignment angles are further associated with the directions of the two distinct axes of the Virgo cluster extending east–west and north–south, strongly suggesting that the two distinct axes are the filamentary structures within the cluster as a trace of infall patterns of galaxies. Given that the spin axis of a massive early-type galaxy does not change easily even in clusters from the hydrodynamic simulations, Virgo early-type galaxies are likely to fall into the cluster along the filamentary structures while maintaining their angular momentum. This implies that many early-type galaxies in clusters are formed in filaments via major mergers before subsequently falling into the cluster. Investigating the kinematic alignment in other clusters will allow us to understand the formation of galaxy clusters and early-type galaxies.

Clustering Financial Time Series by Network Community Analysis

NASA Astrophysics Data System (ADS)

Piccardi, Carlo; Calatroni, Lisa; Bertoni, Fabio

In this paper, we describe a method for clustering financial time series which is based on community analysis, a recently developed approach for partitioning the nodes of a network (graph). A network with N nodes is associated to the set of N time series. The weight of the link (i, j), which quantifies the similarity between the two corresponding time series, is defined according to a metric based on symbolic time series analysis, which has recently proved effective in the context of financial time series. Then, searching for network communities allows one to identify groups of nodes (and then time series) with strong similarity. A quantitative assessment of the significance of the obtained partition is also provided. The method is applied to two distinct case-studies concerning the US and Italy Stock Exchange, respectively. In the US case, the stability of the partitions over time is also thoroughly investigated. The results favorably compare with those obtained with the standard tools typically used for clustering financial time series, such as the minimal spanning tree and the hierarchical tree.

Identification of Urban Leprosy Clusters

PubMed Central

Paschoal, José Antonio Armani; Paschoal, Vania Del'Arco; Nardi, Susilene Maria Tonelli; Rosa, Patrícia Sammarco; Ismael, Manuela Gallo y Sanches; Sichieri, Eduvaldo Paulo

2013-01-01

Overpopulation of urban areas results from constant migrations that cause disordered urban growth, constituting clusters defined as sets of people or activities concentrated in relatively small physical spaces that often involve precarious conditions. Aim. Using residential grouping, the aim was to identify possible clusters of individuals in São José do Rio Preto, Sao Paulo, Brazil, who have or have had leprosy. Methods. A population-based, descriptive, ecological study using the MapInfo and CrimeStat techniques, geoprocessing, and space-time analysis evaluated the location of 425 people treated for leprosy between 1998 and 2010. Clusters were defined as concentrations of at least 8 people with leprosy; a distance of up to 300 meters between residences was adopted. Additionally, the year of starting treatment and the clinical forms of the disease were analyzed. Results. Ninety-eight (23.1%) of 425 geocoded cases were located within one of ten clusters identified in this study, and 129 cases (30.3%) were in the region of a second-order cluster, an area considered of high risk for the disease. Conclusion. This study identified ten clusters of leprosy cases in the city and identified an area of high risk for the appearance of new cases of the disease. PMID:24288467

The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies

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

Grasha, K.; Calzetti, D.; Adamo, A.

We present a study of the hierarchical clustering of the young stellar clusters in six local (3–15 Mpc) star-forming galaxies using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey). We identified 3685 likely clusters and associations, each visually classified by their morphology, and we use the angular two-point correlation function to study the clustering of these stellar systems. We find that the spatial distribution of the young clusters and associations are clustered with respect to each other, forming large, unbound hierarchical star-forming complexes that are in general very young. Themore » strength of the clustering decreases with increasing age of the star clusters and stellar associations, becoming more homogeneously distributed after ∼40–60 Myr and on scales larger than a few hundred parsecs. In all galaxies, the associations exhibit a global behavior that is distinct and more strongly correlated from compact clusters. Thus, populations of clusters are more evolved than associations in terms of their spatial distribution, traveling significantly from their birth site within a few tens of Myr, whereas associations show evidence of disruption occurring very quickly after their formation. The clustering of the stellar systems resembles that of a turbulent interstellar medium that drives the star formation process, correlating the components in unbound star-forming complexes in a hierarchical manner, dispersing shortly after formation, suggestive of a single, continuous mode of star formation across all galaxies.« less

Methanethiol chemistry on TiO 2-supported Ni clusters

NASA Astrophysics Data System (ADS)

Ozturk, O.; Park, J. B.; Black, T. J.; Rodriguez, J. A.; Hrbek, J.; Chen, D. A.

2008-10-01

The thermal decomposition of methanethiol on Ni clusters grown on TiO 2(1 1 0) was studied by temperature programmed desorption (TPD), X-ray photoelectron spectroscopy (XPS) and low energy ion scattering (LEIS). On all of the Ni surfaces investigated, methane and hydrogen were observed as gaseous products in the TPD experiments, and the only sulfur-containing species that desorbed from the surface was methanethiol itself at low temperatures. The two pathways for methanethiol reaction were hydrodesulfurization to produce methane and nonselective decomposition, which leaves atomic carbon and sulfur on the surface. From high resolution XPS studies, methyl thiolate was identified as the surface intermediate for reaction on TiO 2 and on all of the Ni surfaces investigated, similar to what is observed on single-crystal Ni surfaces. However, the binding sites for methyl thiolate on the 1 ML (monolayer) Ni clusters were different from those on the Ni clusters at coverages of 2.5 ML and higher, based on the S(2p) binding energies for methyl thiolate. No distinct changes in activity or selectivity were observed for the smaller Ni clusters grown at low coverage compared to the more film-like Ni surfaces other than what could be accounted for by changes in total surface area. Interactions between the Ni clusters and the TiO 2 support had two main effects on chemical activity. First, carbon was oxidized by oxygen from the TiO 2 lattice to produce CO at temperatures above 800 K. Second, annealing induced encapsulation of the Ni clusters by reduced TiO x and chemisorbed oxygen. At 800 K, the Ni clusters were totally encapsulated, resulting in a complete loss of methanethiol activity; partial encapsulation at 700 K caused a smaller decrease in activity accompanied by increased oxidation of carbon by lattice oxygen.

Clustervision: Visual Supervision of Unsupervised Clustering.

PubMed

Kwon, Bum Chul; Eysenbach, Ben; Verma, Janu; Ng, Kenney; De Filippi, Christopher; Stewart, Walter F; Perer, Adam

2018-01-01

Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.

Quasi-Likelihood Techniques in a Logistic Regression Equation for Identifying Simulium damnosum s.l. Larval Habitats Intra-cluster Covariates in Togo.

PubMed

Jacob, Benjamin G; Novak, Robert J; Toe, Laurent; Sanfo, Moussa S; Afriyie, Abena N; Ibrahim, Mohammed A; Griffith, Daniel A; Unnasch, Thomas R

2012-01-01

The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l. a major black-fly vector of Onchoceriasis, postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects. Generally, this correlation comes from two sources: (1) the design of the random effects and their assumed covariance from the multiple levels within the regression model; and, (2) the correlation structure of the residuals. Unfortunately, inconspicuous errors in residual intra-cluster correlation estimates can overstate precision in forecasted S.damnosum s.l. riverine larval habitat explanatory attributes regardless how they are treated (e.g., independent, autoregressive, Toeplitz, etc). In this research, the geographical locations for multiple riverine-based S. damnosum s.l. larval ecosystem habitats sampled from 2 pre-established epidemiological sites in Togo were identified and recorded from July 2009 to June 2010. Initially the data was aggregated into proc genmod. An agglomerative hierarchical residual cluster-based analysis was then performed. The sampled clustered study site data was then analyzed for statistical correlations using Monthly Biting Rates (MBR). Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS. A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by Annual Biting Rates (ABR). This data was overlain onto multitemporal sub-meter pixel resolution satellite data (i.e., QuickBird 0.61m wavbands ). Orthogonal spatial filter eigenvectors were then generated in SAS/GIS. Univariate and non-linear regression-based models (i.e., Logistic, Poisson and Negative Binomial) were also employed to determine probability distributions and to identify statistically significant parameter

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.

Corepressive interaction and clustering of degrade-and-fire oscillators

PubMed Central

Fernandez, Bastien; Tsimring, Lev S.

2016-01-01

Strongly nonlinear degrade-and-fire (DF) oscillations may emerge in genetic circuits having a delayed negative feedback loop as their core element. Here we study the synchronization of DF oscillators coupled through a common repressor field. For weak coupling, initially distinct oscillators remain desynchronized. For stronger coupling, oscillators can be forced to wait in the repressed state until the global repressor field is sufficiently degraded, and then they fire simultaneously forming a synchronized cluster. Our analytical theory provides necessary and sufficient conditions for clustering and specifies the maximum number of clusters that can be formed in the asymptotic regime. We find that in the thermodynamic limit a phase transition occurs at a certain coupling strength from the weakly clustered regime with only microscopic clusters to a strongly clustered regime where at least one giant cluster has to be present. PMID:22181453

Clinical Implications of Cluster Analysis-Based Classification of Acute Decompensated Heart Failure and Correlation with Bedside Hemodynamic Profiles.

PubMed

Ahmad, Tariq; Desai, Nihar; Wilson, Francis; Schulte, Phillip; Dunning, Allison; Jacoby, Daniel; Allen, Larry; Fiuzat, Mona; Rogers, Joseph; Felker, G Michael; O'Connor, Christopher; Patel, Chetan B

2016-01-01

Classification of acute decompensated heart failure (ADHF) is based on subjective criteria that crudely capture disease heterogeneity. Improved phenotyping of the syndrome may help improve therapeutic strategies. To derive cluster analysis-based groupings for patients hospitalized with ADHF, and compare their prognostic performance to hemodynamic classifications derived at the bedside. We performed a cluster analysis on baseline clinical variables and PAC measurements of 172 ADHF patients from the ESCAPE trial. Employing regression techniques, we examined associations between clusters and clinically determined hemodynamic profiles (warm/cold/wet/dry). We assessed association with clinical outcomes using Cox proportional hazards models. Likelihood ratio tests were used to compare the prognostic value of cluster data to that of hemodynamic data. We identified four advanced HF clusters: 1) male Caucasians with ischemic cardiomyopathy, multiple comorbidities, lowest B-type natriuretic peptide (BNP) levels; 2) females with non-ischemic cardiomyopathy, few comorbidities, most favorable hemodynamics; 3) young African American males with non-ischemic cardiomyopathy, most adverse hemodynamics, advanced disease; and 4) older Caucasians with ischemic cardiomyopathy, concomitant renal insufficiency, highest BNP levels. There was no association between clusters and bedside-derived hemodynamic profiles (p = 0.70). For all adverse clinical outcomes, Cluster 4 had the highest risk, and Cluster 2, the lowest. Compared to Cluster 4, Clusters 1-3 had 45-70% lower risk of all-cause mortality. Clusters were significantly associated with clinical outcomes, whereas hemodynamic profiles were not. By clustering patients with similar objective variables, we identified four clinically relevant phenotypes of ADHF patients, with no discernable relationship to hemodynamic profiles, but distinct associations with adverse outcomes. Our analysis suggests that ADHF classification using simultaneous

Influence of HLA-DR and -DQ alleles on autoantibody recognition of distinct epitopes within the juxtamembrane domain of the IA-2 autoantigen in type 1 diabetes.

PubMed

Richardson, Carolyn C; McLaughlin, Kerry A; Morgan, Diana; Feltbower, Richard G; Christie, Michael R

2016-02-01

Insulinoma-associated protein 2 (IA-2) is a major target of autoimmunity in type 1 diabetes. When first detected, IA-2-autoantibodies commonly bind epitopes in the juxtamembrane (JM) domain of IA-2 and antibody responses subsequently spread to the tyrosine phosphatase domain. Definition of structures of epitopes in the JM domain, and genetic requirements for autoimmunity to these epitopes, is important for our understanding of initiation and progression of autoimmunity. The aims of this study were to investigate the contribution of individual amino acids in the IA-2 JM domain to antibody binding to these epitopes and the role of HLA genotypes in determining epitope specificity. Regions of the JM domain recognised by autoantibodies were identified by peptide competition and inhibitory effects of alanine substitutions of residues within the JM region. Antibody binding was determined by radioligand binding assays using sera from patients genotyped for HLA-DRB1 and -DQB1 alleles. Patients were categorised into two distinct groups of JM antibody reactivity according to peptide inhibition. Inhibition by substitutions of individual amino acids within the JM domain differed between patients, indicating heterogeneity in epitope recognition. Cluster analysis defined six groups of residues having similar inhibitory effects on antibody binding, with three clusters showing differences in patients affected or unaffected by peptide. One cluster demonstrated significant differences in antibody binding between HLA-DRB1*04 and HLA-DRB1*07 patients and within DRB1*04 individuals; antibody recognition of a second cluster depended on expression of HLA-DQB1*0302. The results identify amino acids contributing to distinct epitopes on IA-2, with both HLA-DR and HLA-DQ alleles influencing epitope specificity.

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

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

GDPC: Gravitation-based Density Peaks Clustering algorithm

NASA Astrophysics Data System (ADS)

Jiang, Jianhua; Hao, Dehao; Chen, Yujun; Parmar, Milan; Li, Keqin

2018-07-01

The Density Peaks Clustering algorithm, which we refer to as DPC, is a novel and efficient density-based clustering approach, and it is published in Science in 2014. The DPC has advantages of discovering clusters with varying sizes and varying densities, but has some limitations of detecting the number of clusters and identifying anomalies. We develop an enhanced algorithm with an alternative decision graph based on gravitation theory and nearby distance to identify centroids and anomalies accurately. We apply our method to some UCI and synthetic data sets. We report comparative clustering performances using F-Measure and 2-dimensional vision. We also compare our method to other clustering algorithms, such as K-Means, Affinity Propagation (AP) and DPC. We present F-Measure scores and clustering accuracies of our GDPC algorithm compared to K-Means, AP and DPC on different data sets. We show that the GDPC has the superior performance in its capability of: (1) detecting the number of clusters obviously; (2) aggregating clusters with varying sizes, varying densities efficiently; (3) identifying anomalies accurately.

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.

Star Formation in Nearby Clusters (SFiNCs)

NASA Astrophysics Data System (ADS)

Getman, Konstantin

Most stars form in clusters that rapidly disperse, yet we have a poor understanding of the processes of cluster formation and early evolution. Do clusters form `top-down', rapidly in a dense molecular cloud core? Or, since clouds are turbulent, do clusters form `bottomup' by merging subclusters produced in small kinematically-distinct molecular structures? Do clusters principally form in elongated molecular structures such as Infrared Dark Clouds and Herschel filaments? One of the central reasons for slow progress in resolving these questions is the lack of homogeneous and reliable census of stellar members (both disk-bearing and disk-free) for a wide range of star forming environments. To address these issues we are now completing our major effort, called MYStIX (Massive Young Star-Forming Complex Study in Infrared and X-ray). It combines the Chandra archive with UKIRT+2MASS near-infrared and Spitzer mid-infrared surveys to identify young stellar objects in a wide range of evolutionary stages, from protostars to disk-free pre-main sequence stars, in 20 star forming regions at distances from 0.4 to 3.6 kpc. Each MYStIX region was chosen to have a rich OB-dominated cluster. Started in 2009 with NASA/ADAP and NSF funding, MYStIX has emerged with 8 technical/catalog and the first 4 of a series of science papers (http://astro.psu.edu/mystix). Early MYStIX results include: demonstration of diverse morphologies of young clusters from simple ellipsoids to elongated, clumpy substructures; demonstration of spatio-age gradients across star formation regions; the discovery of core-halo age gradients within two rich nearby MYStIX clusters; and the discovery of important astrophysically empirical correlations among different subcluster properties such as age, absorption, core radius, central stellar density, and total intrinsic population. The early MYStIX result provide new observational evidence for subcluster merging and cluster expansion following gas dissipation. We

Evolution of coding and non-coding genes in HOX clusters of a marsupial.

PubMed

Yu, Hongshi; Lindsay, James; Feng, Zhi-Ping; Frankenberg, Stephen; Hu, Yanqiu; Carone, Dawn; Shaw, Geoff; Pask, Andrew J; O'Neill, Rachel; Papenfuss, Anthony T; Renfree, Marilyn B

2012-06-18

The HOX gene clusters are thought to be highly conserved amongst mammals and other vertebrates, but the long non-coding RNAs have only been studied in detail in human and mouse. The sequencing of the kangaroo genome provides an opportunity to use comparative analyses to compare the HOX clusters of a mammal with a distinct body plan to those of other mammals. Here we report a comparative analysis of HOX gene clusters between an Australian marsupial of the kangaroo family and the eutherians. There was a strikingly high level of conservation of HOX gene sequence and structure and non-protein coding genes including the microRNAs miR-196a, miR-196b, miR-10a and miR-10b and the long non-coding RNAs HOTAIR, HOTAIRM1 and HOXA11AS that play critical roles in regulating gene expression and controlling development. By microRNA deep sequencing and comparative genomic analyses, two conserved microRNAs (miR-10a and miR-10b) were identified and one new candidate microRNA with typical hairpin precursor structure that is expressed in both fibroblasts and testes was found. The prediction of microRNA target analysis showed that several known microRNA targets, such as miR-10, miR-414 and miR-464, were found in the tammar HOX clusters. In addition, several novel and putative miRNAs were identified that originated from elsewhere in the tammar genome and that target the tammar HOXB and HOXD clusters. This study confirms that the emergence of known long non-coding RNAs in the HOX clusters clearly predate the marsupial-eutherian divergence 160 Ma ago. It also identified a new potentially functional microRNA as well as conserved miRNAs. These non-coding RNAs may participate in the regulation of HOX genes to influence the body plan of this marsupial.

Evolution of coding and non-coding genes in HOX clusters of a marsupial

PubMed Central

2012-01-01

Background The HOX gene clusters are thought to be highly conserved amongst mammals and other vertebrates, but the long non-coding RNAs have only been studied in detail in human and mouse. The sequencing of the kangaroo genome provides an opportunity to use comparative analyses to compare the HOX clusters of a mammal with a distinct body plan to those of other mammals. Results Here we report a comparative analysis of HOX gene clusters between an Australian marsupial of the kangaroo family and the eutherians. There was a strikingly high level of conservation of HOX gene sequence and structure and non-protein coding genes including the microRNAs miR-196a, miR-196b, miR-10a and miR-10b and the long non-coding RNAs HOTAIR, HOTAIRM1 and HOXA11AS that play critical roles in regulating gene expression and controlling development. By microRNA deep sequencing and comparative genomic analyses, two conserved microRNAs (miR-10a and miR-10b) were identified and one new candidate microRNA with typical hairpin precursor structure that is expressed in both fibroblasts and testes was found. The prediction of microRNA target analysis showed that several known microRNA targets, such as miR-10, miR-414 and miR-464, were found in the tammar HOX clusters. In addition, several novel and putative miRNAs were identified that originated from elsewhere in the tammar genome and that target the tammar HOXB and HOXD clusters. Conclusions This study confirms that the emergence of known long non-coding RNAs in the HOX clusters clearly predate the marsupial-eutherian divergence 160 Ma ago. It also identified a new potentially functional microRNA as well as conserved miRNAs. These non-coding RNAs may participate in the regulation of HOX genes to influence the body plan of this marsupial. PMID:22708672

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

PubMed

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

2018-06-19

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

Promoter Hypermethylation Profiling Identifies Subtypes of Head and Neck Cancer with Distinct Viral, Environmental, Genetic and Survival Characteristics

PubMed Central

Choudhury, Javed Hussain; Ghosh, Sankar Kumar

2015-01-01

Background Epigenetic and genetic alteration plays a major role to the development of head and neck squamous cell carcinoma (HNSCC). Consumption of tobacco (smoking/chewing) and human papilloma virus (HPV) are also associated with an increase the risk of HNSCC. Promoter hypermethylation of the tumor suppression genes is related with transcriptional inactivation and loss of gene expression. We investigated epigenetic alteration (promoter methylation of tumor-related genes/loci) in tumor tissues in the context of genetic alteration, viral infection, and tobacco exposure and survival status. Methodology The study included 116 tissue samples (71 tumor and 45 normal tissues) from the Northeast Indian population. Methylation specific polymerase chain reaction (MSP) was used to determine the methylation status of 10 tumor-related genes/loci (p16, DAPK, RASSF1, BRAC1, GSTP1, ECAD, MLH1, MINT1, MINT2 and MINT31). Polymorphisms of CYP1A1, GST (M1 & T1), XRCC1and XRCC2 genes were studied by using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and multiplex-PCR respectively. Principal Findings Unsupervised hierarchical clustering analysis based on methylation pattern had identified two tumor clusters, which significantly differ by CpG island methylator phenotype (CIMP), tobacco, GSTM1, CYP1A1, HPV and survival status. Analyzing methylation of genes/loci individually, we have found significant higher methylation of DAPK, RASSF1, p16 and MINT31genes (P = 0.031, 0.013, 0.031 and 0.015 respectively) in HPV (+) cases compared to HPV (-). Furthermore, a CIMP-high and Cluster-1 characteristic was also associated with poor survival. Conclusions Promoter methylation profiles reflecting a correlation with tobacco, HPV, survival status and genetic alteration and may act as a marker to determine subtypes and patient outcome in HNSCC. PMID:26098903

Teenage Drinking, Symbolic Capital and Distinction

ERIC Educational Resources Information Center

Jarvinen, Margaretha; Gundelach, Peter

2007-01-01

This article analyses alcohol-related lifestyles among Danish teenagers. Building on Bourdieu's reasoning on symbolic capital and distinction, we analyse three interrelated themes. First, we show that alcohol-related variables (drinking patterns, drinking debut, experience of intoxication, etc.) can be used to identify some very distinctive life…

Adaptive Scaling of Cluster Boundaries for Large-Scale Social Media Data Clustering.

PubMed

Meng, Lei; Tan, Ah-Hwee; Wunsch, Donald C

2016-12-01

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the clustering mechanism of Fuzzy ART, and discover the vigilance region (VR) that essentially determines how a cluster in the Fuzzy ART system recognizes similar patterns in the feature space. The VR gives an intrinsic interpretation of the clustering mechanism and limitations of Fuzzy ART. Second, we introduce the idea of allowing different clusters in the Fuzzy ART system to have different vigilance levels in order to meet the diverse nature of the pattern distribution of social media data. To this end, we propose three vigilance adaptation methods, namely, the activation maximization (AM) rule, the confliction minimization (CM) rule, and the hybrid integration (HI) rule. With an initial vigilance value, the resulting clustering algorithms, namely, the AM-ART, CM-ART, and HI-ART, can automatically adapt the vigilance values of all clusters during the learning epochs in order to produce better cluster boundaries. Experiments on four social media data sets show that AM-ART, CM-ART, and HI-ART are more robust than Fuzzy ART to the initial vigilance value, and they usually achieve better or comparable performance and much faster speed than the state-of-the-art clustering algorithms that also do not require a predefined number of clusters.

Brighter galaxy bias: underestimating the velocity dispersions of galaxy clusters

NASA Astrophysics Data System (ADS)

Old, L.; Gray, M. E.; Pearce, F. R.

2013-09-01

We study the systematic bias introduced when selecting the spectroscopic redshifts of brighter cluster galaxies to estimate the velocity dispersion of galaxy clusters from both simulated and observational galaxy catalogues. We select clusters with Ngal ≥ 50 at five low-redshift snapshots from the publicly available De Lucia & Blaziot semi-analytic model galaxy catalogue. Clusters are also selected from the Tempel Sloan Digital Sky Survey Data Release 8 groups and clusters catalogue across the redshift range 0.021 ≤ z ≤ 0.098. We employ various selection techniques to explore whether the velocity dispersion bias is simply due to a lack of dynamical information or is the result of an underlying physical process occurring in the cluster, for example, dynamical friction experienced by the brighter cluster members. The velocity dispersions of the parent dark matter (DM) haloes are compared to the galaxy cluster dispersions and the stacked distribution of DM particle velocities is examined alongside the corresponding galaxy velocity distribution. We find a clear bias between the halo and the semi-analytic galaxy cluster velocity dispersion on the order of σgal/σDM ˜ 0.87-0.95 and a distinct difference in the stacked galaxy and DM particle velocities distribution. We identify a systematic underestimation of the velocity dispersions when imposing increasing absolute I-band magnitude limits. This underestimation is enhanced when using only the brighter cluster members for dynamical analysis on the order of 5-35 per cent, indicating that dynamical friction is a serious source of bias when using galaxy velocities as tracers of the underlying gravitational potential. In contrast to the literature we find that the resulting bias is not only halo mass dependent but also that the nature of the dependence changes according to the galaxy selection strategy. We make a recommendation that, in the realistic case of limited availability of spectral observations, a strictly

Phenotypes determined by cluster analysis in severe or difficult-to-treat asthma.

PubMed

Schatz, Michael; Hsu, Jin-Wen Y; Zeiger, Robert S; Chen, Wansu; Dorenbaum, Alejandro; Chipps, Bradley E; Haselkorn, Tmirah

2014-06-01

Asthma phenotyping can facilitate understanding of disease pathogenesis and potential targeted therapies. To further characterize the distinguishing features of phenotypic groups in difficult-to-treat asthma. Children ages 6-11 years (n = 518) and adolescents and adults ages ≥12 years (n = 3612) with severe or difficult-to-treat asthma from The Epidemiology and Natural History of Asthma: Outcomes and Treatment Regimens (TENOR) study were evaluated in this post hoc cluster analysis. Analyzed variables included sex, race, atopy, age of asthma onset, smoking (adolescents and adults), passive smoke exposure (children), obesity, and aspirin sensitivity. Cluster analysis used the hierarchical clustering algorithm with the Ward minimum variance method. The results were compared among clusters by χ(2) analysis; variables with significant (P < .05) differences among clusters were considered as distinguishing feature candidates. Associations among clusters and asthma-related health outcomes were assessed in multivariable analyses by adjusting for socioeconomic status, environmental exposures, and intensity of therapy. Five clusters were identified in each age stratum. Sex, atopic status, and nonwhite race were distinguishing variables in both strata; passive smoke exposure was distinguishing in children and aspirin sensitivity in adolescents and adults. Clusters were not related to outcomes in children, but 2 adult and adolescent clusters distinguished by nonwhite race and aspirin sensitivity manifested poorer quality of life (P < .0001), and the aspirin-sensitive cluster experienced more frequent asthma exacerbations (P < .0001). Distinct phenotypes appear to exist in patients with severe or difficult-to-treat asthma, which is related to outcomes in adolescents and adults but not in children. The study of the therapeutic implications of these phenotypes is warranted. Copyright © 2013 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights

GibbsCluster: unsupervised clustering and alignment of peptide sequences.

PubMed

Andreatta, Massimo; Alvarez, Bruno; Nielsen, Morten

2017-07-03

Receptor interactions with short linear peptide fragments (ligands) are at the base of many biological signaling processes. Conserved and information-rich amino acid patterns, commonly called sequence motifs, shape and regulate these interactions. Because of the properties of a receptor-ligand system or of the assay used to interrogate it, experimental data often contain multiple sequence motifs. GibbsCluster is a powerful tool for unsupervised motif discovery because it can simultaneously cluster and align peptide data. The GibbsCluster 2.0 presented here is an improved version incorporating insertion and deletions accounting for variations in motif length in the peptide input. In basic terms, the program takes as input a set of peptide sequences and clusters them into meaningful groups. It returns the optimal number of clusters it identified, together with the sequence alignment and sequence motif characterizing each cluster. Several parameters are available to customize cluster analysis, including adjustable penalties for small clusters and overlapping groups and a trash cluster to remove outliers. As an example application, we used the server to deconvolute multiple specificities in large-scale peptidome data generated by mass spectrometry. The server is available at http://www.cbs.dtu.dk/services/GibbsCluster-2.0. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Three subgroups of pain profiles identified in 227 women with arthritis: a latent class analysis.

PubMed

de Luca, Katie; Parkinson, Lynne; Downie, Aron; Blyth, Fiona; Byles, Julie

2017-03-01

The objectives were to identify subgroups of women with arthritis based upon the multi-dimensional nature of their pain experience and to compare health and socio-demographic variables between subgroups. A latent class analysis of 227 women with self-reported arthritis was used to identify clusters of women based upon the sensory, affective, and cognitive dimensions of the pain experience. Multivariate multinomial logistic regression analysis was used to determine the relationship between cluster membership and health and sociodemographic characteristics. A three-class cluster model was most parsimonious. 39.5 % of women had a unidimensional pain profile; 38.6 % of women had moderate multidimensional pain profile that included additional pain symptomatology such as sensory qualities and pain catastrophizing; and 21.9 % of women had severe multidimensional pain profile that included prominent pain symptomatology such as sensory and affective qualities of pain, pain catastrophizing, and neuropathic pain. Women with severe multidimensional pain profile have a 30.5 % higher risk of poorer quality of life and a 7.3 % higher risk of suffering depression, and women with moderate multidimensional pain profile have a 6.4 % higher risk of poorer quality of life when compared to women with unidimensional pain. This study identified three distinct subgroups of pain profiles in older women with arthritis. Women had very different experiences of pain, and cluster membership impacted significantly on health-related quality of life. These preliminary findings provide a stronger understanding of profiles of pain and may contribute to the development of tailored treatment options in arthritis.

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.

Genomic Characterization of Vulvar (Pre)cancers Identifies Distinct Molecular Subtypes with Prognostic Significance.

PubMed

Nooij, Linda S; Ter Haar, Natalja T; Ruano, Dina; Rakislova, Natalia; van Wezel, Tom; Smit, Vincent T H B M; Trimbos, Baptist J B M Z; Ordi, Jaume; van Poelgeest, Mariette I E; Bosse, Tjalling

2017-11-15

Purpose: Vulvar cancer (VC) can be subclassified by human papillomavirus (HPV) status. HPV-negative VCs frequently harbor TP53 mutations; however, in-depth analysis of other potential molecular genetic alterations is lacking. We comprehensively assessed somatic mutations in a large series of vulvar (pre)cancers. Experimental Design: We performed targeted next-generation sequencing (17 genes), p53 immunohistochemistry and HPV testing on 36 VC and 82 precursors (sequencing cohort). Subsequently, the prognostic significance of the three subtypes identified in the sequencing cohort was assessed in a series of 236 VC patients (follow-up cohort). Results: Frequent recurrent mutations were identified in HPV-negative vulvar (pre)cancers in TP53 (42% and 68%), NOTCH1 (28% and 41%), and HRAS (20% and 31%). Mutation frequency in HPV-positive vulvar (pre)cancers was significantly lower ( P = 0.001). Furthermore, a substantial subset of the HPV-negative precursors (35/60, 58.3%) and VC (10/29, 34.5%) were TP53 wild-type (wt), suggesting a third, not-previously described, molecular subtype. Clinical outcomes in the three different subtypes (HPV + , HPV - /p53wt, HPV - /p53abn) were evaluated in a follow-up cohort consisting of 236 VC patients. Local recurrence rate was 5.3% for HPV + , 16.3% for HPV - /p53wt and 22.6% for HPV - /p53abn tumors ( P = 0.044). HPV positivity remained an independent prognostic factor for favorable outcome in the multivariable analysis ( P = 0.020). Conclusions: HPV - and HPV + vulvar (pre)cancers display striking differences in somatic mutation patterns. HPV - /p53wt VC appear to be a distinct clinicopathologic subgroup with frequent NOTCH1 mutations. HPV + VC have a significantly lower local recurrence rate, independent of clinicopathological variables, opening opportunities for reducing overtreatment in VC. Clin Cancer Res; 23(22); 6781-9. ©2017 AACR . ©2017 American Association for Cancer Research.

Structure and Sequence Analyses of Clustered Protocadherins Reveal Antiparallel Interactions that Mediate Homophilic Specificity.

PubMed

Nicoludis, John M; Lau, Sze-Yi; Schärfe, Charlotta P I; Marks, Debora S; Weihofen, Wilhelm A; Gaudet, Rachelle

2015-11-03

Clustered protocadherin (Pcdh) proteins mediate dendritic self-avoidance in neurons via specific homophilic interactions in their extracellular cadherin (EC) domains. We determined crystal structures of EC1-EC3, containing the homophilic specificity-determining region, of two mouse clustered Pcdh isoforms (PcdhγA1 and PcdhγC3) to investigate the nature of the homophilic interaction. Within the crystal lattices, we observe antiparallel interfaces consistent with a role in trans cell-cell contact. Antiparallel dimerization is supported by evolutionary correlations. Two interfaces, located primarily on EC2-EC3, involve distinctive clustered Pcdh structure and sequence motifs, lack predicted glycosylation sites, and contain residues highly conserved in orthologs but not paralogs, pointing toward their biological significance as homophilic interaction interfaces. These two interfaces are similar yet distinct, reflecting a possible difference in interaction architecture between clustered Pcdh subfamilies. These structures initiate a molecular understanding of clustered Pcdh assemblies that are required to produce functional neuronal networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

Use of Parsimony Analysis to Identify Areas of Endemism of Chinese Birds: Implications for Conservation and Biogeography

PubMed Central

Huang, Xiao-Lei; Qiao, Ge-Xia; Lei, Fu-Min

2010-01-01

Parsimony analysis of endemicity (PAE) was used to identify areas of endemism (AOEs) for Chinese birds at the subregional level. Four AOEs were identified based on a distribution database of 105 endemic species and using 18 avifaunal subregions as the operating geographical units (OGUs). The four AOEs are the Qinghai-Zangnan Subregion, the Southwest Mountainous Subregion, the Hainan Subregion and the Taiwan Subregion. Cladistic analysis of subregions generally supports the division of China’s avifauna into Palaearctic and Oriental realms. Two PAE area trees were produced from two different distribution datasets (year 1976 and 2007). The 1976 topology has four distinct subregional branches; however, the 2007 topology has three distinct branches. Moreover, three Palaearctic subregions in the 1976 tree clustered together with the Oriental subregions in the 2007 tree. Such topological differences may reflect changes in the distribution of bird species through circa three decades. PMID:20559504

Clustering of dystonia in some pedigrees with autosomal dominant essential tremor suggests the existence of a distinct subtype of essential tremor

PubMed Central

2010-01-01

Background There is an ongoing debate whether essential tremor (ET) represents a monosymptomatic disorder or other neurologic symptoms are compatible with the diagnosis of ET. Many patients with clinically definite ET develop dystonia. It remains unknown whether tremor associated with dystonia represent a subtype of ET. We hypothesized that ET with dystonia represents a distinct subtype of ET. Methods We studied patients diagnosed with familial ET and dystonia. We included only those patients whose first-degree relatives met diagnostic criteria for ET or dystonia with tremor. This cohort was ascertained for the presence of focal, segmental, multifocal, hemidystonia or generalized dystonia, and ET. Results We included 463 patients from 97 kindreds with autosomal dominant mode of inheritance (AD), defined by the vertical transmission of the disease. ET was the predominant phenotype in every ascertained family and each was phenotypically classified as AD ET. "Pure" ET was present in 365 individuals. Focal or segmental dystonia was present in 98 of the 463 patients; 87 of the 98 patients had ET associated with dystonia, one had dystonic tremor and ten had isolated dystonia. The age of onset and tremor severity did not differ between patients with "pure" ET and ET associated with dystonia. We did not observe a random distribution of dystonia in AD ET pedigrees and all patients with dystonia associated with ET were clustered in 28% of all included pedigrees (27/97, p < 0.001). Conclusions Our results suggest that familial ET associated with dystonia may represent a distinct subtype of ET. PMID:20670416

Flow over gravel beds with clusters

NASA Astrophysics Data System (ADS)

Little, M.; Venditti, J. G.

2014-12-01

The structure of a gravel bed has been shown to alter the entrainment threshold. Structures such as clusters, reticulate stone cells and other discrete structures lock grains together, making it more difficult for them to be mobilized. These structures also generate form drag, reducing the shear stress available for mobilization. Form drag over gravel beds is often assumed to be negligible, but this assumption is not well supported. Here, we explore how cluster density and arrangement affect flow resistance and the flow structure over a fixed gravel bed in a flume experiment. Cluster density was varied from 6 to 68.3 clusters per square meter which corresponds to areal bed coverages of 2 to 17%. We used regular, irregular and random arrangements of the clusters. Our results show that flow resistance over a planar gravel bed initially declines, then increases with flow depth. The addition of clusters increases flow resistance, but the effect is dependent on cluster density, flow depth and arrangement. At the highest density, clusters can increase flow resistance as by as much as 8 times when compared to flat planar bed with no grain-related form drag. Spatially resolved observations of flow over the clusters indicate that a well-defined wake forms in the lee of each cluster. At low cluster density, the wakes are isolated and weak. As cluster density increases, the wakes become stronger. At the highest density, the wakes interact and the within cluster flow field detaches from the overlying flow. This generates a distinct shear layer at the height of the clusters. In spite of this change in the flow field at high density, our results suggest that flow resistance simply increases with cluster density. Our results suggest that the form drag associated with a gravel bed can be substantial and that it depends on the arrangement of the grains on the bed.

X-ray archaeology in the Coma cluster

NASA Technical Reports Server (NTRS)

White, Simon D. M.; Briel, Ulrich G.; Henry, J. P.

1993-01-01

We present images of X-ray emission from hot gas within the Coma cluster of galaxies. These maps, made with the ROSAT satellite, have much higher SNR than any previous X-ray image of a galaxy cluster, and allow cluster structure to be analyzed in unprecedented detail. They show greater structural irregularity than might have been anticipated from earlier observations of Coma. Emission is detected from a number of bright cluster galaxies in addition to the two known previously. In four cases, there is evidence that these galaxies lie at the center of an extended subconcentration within the cluster, possibly the remnant of their associated groups. For at least two galaxies, the images show direct evidence for ongoing disruption of their gaseous atmosphere. The luminosity associated with these galaxies is comparable to that detected around similar ellipticals in much poorer environments. Emission is easily detected to the limit of our field, about 1 deg from the cluster center, and appears to become more regular at large radii. The data show clearly that this archetype of a rich and regular galaxy cluster was, in fact, formed by the merging of several distinct subunits which are not yet fully destroyed.

Semi-supervised clustering methods.

PubMed

Bair, Eric

2013-01-01

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as "semi-supervised clustering" methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided.

Streptococcus pneumoniae and Pseudomonas aeruginosa pneumonia induce distinct host responses

PubMed Central

McConnell, Kevin W.; McDunn, Jonathan E.; Clark, Andrew T.; Dunne, W. Michael; Dixon, David J.; Turnbull, Isaiah R.; DiPasco, Peter J.; Osberghaus, William F.; Sherman, Benjamin; Martin, James R.; Walter, Michael J.; Cobb, J. Perren; Buchman, Timothy G.; Hotchkiss, Richard S.; Coopersmith, Craig M.

2009-01-01

Objective Pathogens that cause pneumonia may be treated in a targeted fashion by antibiotics, but if this therapy fails, treatment involves only non-specific supportive measures, independent of the inciting infection. The purpose of this study was to determine whether host response is similar following disparate infections with similar mortalities. Design Prospective, randomized controlled study. Setting Animal laboratory in a university medical center. Interventions Pneumonia was induced in FVB/N mice by either Streptococcus pneumoniae or two different concentrations of Pseudomonas aeruginosa. Plasma and bronchoalveolar lavage fluid from septic animals was assayed by a microarray immunoassay measuring 18 inflammatory mediators at multiple timepoints. Measurements and Main Results The host response was dependent upon the causative organism as well as kinetics of mortality, but the pro- and anti- inflammatory response was independent of inoculum concentration or degree of bacteremia. Pneumonia caused by different concentrations of the same bacteria, Pseudomonas aeruginosa, also yielded distinct inflammatory responses; however, inflammatory mediator expression did not directly track the severity of infection. For all infections, the host response was compartmentalized, with markedly different concentrations of inflammatory mediators in the systemic circulation and the lungs. Hierarchical clustering analysis resulted in the identification of 5 distinct clusters of the host response to bacterial infection. Principal components analysis correlated pulmonary MIP-2 and IL-10 with progression of infection while elevated plasma TNFsr2 and MCP-1 were indicative of fulminant disease with >90% mortality within 48 hours. Conclusions Septic mice have distinct local and systemic responses to Streptococcus pneumoniae and Pseudomonas aeruginosa pneumonia. Targeting specific host inflammatory responses induced by distinct bacterial infections could represent a potential therapeutic

Streptococcus pneumoniae and Pseudomonas aeruginosa pneumonia induce distinct host responses.

PubMed

McConnell, Kevin W; McDunn, Jonathan E; Clark, Andrew T; Dunne, W Michael; Dixon, David J; Turnbull, Isaiah R; Dipasco, Peter J; Osberghaus, William F; Sherman, Benjamin; Martin, James R; Walter, Michael J; Cobb, J Perren; Buchman, Timothy G; Hotchkiss, Richard S; Coopersmith, Craig M

2010-01-01

Pathogens that cause pneumonia may be treated in a targeted fashion by antibiotics, but if this therapy fails, then treatment involves only nonspecific supportive measures, independent of the inciting infection. The purpose of this study was to determine whether host response is similar after disparate infections with similar mortalities. Prospective, randomized controlled study. Animal laboratory in a university medical center. Pneumonia was induced in FVB/N mice by either Streptococcus pneumoniae or two different concentrations of Pseudomonas aeruginosa. Plasma and bronchoalveolar lavage fluid from septic animals was assayed by a microarray immunoassay measuring 18 inflammatory mediators at multiple time points. The host response was dependent on the causative organism as well as kinetics of mortality, but the pro-inflammatory and anti-inflammatory responses were independent of inoculum concentration or degree of bacteremia. Pneumonia caused by different concentrations of the same bacteria, Pseudomonas aeruginosa, also yielded distinct inflammatory responses; however, inflammatory mediator expression did not directly track the severity of infection. For all infections, the host response was compartmentalized, with markedly different concentrations of inflammatory mediators in the systemic circulation and the lungs. Hierarchical clustering analysis resulted in the identification of five distinct clusters of the host response to bacterial infection. Principal components analysis correlated pulmonary macrophage inflammatory peptide-2 and interleukin-10 with progression of infection, whereas elevated plasma tumor necrosis factor sr2 and macrophage chemotactic peptide-1 were indicative of fulminant disease with >90% mortality within 48 hrs. Septic mice have distinct local and systemic responses to Streptococcus pneumoniae and Pseudomonas aeruginosa pneumonia. Targeting specific host inflammatory responses induced by distinct bacterial infections could represent a

Photoionization of rare gas clusters

NASA Astrophysics Data System (ADS)

Zhang, Huaizhen

This thesis concentrates on the study of photoionization of van der Waals clusters with different cluster sizes. The goal of the experimental investigation is to understand the electronic structure of van der Waals clusters and the electronic dynamics. These studies are fundamental to understand the interaction between UV-X rays and clusters. The experiments were performed at the Advanced Light Source at Lawrence Berkeley National Laboratory. The experimental method employs angle-resolved time-of-flight photoelectron spectrometry, one of the most powerful methods for probing the electronic structure of atoms, molecules, clusters and solids. The van der Waals cluster photoionization studies are focused on probing the evolution of the photoelectron angular distribution parameter as a function of photon energy and cluster size. The angular distribution has been known to be a sensitive probe of the electronic structure in atoms and molecules. However, it has not been used in the case of van der Waals clusters. We carried out outer-valence levels, inner-valence levels and core-levels cluster photoionization experiments. Specifically, this work reports on the first quantitative measurements of the angular distribution parameters of rare gas clusters as a function of average cluster sizes. Our findings for xenon clusters is that the overall photon-energy-dependent behavior of the photoelectrons from the clusters is very similar to that of the corresponding free atoms. However, distinct differences in the angular distribution point at cluster-size-dependent effects were found. For krypton clusters, in the photon energy range where atomic photoelectrons have a high angular anisotropy, our measurements show considerably more isotropic angular distributions for the cluster photoelectrons, especially right above the 3d and 4p thresholds. For the valence electrons, a surprising difference between the two spin-orbit components was found. For argon clusters, we found that the

B. subtilis as a Model for Studying the Assembly of Fe-S Clusters in Gram-Positive Bacteria.

PubMed

Dos Santos, Patricia C

2017-01-01

Complexes of iron and sulfur (Fe-S clusters) are widely distributed in nature and participate in essential biochemical reactions. The biological formation of Fe-S clusters involves dedicated pathways responsible for the mobilization of sulfur, the assembly of Fe-S clusters, and the transfer of these clusters to target proteins. Genomic analysis of Bacillus subtilis and other Gram-positive bacteria indicated the presence of only one Fe-S cluster biosynthesis pathway, which is distinct in number of components and organization from previously studied systems. B. subtilis has been used as a model system for the characterization of cysteine desulfurases responsible for sulfur mobilization reactions in the biogenesis of Fe-S clusters and other sulfur-containing cofactors. Cysteine desulfurases catalyze the cleavage of the C-S bond from the amino acid cysteine and subsequent transfer of sulfur to acceptor molecules. These reactions can be monitored by the rate of alanine formation, the first product in the reaction, and sulfide formation, a byproduct of reactions performed under reducing conditions. The assembly of Fe-S clusters on protein scaffolds and the transfer of these clusters to target acceptors are determined through a combination of spectroscopic methods probing the rate of cluster assembly and transfer. This chapter provides a description of reactions promoting the assembly of Fe-S clusters in bacteria as well as methods used to study functions of each biosynthetic component and identify mechanistic differences employed by these enzymes across different pathways. © 2017 Elsevier Inc. All rights reserved.

The Structure of the Star-forming Cluster RCW 38

NASA Astrophysics Data System (ADS)

Winston, E.; Wolk, S. J.; Bourke, T. L.; Megeath, S. T.; Gutermuth, R.; Spitzbart, B.

2011-12-01

We present a study of the structure of the high-mass star-forming region RCW 38 and the spatial distribution of its young stellar population. Spitzer Infrared Array Camera (IRAC) photometry (3-8 μm) is combined with Two Micron All Sky Survey near-IR data to identify young stellar objects (YSOs) by IR-excess emission from their circumstellar material. Chandra X-ray data are used to identify class III pre-main-sequence stars lacking circumstellar material. We identify 624 YSOs: 23 class 0/I and 90 flat spectrum protostars, 437 class II stars, and 74 class III stars. We also identify 29 (27 new) O star candidates over the IRAC field. Seventy-two stars exhibit IR-variability, including 7 class 0/I and 12 flat spectrum YSOs. A further 177 tentative candidates are identified by their location in the IRAC [3.6] versus [3.6]-[5.8] color-magnitude diagram. We find strong evidence of subclustering in the region. Three subclusters were identified surrounding the central cluster, with massive and variable stars in each subcluster. The central region shows evidence of distinct spatial distributions of the protostars and pre-main-sequence stars. A previously detected IR cluster, DB2001_Obj36, has been established as a subcluster of RCW 38. This suggests that star formation in RCW 38 occurs over a more extended area than previously thought. The gas-to-dust ratio is examined using the X-ray derived hydrogen column density, N H and the K-band extinction, and found to be consistent with the diffuse interstellar medium, in contrast with Serpens and NGC 1333. We posit that the high photoionizing flux of massive stars in RCW 38 affects the agglomeration of the dust grains.

Clusternomics: Integrative context-dependent clustering for heterogeneous datasets

PubMed Central

Wernisch, Lorenz

2017-01-01

Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm. PMID:29036190

Clusternomics: Integrative context-dependent clustering for heterogeneous datasets.

PubMed

Gabasova, Evelina; Reid, John; Wernisch, Lorenz

2017-10-01

Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm.

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

PubMed

Hulsegge, B; de Greef, K H

2018-05-01

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

Novel clustering of items from the Autism Diagnostic Interview-Revised to define phenotypes within autism spectrum disorders

PubMed Central

Hu, Valerie W.; Steinberg, Mara E.

2009-01-01

Heterogeneity in phenotypic presentation of ASD has been cited as one explanation for the difficulty in pinpointing specific genes involved in autism. Recent studies have attempted to reduce the “noise” in genetic and other biological data by reducing the phenotypic heterogeneity of the sample population. The current study employs multiple clustering algorithms on 123 item scores from the Autism Diagnostic Interview-Revised (ADI-R) diagnostic instrument of nearly 2000 autistic individuals to identify subgroups of autistic probands with clinically relevant behavioral phenotypes in order to isolate more homogeneous groups of subjects for gene expression analyses. Our combined cluster analyses suggest optimal division of the autistic probands into 4 phenotypic clusters based on similarity of symptom severity across the 123 selected item scores. One cluster is characterized by severe language deficits, while another exhibits milder symptoms across the domains. A third group possesses a higher frequency of savant skills while the fourth group exhibited intermediate severity across all domains. Grouping autistic individuals by multivariate cluster analysis of ADI-R scores reveals meaningful phenotypes of subgroups within the autistic spectrum which we show, in a related (accompanying) study, to be associated with distinct gene expression profiles. PMID:19455643

Is antibody clustering predictive of clinical subsets and damage in systemic lupus erythematosus?

PubMed

To, C H; Petri, M

2005-12-01

To examine autoantibody clusters and their associations with clinical features and organ damage accrual in patients with systemic lupus erythematosus (SLE). The study group comprised 1,357 consecutive patients with SLE who were recruited to participate in a prospective longitudinal cohort study. In the cohort, 92.6% of the patients were women, the mean +/- SD age of the patients was 41.3 +/- 12.7 years, 55.9% were Caucasian, 39.1% were African American, and 5% were Asian. Seven autoantibodies (anti-double-stranded DNA [anti-dsDNA], anti-Sm, anti-Ro, anti-La, anti-RNP, lupus anticoagulant (LAC), and anticardiolipin antibody [aCL]) were selected for cluster analysis using the K-means cluster analysis procedure. Three distinct autoantibody clusters were identified: cluster 1 (anti-Sm and anti-RNP), cluster 2 (anti-dsDNA, anti-Ro, and anti-La), and cluster 3 (anti-dsDNA, LAC, and aCL). Patients in cluster 1 (n = 451), when compared with patients in clusters 2 (n = 470) and 3 (n = 436), had the lowest incidence of proteinuria (39.7%), anemia (52.8%), lymphopenia (33.9%), and thrombocytopenia (13.7%). The incidence of nephrotic syndrome and leukopenia was also lower in cluster 1 than in cluster 2. Cluster 2 had the highest female-to-male ratio (22:1) and the greatest proportion of Asian patients. Among the 3 clusters, cluster 2 had significantly more patients presenting with secondary Sjögren's syndrome (15.7%). Cluster 3, when compared with the other 2 clusters, consisted of more Caucasian and fewer African American patients and was characterized by the highest incidence of arterial thrombosis (17.4%), venous thrombosis (25.7%), and livedo reticularis (31.4%). By using the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index, the greatest frequency of nephrotic syndrome (8.9%) was observed in patients in cluster 2, whereas cluster 3 patients had the highest percentage of damage due to cerebrovascular accident (12.8%) and

Transcriptome signature identifies distinct cervical pathways induced in lipopolysaccharide-mediated preterm birth.

PubMed

Willcockson, Alexandra R; Nandu, Tulip; Liu, Cheuk-Lun; Nallasamy, Shanmugasundaram; Kraus, W Lee; Mahendroo, Mala

2018-03-01

With half a million babies born preterm each year in the USA and about 15 million worldwide, preterm birth (PTB) remains a global health issue. Preterm birth is a primary cause of infant morbidity and mortality and can impact lives long past infancy. The fact that there are numerous, and many currently unidentified, etiologies of PTB has hindered development of tools for risk evaluation and preventative therapies. Infection is estimated to be involved in nearly 40% of PTBs of known etiology; therefore, understanding how infection-mediated inflammation alters the cervical milieu and leads to preterm tissue biomechanical changes are questions of interest. Using RNA-seq, we identified enrichment of components involved in inflammasome activation and unique proteases in the mouse cervix during lipopolysaccharide (LPS)-mediated PTB and not physiologically at term before labor. Despite transcriptional induction of inflammasome components, there was no evidence of functional activation based on assessment of mature IL1B and IL18 proteins. The increased transcription of proteases that target both elastic fibers and collagen and concentration of myeloid-derived cells capable of protease synthesis in the cervical stroma support the structural disruption of elastic fibers as a functional output of protease activity. The recent demonstration that elastic fibers contribute to the biomechanical function of the pregnant cervix suggests their protease-induced disruption in the infection model of LPS-mediated PTB and may contribute to premature loss of mechanical competency and preterm delivery. Collectively, the transcriptomics and ultrastructural data provide new insights into the distinct mechanisms of premature cervical remodeling in response to infection.

Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters

NASA Astrophysics Data System (ADS)

Pasquato, Mario; Chung, Chul

2016-05-01

Context. Machine-learning (ML) solves problems by learning patterns from data with limited or no human guidance. In astronomy, ML is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims: We apply ML to gravitational N-body simulations of star clusters that are either formed by merging two progenitors or evolved in isolation, planning to later identify globular clusters (GCs) that may have a history of merging from observational data. Methods: We create mock-observations from simulated GCs, from which we measure a set of parameters (also called features in the machine-learning field). After carrying out dimensionality reduction on the feature space, the resulting datapoints are fed in to various classification algorithms. Using repeated random subsampling validation, we check whether the groups identified by the algorithms correspond to the underlying physical distinction between mergers and monolithically evolved simulations. Results: The three algorithms we considered (C5.0 trees, k-nearest neighbour, and support-vector machines) all achieve a test misclassification rate of about 10% without parameter tuning, with support-vector machines slightly outperforming the others. The first principal component of feature space correlates with cluster concentration. If we exclude it from the regression, the performance of the algorithms is only slightly reduced.

STAR CLUSTERS IN M31. II. OLD CLUSTER METALLICITIES AND AGES FROM HECTOSPEC DATA

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

Caldwell, Nelson; Schiavon, Ricardo; Morrison, Heather

2011-02-15

We present new high signal-to-noise spectroscopic data on the M31 globular cluster (GC) system, obtained with the Hectospec multifiber spectrograph on the 6.5 m MMT. More than 300 clusters have been observed at a resolution of 5 A and with a median S/N of 75 per A, providing velocities with a median uncertainty of 6 km s{sup -1}. The primary focus of this paper is the determination of mean cluster metallicities, ages, and reddenings. Metallicities were estimated using a calibration of Lick indices with [Fe/H] provided by Galactic GCs. These match well the metallicities of 24 M31 clusters determined frommore » Hubble Space Telescope color-magnitude diagrams, the differences having an rms of 0.2 dex. The metallicity distribution is not generally bimodal, in strong distinction with the bimodal Galactic globular distribution. Rather, the M31 distribution shows a broad peak, centered at [Fe/H] = -1, possibly with minor peaks at [Fe/H] = -1.4, -0.7, and -0.2, suggesting that the cluster systems of M31 and the Milky Way had different formation histories. Ages for clusters with [Fe/H] > - 1 were determined using the automatic stellar population analysis program EZ{sub A}ges. We find no evidence for massive clusters in M31 with intermediate ages, those between 2 and 6 Gyr. Moreover, we find that the mean ages of the old GCs are remarkably constant over about a decade in metallicity (-0.95{approx}< [Fe/H] {approx}<0.0).« less

Effect of Clustering Algorithm on Establishing Markov State Model for Molecular Dynamics Simulations.

PubMed

Li, Yan; Dong, Zigang

2016-06-27

Recently, the Markov state model has been applied for kinetic analysis of molecular dynamics simulations. However, discretization of the conformational space remains a primary challenge in model building, and it is not clear how the space decomposition by distinct clustering strategies exerts influence on the model output. In this work, different clustering algorithms are employed to partition the conformational space sampled in opening and closing of fatty acid binding protein 4 as well as inactivation and activation of the epidermal growth factor receptor. Various classifications are achieved, and Markov models are set up accordingly. On the basis of the models, the total net flux and transition rate are calculated between two distinct states. Our results indicate that geometric and kinetic clustering perform equally well. The construction and outcome of Markov models are heavily dependent on the data traits. Compared to other methods, a combination of Bayesian and hierarchical clustering is feasible in identification of metastable states.

Pre-crash scenarios at road junctions: A clustering method for car crash data.

PubMed

Nitsche, Philippe; Thomas, Pete; Stuetz, Rainer; Welsh, Ruth

2017-10-01

Given the recent advancements in autonomous driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual simulation environments or on real-world test tracks. This paper presents a novel data analysis method including the preparation, analysis and visualization of car crash data, to identify the critical pre-crash scenarios at T- and four-legged junctions as a basis for testing the safety of automated driving systems. The presented method employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1056 junction crashes in the UK, which were exported from the in-depth "On-the-Spot" database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. The results support existing findings on road junction accidents and provide benchmark situations for safety performance tests in order to reduce the possible number parameter combinations. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

A distinct class of homeodomain proteins is encoded by two sequentially expressed Drosophila genes from the 93D/E cluster.

PubMed Central

Jagla, K; Stanceva, I; Dretzen, G; Bellard, F; Bellard, M

1994-01-01

Homeodomains appear to be one of the most frequently employed DNA-binding domains in a superfamily of transacting factors. It is likely that during evolution several sub-types of homeodomain have evolved from a common ancestral domain, resulting in distinct but closely related DNA-binding preferences. Here we describe the conservation of a distinct type of homeodomain encoded by the Drosophila lady-bird-late (lbl) gene, previously named nkch4 (1). Using degenerate PCR primers corresponding to the most divergent regions of the first and third helix of the Lbl homeodomain we have amplified, from genomic DNA of the fly, a lady-bird-like homeobox fragment. The Drosophila PCR products contained both the lbl (1) and a highly related homeobox sequence, which we named lady-bird-early (lbe). This new Drosophila gene resides directly upstream to lbl and together with tinman/NK4 (2, 3, 4, 5), bagpipe/NK3 (2, 4) S59/NK1 (4, 6) and 93Bal (7) compose the 93D/E homeobox gene cluster. Ibe and lbl are transcribed from the same strand and in a temporal order corresponding to their 5'-3' chromosomal location. Transcripts of both genes are found in the epiderm of Drosophila embryos, in cells known to express a segment polarity gene wingless (8), and their spatial and temporal colinearity of expression strongly suggests that they cooperate during segmentation. The amino-acid composition of both Lady-bird homeodomains differ from that of Antp-type at several positions involved in DNA recognition. These substitutions appear to modify DNA-binding preferences since Lbl homeodomain is unable to recognize the most common homeodomain binding TAAT motif in gel retardation experiments. Images PMID:7909370

Recent Insights Into the Prenucleation Cluster Pathway

NASA Astrophysics Data System (ADS)

Gebauer, D.; Kellermeier, M.; Berg, J. K.

2012-12-01

Stable calcium carbonate pre-nucleation clusters (PNCs) form in aqueous solution prior to nucleation of CaCO3 (1). Computer simulations suggest that the thermodynamic stability of PNCs is based upon strong hydration in combination with a distinct entropic contribution (2). In this way, PNCs can compete enthalpically with ion pairs and entropically with amorpous calcium carbonate (ACC). The clue is a high degree of structural disorder in highly dynamic, liquid- and chain-like polymeric structures of calcium carbonate ion pairs (2). Nucleation of solid calcium carbonate from these polymeric species proceeds via PNC aggregation rather than via ion-by-ion additions to un-/metastable nuclei (3). Owing to these basic characteristics, the pre-nucleation cluster pathway has been referred to as "non-classical nucleation" (4). Non-classical nucleation leads to distinct short-range structural features in ACC, and depending on pH they relate to the crystalline long-range order of calcite or vaterite (5). This suggests that calcium carbonate exhibits polyamorphism, and that distinct polyamorphs may play a central role during polymorph selection. In this contribution, we outline the scenario described above, and focus on recent insights into the pre-nucleation cluster pathway. 1. D. Gebauer, A. Völkel & H. Cölfen, Science 322, 1819-1822 (2008). 2. R. Demichelis, P. Raiteri, J.D. Gale, D. Quigley, D. Gebauer, Nat. Commun. 2, 590 (2011). 3. M. Kellermeier et al., Adv. Funct. Mater., DOI: 10.1002/adfm.201200953 (2012). 4. D. Gebauer, H. Cölfen, Nano Today 6, 564-584 (2011). 5. D. Gebauer et al., Angew. Chem. Int. Ed. 49, 8889-8891 (2010).

First-principles melting of gallium clusters down to nine atoms: structural and electronic contributions to melting.

PubMed

Steenbergen, Krista G; Gaston, Nicola

2013-10-07

First-principles Born-Oppenheimer molecular dynamics simulations of small gallium clusters, including parallel tempering, probe the distinction between cluster and molecule in the size range of 7-12 atoms. In contrast to the larger sizes, dynamic measures of structural change at finite temperature demonstrate that Ga7 and Ga8 do not melt, suggesting a size limit to melting in gallium exists at 9 atoms. Analysis of electronic structure further supports this size limit, additionally demonstrating that a covalent nature cannot be identified for clusters larger than the gallium dimer. Ga9, Ga10 and Ga11 melt at greater-than-bulk temperatures, with no evident covalent character. As Ga12 represents the first small gallium cluster to melt at a lower-than-bulk temperature, we examine the structural properties of each cluster at finite temperature in order to probe both the origins of greater-than-bulk melting, as well as the significant differences in melting temperatures induced by a single atom addition. Size-sensitive melting temperatures can be explained by both energetic and entropic differences between the solid and liquid phases for each cluster. We show that the lower-than-bulk melting temperature of the 12-atom cluster can be attributed to persistent pair bonding, reminiscent of the pairing observed in α-gallium. This result supports the attribution of greater-than-bulk melting in gallium clusters to the anomalously low melting temperature of the bulk, due to its dimeric structure.

Clustering of health-related behaviors, health outcomes and demographics in Dutch adolescents: a cross-sectional study.

PubMed

Busch, Vincent; Van Stel, Henk F; Schrijvers, Augustinus J P; de Leeuw, Johannes R J

2013-12-04

Recent studies show several health-related behaviors to cluster in adolescents. This has important implications for public health. Interrelated behaviors have been shown to be most effectively targeted by multimodal interventions addressing wider-ranging improvements in lifestyle instead of via separate interventions targeting individual behaviors. However, few previous studies have taken into account a broad, multi-disciplinary range of health-related behaviors and connected these behavioral patterns to health-related outcomes. This paper presents an analysis of the clustering of a broad range of health-related behaviors with relevant demographic factors and several health-related outcomes in adolescents. Self-report questionnaire data were collected from a sample of 2,690 Dutch high school adolescents. Behavioral patterns were deducted via Principal Components Analysis. Subsequently a Two-Step Cluster Analysis was used to identify groups of adolescents with similar behavioral patterns and health-related outcomes. Four distinct behavioral patterns describe the analyzed individual behaviors: 1- risk-prone behavior, 2- bully behavior, 3- problematic screen time use, and 4- sedentary behavior. Subsequent cluster analysis identified four clusters of adolescents. Multi-problem behavior was associated with problematic physical and psychosocial health outcomes, as opposed to those exerting relatively few unhealthy behaviors. These associations were relatively independent of demographics such as ethnicity, gender and socio-economic status. The results show that health-related behaviors tend to cluster, indicating that specific behavioral patterns underlie individual health behaviors. In addition, specific patterns of health-related behaviors were associated with specific health outcomes and demographic factors. In general, unhealthy behavior on account of multiple health-related behaviors was associated with both poor psychosocial and physical health. These findings have

Clustering of health-related behaviors, health outcomes and demographics in Dutch adolescents: a cross-sectional study

PubMed Central

2013-01-01

Background Recent studies show several health-related behaviors to cluster in adolescents. This has important implications for public health. Interrelated behaviors have been shown to be most effectively targeted by multimodal interventions addressing wider-ranging improvements in lifestyle instead of via separate interventions targeting individual behaviors. However, few previous studies have taken into account a broad, multi-disciplinary range of health-related behaviors and connected these behavioral patterns to health-related outcomes. This paper presents an analysis of the clustering of a broad range of health-related behaviors with relevant demographic factors and several health-related outcomes in adolescents. Methods Self-report questionnaire data were collected from a sample of 2,690 Dutch high school adolescents. Behavioral patterns were deducted via Principal Components Analysis. Subsequently a Two-Step Cluster Analysis was used to identify groups of adolescents with similar behavioral patterns and health-related outcomes. Results Four distinct behavioral patterns describe the analyzed individual behaviors: 1- risk-prone behavior, 2- bully behavior, 3- problematic screen time use, and 4- sedentary behavior. Subsequent cluster analysis identified four clusters of adolescents. Multi-problem behavior was associated with problematic physical and psychosocial health outcomes, as opposed to those exerting relatively few unhealthy behaviors. These associations were relatively independent of demographics such as ethnicity, gender and socio-economic status. Conclusions The results show that health-related behaviors tend to cluster, indicating that specific behavioral patterns underlie individual health behaviors. In addition, specific patterns of health-related behaviors were associated with specific health outcomes and demographic factors. In general, unhealthy behavior on account of multiple health-related behaviors was associated with both poor psychosocial

SACS: Spitzer Archival Cluster Survey

NASA Astrophysics Data System (ADS)

Stern, Daniel

Emerging from the cosmic web, galaxy clusters are the most massive gravitationally bound structures in the universe. Thought to have begun their assembly at z > 2, clusters provide insights into the growth of large-scale structure as well as the physics that drives galaxy evolution. Understanding how and when the most massive galaxies assemble their stellar mass, stop forming stars, and acquire their observed morphologies in these environments remain outstanding questions. The redshift range 1.3 < z < 2 is a key epoch in this respect: elliptical galaxies start to become the dominant population in cluster cores, and star formation in spiral galaxies is being quenched. Until recently, however, this redshift range was essentially unreachable with available instrumentation, with clusters at these redshifts exceedingly challenging to identify from either ground-based optical/nearinfrared imaging or from X-ray surveys. Mid-infrared (MIR) imaging with the IRAC camera on board of the Spitzer Space Telescope has changed the landscape. High-redshift clusters are easily identified in the MIR due to a combination of the unique colors of distant galaxies and a negative k-correction in the 3-5 μm range which makes such galaxies bright. Even 90-sec observations with Spitzer/IRAC, a depth which essentially all extragalactic observations in the archive achieve, is sufficient to robustly detect overdensities of L* galaxies out to z~2. Here we request funding to embark on a ambitious scientific program, the “SACS: Spitzer Archival Cluster Survey”, a comprehensive search for the most distant galaxy clusters in all Spitzer/IRAC extragalactic pointings available in the archive. With the SACS we aim to discover ~2000 of 1.3 < z < 2.5 clusters, thus provide the ultimate catalog for high-redshift MIR selected clusters: a lasting legacy for Spitzer. The study we propose will increase by more than a factor of 10 the number of high-redshift clusters discovered by all previous surveys

Evidence for cluster shape effects on the kinetic energy spectrum in thermionic emission.

PubMed

Calvo, F; Lépine, F; Baguenard, B; Pagliarulo, F; Concina, B; Bordas, C; Parneix, P

2007-11-28

Experimental kinetic energy release distributions obtained for the thermionic emission from C(n) (-) clusters, 10< or =n< or =20, exhibit significant non-Boltzmann variations. Using phase space theory, these different features are analyzed and interpreted as the consequence of contrasting shapes in the daughter clusters; linear and nonlinear isomers have clearly distinct signatures. These results provide a novel indirect structural probe for atomic clusters associated with their thermionic emission spectra.

Cluster analysis reveals seasonal variation of sperm subpopulations in extended boar semen

PubMed Central

IBĂNESCU, Iulian; LEIDING, Claus; BOLLWEIN, Heinrich

2017-01-01

This study aimed to identify motile sperm subpopulations in extended boar semen and to observe the presumptive seasonal variation in their distribution. Data from 4837 boar ejaculates collected over a two-year period were analyzed in terms of kinematic parameters by Computer Assisted Sperm Analysis (CASA). Individual sperm data were used to determine subgroups of motile sperm within the ejaculates using cluster analysis. Four motile sperm subpopulations (SP) were identified, with distinct movement patterns: SP1 sperm with high velocity and high linearity; SP2 sperm with high velocity but low linearity; SP3 sperm with low velocity but high linearity; and SP4 sperm with low velocity and low linearity. SP1 constituted the least overall proportion within the ejaculates (P < 0.05). Season of semen collection significantly influenced the different proportions of sperm subpopulations. Spring was characterized by similar proportions of SP1 and SP4 (NS) and higher proportions of SP3. Summer brought a decrease in both subgroups containing fast sperm (SP1 and SP2) (P < 0.05). During autumn, increases in SP2 and SP4 were recorded. Winter substantially affected the proportions of all sperm subpopulations (P < 0.05) and SP2 became the most represented subgroup, while SP1 (fast and linear) reached its highest proportion compared to other seasons. In conclusion, extended boar semen is structured in distinct motile sperm subpopulations whose proportions vary according to the season of collection. Summer and autumn seem to have a negative impact on the fast and linear subpopulation. Cluster analysis can be useful in revealing differences in semen quality that are not normally detected by classical evaluation based on mean values. PMID:29081440

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

Identified Serotonin-Releasing Neurons Induce Behavioral Quiescence and Suppress Mating in Drosophila.

PubMed

Pooryasin, Atefeh; Fiala, André

2015-09-16

Animals show different levels of activity that are reflected in sensory responsiveness and endogenously generated behaviors. Biogenic amines have been determined to be causal factors for these states of arousal. It is well established that, in Drosophila, dopamine and octopamine promote increased arousal. However, little is known about factors that regulate arousal negatively and induce states of quiescence. Moreover, it remains unclear whether global, diffuse modulatory systems comprehensively affecting brain activity determine general states of arousal. Alternatively, individual aminergic neurons might selectively modulate the animals' activity in a distinct behavioral context. Here, we show that artificially activating large populations of serotonin-releasing neurons induces behavioral quiescence and inhibits feeding and mating. We systematically narrowed down a role of serotonin in inhibiting endogenously generated locomotor activity to neurons located in the posterior medial protocerebrum. We identified neurons of this cell cluster that suppress mating, but not feeding behavior. These results suggest that serotonin does not uniformly act as global, negative modulator of general arousal. Rather, distinct serotoninergic neurons can act as inhibitory modulators of specific behaviors. An animal's responsiveness to external stimuli and its various types of endogenously generated, motivated behavior are highly dynamic and change between states of high activity and states of low activity. It remains unclear whether these states are mediated by unitary modulatory systems globally affecting brain activity, or whether distinct neurons modulate specific neuronal circuits underlying particular types of behavior. Using the model organism Drosophila melanogaster, we find that activating large proportions of serotonin-releasing neurons induces behavioral quiescence. Moreover, distinct serotonin-releasing neurons that we genetically isolated and identified negatively affect

Toward An Understanding of Cluster Evolution: A Deep X-Ray Selected Cluster Catalog from ROSAT

NASA Technical Reports Server (NTRS)

Jones, Christine; Oliversen, Ronald (Technical Monitor)

2002-01-01

In the past year, we have focussed on studying individual clusters found in this sample with Chandra, as well as using Chandra to measure the luminosity-temperature relation for a sample of distant clusters identified through the ROSAT study, and finally we are continuing our study of fossil groups. For the luminosity-temperature study, we compared a sample of nearby clusters with a sample of distant clusters and, for the first time, measured a significant change in the relation as a function of redshift (Vikhlinin et al. in final preparation for submission to Cape). We also used our ROSAT analysis to select and propose for Chandra observations of individual clusters. We are now analyzing the Chandra observations of the distant cluster A520, which appears to have undergone a recent merger. Finally, we have completed the analysis of the fossil groups identified in ROM observations. In the past few months, we have derived X-ray fluxes and luminosities as well as X-ray extents for an initial sample of 89 objects. Based on the X-ray extents and the lack of bright galaxies, we have identified 16 fossil groups. We are comparing their X-ray and optical properties with those of optically rich groups. A paper is being readied for submission (Jones, Forman, and Vikhlinin in preparation).

X ray archeology in the Coma cluster

NASA Technical Reports Server (NTRS)

White, Simon D. M.; Briel, Ulrich G.; Henry, J. Patrick

1993-01-01

Images of X-ray emission from hot gas within the Coma cluster of galaxies are presented. These maps, made with the Rosat satellite, have high signal to noise ratio and allow cluster structure to be analyzed in unprecedented detail. They show greater structural irregularity than could be anticipated from earlier observations of Coma. Emission is detected from a number of bright cluster galaxies in addition to the two known previously. In four cases there is evidence that these galaxies lie at the center of an extended subconcentration within the cluster, possibly the remnant of their associated groups. For at least two galaxies the images show direct evidence for ongoing disruption of their gaseous atmosphere. The luminosity associated with these galaxies is comparable to that detected around similar ellipticals in much poorer environments. Emission is easily detected and appears to become more regular at large radii. The data show that this archetype of a rich and regular galaxy cluster was formed by the merging of several distinct subunits which are not yet fully destroyed.

Geographic clusters in underimmunization and vaccine refusal.

PubMed

Lieu, Tracy A; Ray, G Thomas; Klein, Nicola P; Chung, Cindy; Kulldorff, Martin

2015-02-01

Parental refusal and delay of childhood vaccines has increased in recent years and is believed to cluster in some communities. Such clusters could pose public health risks and barriers to achieving immunization quality benchmarks. Our aims were to (1) describe geographic clusters of underimmunization and vaccine refusal, (2) compare clusters of underimmunization with different vaccines, and (3) evaluate whether vaccine refusal clusters may pose barriers to achieving high immunization rates. We analyzed electronic health records among children born between 2000 and 2011 with membership in Kaiser Permanente Northern California. The study population included 154,424 children in 13 counties with continuous membership from birth to 36 months of age. We used spatial scan statistics to identify clusters of underimmunization (having missed 1 or more vaccines by 36 months of age) and vaccine refusal (based on International Classification of Diseases, Ninth Revision, Clinical Modification codes). We identified 5 statistically significant clusters of underimmunization among children who turned 36 months old during 2010-2012. The underimmunization rate within clusters ranged from 18% to 23%, and the rate outside them was 11%. Children in the most statistically significant cluster had 1.58 (P < .001) times the rate of underimmunization as others. Underimmunization with measles, mumps, rubella vaccine and varicella vaccines clustered in similar geographic areas. Vaccine refusal also clustered, with rates of 5.5% to 13.5% within clusters, compared with 2.6% outside them. Underimmunization and vaccine refusal cluster geographically. Spatial scan statistics may be a useful tool to identify locations with challenges to achieving high immunization rates, which deserve focused intervention. Copyright © 2015 by the American Academy of Pediatrics.

The complex star cluster system of NGC 1316 (Fornax A)

NASA Astrophysics Data System (ADS)

Sesto, Leandro A.; Faifer, Favio R.; Forte, Juan C.

2016-10-01

This paper presents Gemini-gri' high-quality photometry for cluster candidates in the field of NGC 1316 (Fornax A) as part of a study that also includes GMOS spectroscopy. A preliminary discussion of the photometric data indicates the presence of four stellar cluster populations with distinctive features in terms of age, chemical abundance and spatial distribution. Two of them seem to be the usually old (metal poor and metal rich) populations typically found in elliptical galaxies. In turn, an intermediate-age (5 Gyr) globular cluster population is the dominant component of the sample (as reported by previous papers). We also find a younger cluster population with a tentative age of ≈ 1 Gyr.

Iron-sulfur cluster biogenesis in mammalian cells: new insights into the molecular mechanisms of cluster delivery

PubMed Central

Maio, Nunziata; Rouault, Tracey. A.

2014-01-01

Iron-sulfur (Fe-S) clusters are ancient, ubiquitous cofactors composed of iron and inorganic sulfur. The combination of the chemical reactivity of iron and sulfur, together with many variations of cluster composition, oxidation states and protein environments, enables Fe-S clusters to participate in numerous biological processes. Fe-S clusters are essential to redox catalysis in nitrogen fixation, mitochondrial respiration and photosynthesis, to regulatory sensing in key metabolic pathways (i. e. cellular iron homeostasis and oxidative stress response), and to the replication and maintenance of the nuclear genome. Fe-S cluster biogenesis is a multistep process that involves a complex sequence of catalyzed protein- protein interactions and coupled conformational changes between the components of several dedicated multimeric complexes. Intensive studies of the assembly process have clarified key points in the biogenesis of Fe-S proteins. However several critical questions still remain, such as: what is the role of frataxin? Why do some defects of Fe-S cluster biogenesis cause mitochondrial iron overload? How are specific Fe-S recipient proteins recognized in the process of Fe-S transfer? This review focuses on the basic steps of Fe-S cluster biogenesis, drawing attention to recent advances achieved on the identification of molecular features that guide selection of specific subsets of nascent Fe-S recipients by the cochaperone HSC20. Additionally, it outlines the distinctive phenotypes of human diseases due to mutations in the components of the basic pathway. PMID:25245479

Grape cluster microclimate influences the aroma composition of Sauvignon blanc wine.

PubMed

Martin, Damian; Grose, Claire; Fedrizzi, Bruno; Stuart, Lily; Albright, Abby; McLachlan, Andrew

2016-11-01

New Zealand Sauvignon blanc (SB) wines are characterised by a distinctive combination of tropical-fruity and green-herbaceous aromatic compounds. The influence of sunlight exposure of grape clusters on juice and wine composition was investigated, with the aim of manipulating aromatic compounds in SB wine. In the absence of basal leaf removal SB clusters naturally exposed to sunlight were riper than shaded clusters, evidenced by higher total soluble solids (TSS) and proline, and lower malic acid, 3-isobutyl-2-methoxypyrazine (IBMP) and arginine. Volatile thiols in wines did not differ between shaded and exposed clusters. At equivalent TSS, cluster exposure had little or no effect on malic acid concentration. Conversely, wine from shaded clusters had almost double the IBMP concentration of wine from exposed clusters at equivalent TSS. The effects on SB juice and wine composition of natural variations in cluster microclimate are not comparable with the effects of cluster exposure created through leaf removal. Copyright © 2016 Elsevier Ltd. All rights reserved.

Free-energy landscape, principal component analysis, and structural clustering to identify representative conformations from molecular dynamics simulations: the myoglobin case.

PubMed

Papaleo, Elena; Mereghetti, Paolo; Fantucci, Piercarlo; Grandori, Rita; De Gioia, Luca

2009-01-01

Several molecular dynamics (MD) simulations were used to sample conformations in the neighborhood of the native structure of holo-myoglobin (holo-Mb), collecting trajectories spanning 0.22 micros at 300 K. Principal component (PCA) and free-energy landscape (FEL) analyses, integrated by cluster analysis, which was performed considering the position and structures of the individual helices of the globin fold, were carried out. The coherence between the different structural clusters and the basins of the FEL, together with the convergence of parameters derived by PCA indicates that an accurate description of the Mb conformational space around the native state was achieved by multiple MD trajectories spanning at least 0.14 micros. The integration of FEL, PCA, and structural clustering was shown to be a very useful approach to gain an overall view of the conformational landscape accessible to a protein and to identify representative protein substates. This method could be also used to investigate the conformational and dynamical properties of Mb apo-, mutant, or delete versions, in which greater conformational variability is expected and, therefore identification of representative substates from the simulations is relevant to disclose structure-function relationship.

Cooling and clusters: when is heating needed?

PubMed

Bryan, Greg; Voit, Mark

2005-03-15

There are (at least) two unsolved problems concerning the current state of the ther- mal gas in clusters of galaxies. The first is to identify the source of the heating which onsets cooling in the centres of clusters with short cooling times (the 'cooling-flow' problem). The second to understand the mechanism which boosts the entropy in cluster and group gas. Since both of these problems involve an unknown source of heating it is tempting to identify them with the same process, particularly since active galactic nuclei heating is observed to be operating at some level in a sample of well-observed 'cooling-flow' clusters. Here we show, using numerical simulations of cluster formation, that much of the gas ending up in clusters cools at high redshift and so the heating is also needed at high redshift, well before the cluster forms. This indicates that the same process operating to solve the cooling-flow problem may not also resolve the cluster-entropy problem.

Cluster-cluster clustering

NASA Technical Reports Server (NTRS)

Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C. S.

1985-01-01

The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales.

Cluster Size Optimization in Sensor Networks with Decentralized Cluster-Based Protocols

PubMed Central

Amini, Navid; Vahdatpour, Alireza; Xu, Wenyao; Gerla, Mario; Sarrafzadeh, Majid

2011-01-01

Network lifetime and energy-efficiency are viewed as the dominating considerations in designing cluster-based communication protocols for wireless sensor networks. This paper analytically provides the optimal cluster size that minimizes the total energy expenditure in such networks, where all sensors communicate data through their elected cluster heads to the base station in a decentralized fashion. LEACH, LEACH-Coverage, and DBS comprise three cluster-based protocols investigated in this paper that do not require any centralized support from a certain node. The analytical outcomes are given in the form of closed-form expressions for various widely-used network configurations. Extensive simulations on different networks are used to confirm the expectations based on the analytical results. To obtain a thorough understanding of the results, cluster number variability problem is identified and inspected from the energy consumption point of view. PMID:22267882

Cluster Size Statistic and Cluster Mass Statistic: Two Novel Methods for Identifying Changes in Functional Connectivity Between Groups or Conditions

PubMed Central

Ing, Alex; Schwarzbauer, Christian

2014-01-01

Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods – the cluster size statistic (CSS) and cluster mass statistic (CMS) – are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity. PMID:24906136

Cluster size statistic and cluster mass statistic: two novel methods for identifying changes in functional connectivity between groups or conditions.

PubMed

Ing, Alex; Schwarzbauer, Christian

2014-01-01

Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods--the cluster size statistic (CSS) and cluster mass statistic (CMS)--are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity.

There are four dynamically and functionally distinct populations of E-cadherin in cell junctions

PubMed Central

Erami, Zahra; Timpson, Paul; Yao, Wu; Zaidel-Bar, Ronen; Anderson, Kurt I.

2015-01-01

ABSTRACT E-cadherin is a trans-membrane tumor suppressor responsible for epithelial cell adhesion. E-cadherin forms adhesive clusters through combined extra-cellular cis- and trans-interactions and intracellular interaction with the actin cytoskeleton. Here we identify four populations of E-cadherin within cell junctions based on the molecular interactions which determine their mobility and adhesive properties. Adhesive and non-adhesive populations of E-cadherin each consist of mobile and immobile fractions. Up to half of the E-cadherin immobilized in cell junctions is non-adhesive. Incorporation of E-cadherin into functional adhesions require all three adhesive interactions, with deletion of any one resulting in loss of effective cell-cell adhesion. Interestingly, the only interaction which could independently slow the diffusion of E-cadherin was the tail-mediated intra-cellular interaction. The adhesive and non-adhesive mobile fractions of E-cadherin can be distinguished by their sensitivity to chemical cross-linking with adhesive clusters. Our data define the size, mobility, and adhesive properties of four distinct populations of E-cadherin within cell junctions, and support association with the actin cytoskeleton as the first step in adhesion formation. PMID:26471767

Semi-supervised clustering methods

PubMed Central

Bair, Eric

2013-01-01

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as “semi-supervised clustering” methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided. PMID:24729830

Subphenotypes of mild-to-moderate COPD by factor and cluster analysis of pulmonary function, CT imaging and breathomics in a population-based survey.

PubMed

Fens, Niki; van Rossum, Annelot G J; Zanen, Pieter; van Ginneken, Bram; van Klaveren, Rob J; Zwinderman, Aeilko H; Sterk, Peter J

2013-06-01

Classification of COPD is currently based on the presence and severity of airways obstruction. However, this may not fully reflect the phenotypic heterogeneity of COPD in the (ex-) smoking community. We hypothesized that factor analysis followed by cluster analysis of functional, clinical, radiological and exhaled breath metabolomic features identifies subphenotypes of COPD in a community-based population of heavy (ex-) smokers. Adults between 50-75 years with a smoking history of at least 15 pack-years derived from a random population-based survey as part of the NELSON study underwent detailed assessment of pulmonary function, chest CT scanning, questionnaires and exhaled breath molecular profiling using an electronic nose. Factor and cluster analyses were performed on the subgroup of subjects fulfilling the GOLD criteria for COPD (post-BD FEV1/FVC < 0.70). Three hundred subjects were recruited, of which 157 fulfilled the criteria for COPD and were included in the factor and cluster analysis. Four clusters were identified: cluster 1 (n = 35; 22%): mild COPD, limited symptoms and good quality of life. Cluster 2 (n = 48; 31%): low lung function, combined emphysema and chronic bronchitis and a distinct breath molecular profile. Cluster 3 (n = 60; 38%): emphysema predominant COPD with preserved lung function. Cluster 4 (n = 14; 9%): highly symptomatic COPD with mildly impaired lung function. In a leave-one-out validation analysis an accuracy of 97.4% was reached. This unbiased taxonomy for mild to moderate COPD reinforces clusters found in previous studies and thereby allows better phenotyping of COPD in the general (ex-) smoking population.

The Evolution of Globular Cluster Systems In Early-Type Galaxies

NASA Astrophysics Data System (ADS)

Grillmair, Carl

1999-07-01

We will measure structural parameters {core radii and concentrations} of globular clusters in three early-type galaxies using deep, four-point dithered observations. We have chosen globular cluster systems which have young, medium-age and old cluster populations, as indicated by cluster colors and luminosities. Our primary goal is to test the hypothesis that globular cluster luminosity functions evolve towards a ``universal'' form. Previous observations have shown that young cluster systems have exponential luminosity functions rather than the characteristic log-normal luminosity function of old cluster systems. We will test to see whether such young system exhibits a wider range of structural parameters than an old systems, and whether and at what rate plausible disruption mechanisms will cause the luminosity function to evolve towards a log-normal form. A simple observational comparison of structural parameters between different age cluster populations and between diff er ent sub-populations within the same galaxy will also provide clues concerning both the formation and destruction mechanisms of star clusters, the distinction between open and globular clusters, and the advisability of using globular cluster luminosity functions as distance indicators.

Finding gene clusters for a replicated time course study

PubMed Central

2014-01-01

Background Finding genes that share similar expression patterns across samples is an important question that is frequently asked in high-throughput microarray studies. Traditional clustering algorithms such as K-means clustering and hierarchical clustering base gene clustering directly on the observed measurements and do not take into account the specific experimental design under which the microarray data were collected. A new model-based clustering method, the clustering of regression models method, takes into account the specific design of the microarray study and bases the clustering on how genes are related to sample covariates. It can find useful gene clusters for studies from complicated study designs such as replicated time course studies. Findings In this paper, we applied the clustering of regression models method to data from a time course study of yeast on two genotypes, wild type and YOX1 mutant, each with two technical replicates, and compared the clustering results with K-means clustering. We identified gene clusters that have similar expression patterns in wild type yeast, two of which were missed by K-means clustering. We further identified gene clusters whose expression patterns were changed in YOX1 mutant yeast compared to wild type yeast. Conclusions The clustering of regression models method can be a valuable tool for identifying genes that are coordinately transcribed by a common mechanism. PMID:24460656

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.

Modifiable lifestyle behavior patterns, sedentary time and physical activity contexts: a cluster analysis among middle school boys and girls in the SALTA study.

PubMed

Marques, Elisa A; Pizarro, Andreia N; Figueiredo, Pedro; Mota, Jorge; Santos, Maria P

2013-06-01

To analyze how modifiable health-related variables are clustered and associated with children's participation in play, active travel and structured exercise and sport among boys and girls. Data were collected from 9 middle-schools in Porto (Portugal) area. A total of 636 children in the 6th grade (340 girls and 296 boys) with a mean age of 11.64 years old participated in the study. Cluster analyses were used to identify patterns of lifestyle and healthy/unhealthy behaviors. Multinomial logistic regression analysis was used to estimate associations between cluster allocation, sedentary time and participation in three different physical activity (PA) contexts: play, active travel, and structured exercise/sport. Four distinct clusters were identified based on four lifestyle risk factors. The most disadvantaged cluster was characterized by high body mass index, low high-density lipoprotein cholesterol and cardiorespiratory fitness and a moderate level of moderate to vigorous PA. Everyday outdoor play (OR=1.85, 95%CI 0.318-0.915) and structured exercise/sport (OR=1.85, 95%CI 0.291-0.990) were associated with healthier lifestyle patterns. There were no significant associations between health patterns and sedentary time or travel mode. Outdoor play and sport/exercise participation seem more important than active travel from school in influencing children's healthy cluster profiles. Copyright © 2013 Elsevier Inc. All rights reserved.

Spatial cluster for clustering the influence factor of birth and death child in Bogor Regency, West Java

NASA Astrophysics Data System (ADS)

Bekti, Rokhana Dwi; Rachmawati, Ro'fah

2014-03-01

The number of birth and death child is the benchmarks to determine and monitor the health and welfare in Indonesia. It can be used to identify groups of people who have a high mortality risk. Identifying group is important to compare the characteristics of human that have high and low risk. These characteristics can be seen from the factors that influenced it. Furthermore, there are factors which influence of birth and death child, such us economic, health facility, education, and others. The influence factors of every individual are different, but there are similarities some individuals which live close together or in the close locations. It means there was spatial effect. To identify group in this research, clustering is done by spatial cluster method, which is view to considering the influence of the location or the relationship between locations. One of spatial cluster method is Spatial 'K'luster Analysis by Tree Edge Removal (SKATER). The research was conducted in Bogor Regency, West Java. The goal was to get a cluster of districts based on the factors that influence birth and death child. SKATER build four number of cluster respectively consists of 26, 7, 2, and 5 districts. SKATER has good performance for clustering which include spatial effect. If it compare by other cluster method, Kmeans has good performance by MANOVA test.